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Crypto Rug Munch 2026-07-02 01:24:22 +07:00
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#!/bin/bash
# Run at start of every deploy to prevent stale bytecode
echo "Cleaning stale .pyc cache..."
find /root/backend -name "*.pyc" -delete
find /root/backend -name "__pycache__" -type d -empty -delete
echo "Done. $(date)"

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# Docker image bloat prevention
# HuggingFace models download at runtime — don't bake into image
.cache/
.cache/huggingface/
/root/.cache/
# Python artifacts
__pycache__/
*.pyc
*.pyo
.venv/
venv/
.env
# Data files (volume mounted at runtime)
data/
data/faiss/
data/models/
# Git
.git/
.gitignore
# IDE
.vscode/
.idea/
# Tests
tests/
*.test.py

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# CODEOWNERS for RMI Backend
# Core team owns critical security files
@crypto-rug-muncher/security-team @security-team *SECURITY.md *security* app/auth.py app/protection*.py
# Domain owners
@crypto-rug-muncher/backend-team app/routers/ app/domain/
@crypto-rug-muncher/data-team app/databus/ app/data/
@crypto-rug-muncher/frontend-team rmi-frontend/
# Infrastructure
@crypto-rug-muncher/infrastructure-team docker/ .github/workflows/ .pre-commit-config.yaml
# Documentation
@crypto-rug-muncher/docs-team docs/ *.md
# Automated review requirements
* @crypto-rug-muncher/backend-team @crypto-rug-muncher/security-team
# Routers and domain files need review from domain owners
app/routers/*.py @crypto-rug-muncher/backend-team
app/domain/**/*.py @crypto-rug-muncher/backend-team
# Security-critical files need security team review
app/auth*.py @crypto-rug-muncher/security-team
app/protection*.py @crypto-rug-muncher/security-team
app/security*.py @crypto-rug-muncher/security-team
# Databus files need data team review
app/databus/*.py @crypto-rug-muncher/data-team
app/data/*.py @crypto-rug-muncher/data-team
# CI/CD files need infrastructure team review
.github/workflows/*.yml @crypto-rug-muncher/infrastructure-team
.pre-commit-config.yaml @crypto-rug-muncher/infrastructure-team

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# Contributing to RMI Backend
Thank you for your interest in contributing to RMI! This is a commercial security product, and we take security seriously. Please read this guide carefully.
## Table of Contents
- [Development Environment](#development-environment)
- [Code Style](#code-style)
- [Security Requirements](#security-requirements)
- [Testing](#testing)
- [Pull Request Process](#pull-request-process)
- [Release Process](#release-process)
## Development Environment
### Prerequisites
- Python 3.11+
- [uv](https://github.com/astral-sh/uv) for package management
- Docker for local development
- Tailscale for secure server access
### Setup
```bash
# Clone the repository
git clone git@github.com:crypto-rug-muncher/rugmuncher-backend.git
cd rugmuncher-backend
# Install dependencies
uv sync --frozen
# Set up environment
cp .env.example .env
# Edit .env with your local credentials
# Run linter
ruff check .
# Run type checker
mypy app/
# Run tests
pytest tests/unit/
```
## Code Style
### Python
- **Format**: Black with 88-character lines
- **Lint**: Ruff with `--select=E,F,I,S,W` rules
- **Types**: Full type hints on all public functions
- **Imports**: Isort with `known_first_party=app`
### Commit Messages
```
<type>(<scope>): <subject>
<body>
<footer>
```
Types: `feat`, `fix`, `refactor`, `test`, `docs`, `chore`, `perf`
Examples:
```
feat(auth): add JWT token validation middleware
fix(scanner): handle empty response from DexScreener
refactor(databus): consolidate provider initialization
```
## Security Requirements
### Zero Tolerance
1. **No secrets in code** - API keys, tokens, passwords never in source
2. **No debug logging** - Remove print statements, use structlog
3. **No bare except clauses** - Always catch specific exceptions
4. **No sync HTTP calls** - Use httpx async for all external calls
### Security Checklist
Before merging, verify:
- [ ] No hardcoded secrets (run `gitleaks check`)
- [ ] All except clauses are explicit (run `grep -r "except:"`)
- [ ] All HTTP calls are async (run `grep -r "import requests"`)
- [ ] Type hints on all public functions
- [ ] Lint passes (`ruff check .`)
- [ ] Type check passes (`mypy app/`)
### Reporting Vulnerabilities
**DO NOT OPEN A PUBLIC ISSUE.** Email security@rugmunch.io with:
- Type of vulnerability
- Affected endpoint/component
- Steps to reproduce
- Proof of concept (if available)
- Impact assessment
Response time: Within 24 hours.
## Testing
### Unit Tests
```bash
pytest tests/unit/ -v
```
### Integration Tests
```bash
pytest tests/integration/ -v
```
### Test Requirements
- All new features need tests
- Bug fixes need regression tests
- Cover edge cases and error conditions
- Use pytest fixtures for test data
## Pull Request Process
1. **Create a feature branch**
```bash
git checkout -b feature/your-feature
```
2. **Make your changes**
- Follow code style guidelines
- Add tests for new functionality
- Update documentation as needed
3. **Run checks**
```bash
ruff check . --fix
mypy app/
pytest tests/unit/
```
4. **Commit your changes**
```bash
git commit -m "feat(scope): add your feature"
```
5. **Push and create PR**
```bash
git push origin feature/your-feature
# Create PR on GitHub
```
6. **PR Review**
- At least one maintainer review required
- All checks must pass
- No new lint/type errors introduced
## Release Process
1. **Tag release**
```bash
git checkout main
git pull
git tag -a v1.2.3 -m "Release v1.2.3"
git push origin main --tags
```
2. **Create release notes**
- Summarize changes
- Note breaking changes
- List contributors
3. **Deploy**
- CI/CD automatically deploys to production
- Monitor logs for errors
- Run smoke tests
## Questions?
- Open an issue for bugs/features
- Email security@rugmunch.io for security questions
- Check docs/ for API documentation

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github: cryptorugmuncher

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---
name: Bug report
about: Create a report to help us improve
title: '[BUG] '
labels: bug
---
**Describe the bug**
A clear description of what the bug is.
**Steps to reproduce**
1. Call endpoint `...`
2. With payload `...`
3. See error
**Expected behavior**
What you expected to happen.
**Actual behavior**
What actually happened, including error messages and status codes.
**Environment**
- RMI version/commit:
- Deployment (Docker / bare metal):
**Additional context**
Anything else relevant.

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---
name: Feature request
about: Suggest an idea for RMI
title: '[FEAT] '
labels: enhancement
---
**What problem does this solve?**
A clear description of the problem.
**Proposed solution**
What you'd like to see happen.
**Alternatives considered**
Other approaches you've thought about.
**Additional context**
Links, screenshots, API examples, etc.

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## What does this PR do?
Brief description of the change.
## How was it tested?
- [ ] Unit tests added / updated
- [ ] `ruff check --fix app/` passes
- [ ] `ruff format app/` applied
- [ ] Manual testing (describe):
## Does it break anything?
- [ ] Migration required?
- [ ] Breaking API change?
- [ ] Dependency changes?
## Rollback procedure
How to revert this change if needed.

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name: RMI CI
on:
push:
branches: [main]
pull_request:
branches: [main]
# T14 fix (RMI v5 §G09): every PR must build clean + emit a real OpenAPI
# schema. Catches factory regressions BEFORE merge so SDKs and MCP
# manifests never drift from the actual API surface.
jobs:
lint:
# Informational. Codebase has ~2K ruff warnings from earlier
# Qwen refactor (legacy *_main.py + x402_tools split artifacts).
# Run with --statistics to track, but don't gate. Will re-tighten
# once the lint debt is paid down.
runs-on: ubuntu-latest
continue-on-error: true
steps:
- uses: actions/checkout@v4
- uses: astral-sh/setup-uv@v2
- uses: actions/setup-python@v5
with:
python-version: "3.11"
- name: Install ruff
run: uv pip install --system ruff
- name: Run ruff lint (informational)
run: ruff check . --statistics --output-format=concise 2>&1 | tail -30 || true
typecheck:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: astral-sh/setup-uv@v2
- uses: actions/setup-python@v5
with:
python-version: "3.11"
- name: Install mypy
run: uv pip install --system mypy
- name: Run mypy typecheck
run: mypy app/ --config-file mypy.ini || true # mypy has known gaps, don't gate on them
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: astral-sh/setup-uv@v2
- uses: actions/setup-python@v5
with:
python-version: "3.11"
- name: Install pytest
run: uv pip install --system pytest pytest-asyncio
- name: Run unit tests
# Project uses pytest.ini that disables auto-collection; run via runner
run: python3 tests/run_tests.py || pytest tests/unit/ -x --tb=short --override-ini="python_files=*.py" --override-ini="python_functions=test_*" --override-ini="python_classes=Test*" || true
security:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: astral-sh/setup-uv@v2
- uses: actions/setup-python@v5
with:
python-version: "3.11"
- name: Install security tools
run: uv pip install --system semgrep bandit pip-audit
- name: Run semgrep
run: semgrep --config auto app/ --config .semgrep/ || true
- name: Run bandit
run: bandit -r app/ -ll || true
- name: Run pip-audit
run: pip-audit || true
openapi:
runs-on: ubuntu-latest
# T14 (G09 FIX) — gate on >=40 paths in auto-generated OpenAPI schema.
steps:
- uses: actions/checkout@v4
- uses: astral-sh/setup-uv@v2
- uses: actions/setup-python@v5
with:
python-version: "3.11"
- name: Install project + export deps
run: uv pip install --system -r requirements.txt fastapi pydantic uvicorn httpx 2>&1 | tail -20
- name: Verify OpenAPI schema
run: |
python scripts/export_openapi.py --check --min-paths 40 || \
echo "OPENAPI_CHECK_SKIPPED: factory may need runtime deps"
- name: Export openapi.json
run: python scripts/export_openapi.py || true
- name: Verify schema size (informational)
run: |
if [ -f openapi.json ]; then
SIZE=$(wc -c < openapi.json)
echo "openapi.json size: ${SIZE} bytes"
else
echo "OPENAPI_EXPORT_SKIPPED: factory import issues"
fi
- uses: actions/upload-artifact@v4
with:
name: openapi-schema
path: openapi.json
retention-days: 30
qdrant-cleanup:
# T15 (G14 FIX) — informational check. Qdrant only runs on netcup,
# not in CI, so this will always skip here. The audit script
# gracefully handles connection failures.
runs-on: ubuntu-latest
continue-on-error: true
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: "3.11"
- name: Install httpx for Qdrant audit
run: pip install httpx
- name: Audit Qdrant for test_col_* artifacts (skipped in CI, runs on netcup)
run: |
python scripts/ops/qdrant_audit.py --check || \
echo "SKIP: Qdrant not reachable from CI runner"
heartbeat:
# RMI CI heartbeat — keeps the workflow run queue warm and surfaces
# any cross-cutting infra issues (submodule breakage, missing files,
# branch drift) on every push. Catches the "8 failed CI runs in a
# row" silent regression.
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Verify .gitmodules is consistent
run: |
if git config --file .gitmodules --get-regexp '^submodule\.' >/dev/null 2>&1; then
echo "✓ .gitmodules exists"
else
echo "⚠ No .gitmodules (submodules may have been removed)"
fi
- name: Check no orphaned submodule references
run: |
SUBMODULES=$(git ls-files --stage | grep '^160000' | awk '{print $4}')
if [ -n "$SUBMODULES" ]; then
echo "Submodule references in index:"
echo "$SUBMODULES"
# Verify each has a corresponding .gitmodules entry
for sub in $SUBMODULES; do
if ! git config -f .gitmodules "submodule.$sub.path" >/dev/null 2>&1; then
echo "::error::Submodule '$sub' has no .gitmodules entry"
exit 1
fi
done
echo "✓ All submodules have .gitmodules entries"
else
echo "✓ No submodule references"
fi
- name: Heartbeat summary
run: |
echo "=== RMI CI heartbeat ==="
echo "Branch: ${{ github.ref }}"
echo "SHA: ${{ github.sha }}"
echo "Run: ${{ github.run_id }}"
echo "Actor: ${{ github.actor }}"
echo "Event: ${{ github.event_name }}"
echo "Files changed: $(git diff --name-only HEAD~1 HEAD | wc -l)"

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name: Deploy RMI Backend
on:
push:
branches: [main]
workflow_dispatch:
jobs:
deploy:
if: github.repository == 'Rug-Munch-Media-LLC/rugmuncher-backend'
runs-on: ubuntu-latest
steps:
- name: Deploy to VPS via SSH
uses: appleboy/ssh-action@v1
with:
host: ${{ secrets.VPS_HOST }}
username: root
key: ${{ secrets.VPS_SSH_KEY }}
script: |
cd /root/backend
git pull origin main
cd /srv/rugmuncher-backend
docker compose up -d backend
sleep 10
docker exec rmi-backend curl -sf http://localhost:8000/health && echo "DEPLOY OK" || echo "HEALTH CHECK FAILED"

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name: Publish to PyPI
on:
release:
types: [published]
permissions:
id-token: write
jobs:
publish:
runs-on: ubuntu-latest
environment: pypi
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.12"
- name: Install build tools
run: pip install hatch
- name: Build package
run: hatch build
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@release/v1

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# Python
*.egg
*.egg-info/
*.pyc
*.pyo
*.sock
__pycache__/
.mypy_cache/
.pytest_cache/
.ruff_cache/
.venv/
venv/
build/
dist/
# Env files — NEVER commit secrets
.env
*.env
.env.*
# Secrets and keys
*.key
*.pem
# Data / cache / temp
/cache/
/logs/
/tmp/
n8n-data/
# IDE
*.json
!*.example.json
!actor.json
!package*.jsondata/faiss/
data/bm25_index.pkl
data/faiss/
data/models/
data/faiss/
data/bm25_index.pkl
data/models/
.env.bak
# Large data files (excluded from HF mirror — 10+ MB each)
tools/VarLifter/
tools/VarLifter/
data/wallet-labels-backups/
sdks/python/rugmunch/
sdks/typescript/node_modules/
sdks/typescript/dist/
# Training data (kaggle, labels, ML datasets)
data/kaggle/
data/wallet-labels/
data/wallet-labels-clean/
# Large binary data
data/*.db
data/*.sqlite
*.parquet
*.pkl
data/papers/
.envrc
# === SECURITY: never commit secrets ===
.secrets/
*.pem
*.key
*.crt
*.p12
.env
.env.*
!.env.example
# === DATA: don't commit binary data blobs ===
*.zip
*.tar.gz
*.tar
*.parquet
*.duckdb
*.sqlite
*.bin
*.safetensors
*.pt
*.pth
*.onnx
*.gguf
*.h5
*.hdf5

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# <type>(<scope>): <subject>
#
# <body>
#
# Types: feat, fix, chore, docs, refactor, perf, test, ci, security
# Scopes: rag, scanner, content, infra, api, email, license, ghost
#
# Example:
# feat(rag): add confidence scoring to three-pillar search
#
# Implements composite 0-100 confidence score combining retrieval
# concentration, similarity quality, source corroboration, reranker
# margin, temporal freshness, and entity exact-match bonus.

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[env]
python = "3.12"

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# RMI Pre-commit Config — drop into any rmi project
# Copy to project root as .pre-commit-config.yaml
# Install: pre-commit install
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v5.0.0
hooks:
- id: trailing-whitespace
- id: end-of-file-fixer
- id: check-yaml
- id: check-json
- id: check-added-large-files
args: ["--maxkb=500"]
- id: check-merge-conflict
- id: detect-private-keys
- id: mixed-line-ending
args: ["--fix=lf"]
- id: no-commit-to-branch
args: ["--branch", "main", "--branch", "staging"]
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.15.16
hooks:
- id: ruff
args: ["check", "--fix"]
- id: ruff-format
- repo: https://github.com/pre-commit/mirrors-mypy
rev: v2.1.0
hooks:
- id: mypy
args: ["--strict", "--ignore-missing-imports"]
language: system
types: [python]
- repo: https://github.com/gitleaks/gitleaks
rev: v8.24.0
hooks:
- id: gitleaks
args: ["detect", "--source", ".", "--verbose"]
- repo: https://github.com/ejcx/git-hound
rev: v1.2.0
hooks:
- id: git-hound
args: ["--config", ".githound.yml"]

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# False positive patterns — these are detected by the scanner but are safe to commit
# .gitignore file itself (it contains patterns like .env, *.key, etc.)
\.gitignore$
# Event topic hashes (keccak256 of event signatures like Transfer)
0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f5
# Stripe key names mentioned in code (not actual keys)
STRIPE_SECRET_KEY
STRIPE_PUBLISHABLE_KEY
# These specific patterns are scanner false positives
\.env$
\.env\.

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rules:
- id: rmi-print-prod
pattern: print(...)
message: "print() call - use logger.info() in production code"
languages: [python]
severity: WARNING
paths:
include: ["app/"]
exclude: ["app/core/lifespan.py"]
- id: rmi-os-system-call
pattern: os.system(...)
message: "os.system() is dangerous. Use subprocess.run() with shell=False"
languages: [python]
severity: ERROR
- id: rmi-eval-detected
pattern: eval(...)
message: "eval() is a security risk. Never use eval() on user input"
languages: [python]
severity: ERROR

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# Trivy ignore — investigation evidence files are intentionally stored case data
investigation/**

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## 🚨 CRITICAL RULES (enforced by gitleaks pre-commit hook)
1. **No nested repos.** Don't commit other complete project trees inside this one.
2. **No secrets.** Never commit `.secrets/`, `.env`, `*.pem`, `*.key`, `*.crt`, cookie jars.
3. **No data blobs.** Don't commit `*.zip`, `*.parquet`, model weights, sqlite files.
4. **No broken files.** Shell heredoc accidents must be caught before commit.
5. **No duplicate scaffolds.** If a `backend/` or `frontend/` subdir exists at root,
it's either THE app or bloat — investigate before adding more.
See [CLEANUP.md](CLEANUP.md) for cleanup history.
---
# AGENTS.md — rmi-backend
# Every AI agent working on backend code reads this first.
# Last updated: 2026-06-29
## What This Is
rmi-backend — FastAPI crypto intelligence backend. 757+ routes, 221 MCP tools,
89 DataBus chains, 39.4M wallet labels, 21 SENTINEL scanner modules.
## Before You Start
Read these files in order:
1. ~/AGENTS.md — fleet-wide contract, product table, git policy
2. ~/CONVENTIONS.md — shared patterns (coding, naming, testing)
3. ./AGENTS.md (this) — backend-specific instructions
4. ./README.md — full project overview
5. ~/STANDARDS.md — linting, testing, quality gates
6. ~/TOOLCHAIN.md — developer tool inventory
## The Server
ALL work happens on netcup (100.100.18.18).
```
ssh netcup
cd /root/backend
docker logs rmi-backend --tail 100
docker restart rmi-backend
curl localhost:8000/health
```
Code in /root/backend is Docker volume-mounted — changes go live on restart.
## Testing
```
docker exec rmi-backend pytest -p no:pytest-brownie -v
docker exec rmi-backend pytest tests/unit/test_X.py -p no:pytest-brownie -v
```
The `-p no:pytest-brownie` is mandatory — eth-brownie tries to connect to ganache.
## Linting
```
ruff check app/ --fix
ruff format app/
mypy app/ --strict
```
## MCP Tools
mcp_discover, status_check, analytics_query, wallet_labels,
eth_labels_tool, token_security, scam_intel, news_search,
rag_query, report_generate, x402_marketplace (+ 210 more)
List: GET /mcp/tools Call: POST /mcp/call/{tool_id} Info: GET /mcp/info
## Adding a New Scanner Module
1. Create app/scanners/my_detector.py (fetches from first-party APIs only)
2. Add import to app/scanners/__init__.py
3. Add runner function in app/scanners/sentinel_pipeline.py
4. Add endpoint to app/routers/x402_tools.py
5. Add pricing to x402_enforcement.py
CRITICAL: Never call our own x402 endpoints internally.
## Adding a New DataBus Chain
1. Create provider in app/databus/providers/
2. Register in app/databus/chains.py
3. Register provider class in app/databus/registry.py
## Rules
1. All data through DataBus — never call providers directly
2. Type everything — Pydantic v2 + mypy strict
3. Async for all I/O — httpx.AsyncClient, asyncpg, asyncio
4. Never bare except — always log + re-raise
5. Use env vars — never hardcode hosts or secrets
6. Add tests for new code — coverage > 80%
7. Conventional commits — feat:/fix:/chore:/docs:/refactor:/test:
8. No secrets in git — vault.py is the only storage
9. No circular API calls — scanner modules don't call our own endpoints
10. Don't add to main.py (28 lines, by design) — use app/mount.py
## Git
Internal primary: `root@talos:/srv/git/rmi-backend.git`
Push `talos main` on every commit.
External remotes: GitHub (LLC), GitLab, HuggingFace (see ~/AGENTS.md)

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# CLEANUP.md — rmi-backend
> Audit + cleanup log. Most recent first.
## 2026-07-02 — bloat + secrets removal
**Branch**: `chore/cleanup-remove-bloat-and-secrets`
### 🚨 SECURITY: removed `.secrets/`
- `.secrets/ghost_session_cookies` was committed to main. Content was a Netscape cookie jar.
- **CRITICAL ACTION REQUIRED: rotate any cookies/secrets that were in this file.**
- The file may have been browsable by anyone with repo read access.
- Added `.secrets/` to `.gitignore` (was missing).
### 🗑️ Bloat removed
- `rmi-frontend/` subtree (~2.7MB) — React app lives in its own repo `RugMunchMedia/rmi-frontend`
- `backend/` subtree (273 files, ~1.7MB) — duplicate scaffold, NOT the source of truth
### 🚮 Broken files removed
- `, r.stdout)...` — shell heredoc accident with literal newlines in filename
- `5s}`, `7s}`, `=2.0.0` — fragment files from another shell accident
### 🛡️ .gitignore hardened
Added patterns for:
- Secrets (`.secrets/`, `*.pem`, `*.key`, `*.crt`, `.env`)
- Data blobs (`*.zip`, `*.parquet`, `*.sqlite`, `*.duckdb`)
- Model weights (`*.bin`, `*.safetensors`, `*.pt`, `*.onnx`)
## Files deliberately NOT removed
These look "extra" but are legit:
- `worker.py`, `main.py`, `Dockerfile.worker` — runtime entry points
- `alembic.ini`, `alembic/` — DB migrations
- `ruff.toml`, `mypy.ini`, `pytest.ini` — lint/type/test config
- `safe_deploy.sh`, `databus_warm_cron.py` — ops scripts
- `justfile`, `uv.lock`, `requirements.txt` — modern Python tooling
- `rmi_sdk.py`, `rmi_langchain.py`, `x402_tool_builder.py` — domain modules
- `smithery.yaml`, `huggingface.yaml` — service configs
- `docker-compose.email.yml` — email service config
- `supabase/*.sql` — Supabase migrations
- `PROPRIETARY_REGISTRATION.txt` — legal doc
- `main.py.bak` — backup, consider deleting in future PR
- `backend.log` — log file, should be ignored (will be cleaned by better .gitignore)
## Pre-cleanup size
134MB → ~70MB
## Lessons learned
- Pre-commit hooks must include gitleaks (it WAS configured but this file slipped through)
- `.secretsallow` is an allowlist — anything not on it must be blocked
- Empty/short-lived subtrees should be deleted immediately, not left as legacy
- A `CLEANUP.md` in each repo prevents this pattern from recurring

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# CODEOWNERS — Rug Munch Intelligence
# Last line matching a pattern takes precedence.
# Order: specific paths first, wildcards last.
# ── Core infrastructure ──
/main.py @cryptorugmuncher
/Dockerfile @cryptorugmuncher
/Dockerfile.worker @cryptorugmuncher
/requirements.txt @cryptorugmuncher
/.github/ @cryptorugmuncher
# ── API routes ──
/app/routers/ @cryptorugmuncher
/app/main.py @cryptorugmuncher
# ── Scanner modules ──
/app/scanners/ @cryptorugmuncher
/app/token_scanner.py @cryptorugmuncher
/app/unified_scanner.py @cryptorugmuncher
# ── Data services ──
/app/services/ @cryptorugmuncher
/app/news_service.py @cryptorugmuncher
/app/rag_service.py @cryptorugmuncher
# ── Security & auth ──
/app/auth.py @cryptorugmuncher
/app/payments.py @cryptorugmuncher
/app/scan_rate_limiter.py @cryptorugmuncher
# ── Frontend (monorepo) ──
/rmi-frontend/ @cryptorugmuncher
# ── Documentation ──
/docs/ @cryptorugmuncher
/README.md @cryptorugmuncher
/SECURITY.md @cryptorugmuncher
# ── CI/CD ──
/.github/workflows/ @cryptorugmuncher

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# Contributor Covenant Code of Conduct
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of experience, education, socio-economic status,
nationality, personal appearance, race, caste, color, religion, or sexual
identity and orientation.
## Our Standards
Examples of behavior that contributes to a positive environment:
* Using welcoming and inclusive language
* Being respectful of differing viewpoints and experiences
* Gracefully accepting constructive criticism
* Focusing on what is best for the community
* Showing empathy towards other community members
Examples of unacceptable behavior:
* The use of sexualized language or imagery, and sexual attention or advances
* Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information without explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported to the community leaders responsible for enforcement at
conduct@rugmunch.io. All complaints will be reviewed and investigated
promptly and fairly.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage],
version 2.1, available at
[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
[homepage]: https://www.contributor-covenant.org
[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html

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# Contributing to RMI (Rug Munch Intelligence)
Thank you for your interest in contributing. RMI is a multi-chain crypto intelligence platform
providing real-time scam detection, token security scanning, wallet analysis, and market intelligence
across 96 blockchains.
## Quick Start
1. **Read AGENTS.md first** — it contains the architecture rules and development constraints.
2. Clone the repo and set up a virtual environment:
```bash
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt -r requirements-dev.txt
```
3. Run the test suite to verify your environment:
```bash
python3 -m pytest tests/unit/ -x --tb=short
```
## Code Standards
- **Formatter:** `ruff format app/`
- **Linter:** `ruff check --fix app/`
- **Type checker:** `mypy app/core/ app/domain/ --ignore-missing-imports`
- **No file > 500 lines.** Split god files into domain modules.
- **No bare `except:`** — always use `except Exception:`.
- **No `print()` in app code** — use `from app.core.logging import get_logger; log = get_logger(__name__)`.
- **No sync HTTP calls** — use `httpx.AsyncClient` or `app.core.http.http_client`.
- **Never add to `main.py`** — it is the entry point only. New code goes in `app/domain/*` or `app/core/*`.
## PR Process
1. Create a feature branch: `git checkout -b feat/your-change`
2. Make changes following the code standards above.
3. Add tests for new functionality (target: 80% coverage on new code).
4. Run the full test suite: `python3 -m pytest tests/ -x`
5. Commit with a clear message (conventional commits preferred).
6. Push and open a PR with a description of what changed and why.
## Where to Start
- Look for issues labeled `good-first-issue` or `help-wanted`.
- Improve test coverage on domain modules.
- Add type hints to untyped public functions.
- Fix lint errors (`ruff check app/`).
- Improve documentation.
## Questions
Open a GitHub issue or discussion if you're unsure about something. We're happy to help.

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# RMI Darkroom — Complete Backend Documentation
## RugMunch Intelligence Platform — Admin Backend v2
---
## Overview
The RMI Darkroom is a comprehensive, enterprise-grade admin backend for the RugMunch Intelligence crypto security platform. It provides full control over users, content, wallets, payments, security, analytics, and token deployment across multiple blockchains.
**Status:** Production-ready | **Version:** 2.0 | **Date:** May 31, 2026
---
## Architecture
```
┌─────────────────────────────────────────────────────────────┐
│ RMI Darkroom Backend │
├─────────────────────────────────────────────────────────────┤
│ Admin SPA (/admin) │ Darkroom UI (/darkroom) │
│ ├─ Dashboard │ ├─ Token Deployer │
│ ├─ User Management │ ├─ Airdrop Manager │
│ ├─ Security Center │ ├─ Multi-chain Launch │
│ ├─ Wallet Manager │ └─ Custom Snapshots │
│ ├─ Analytics │ │
│ ├─ Bulletin Board │ │
│ ├─ Financial │ │
│ └─ Configuration │ │
├─────────────────────────────────────────────────────────────┤
│ API Layer (757+ endpoints) │
│ ├─ /api/v1/admin/backend/* — Admin operations │
│ ├─ /api/v1/wallets/v2/* — Wallet management │
│ ├─ /api/v1/analytics/* — Real-time analytics │
│ ├─ /api/v1/admin/bulletin/* — Content management │
│ ├─ /api/v1/admin/tokens/* — Token deployment │
│ └─ /api/v1/bulletin/* — Public content │
├─────────────────────────────────────────────────────────────┤
│ Core Engines │
│ ├─ Admin Backend (RBAC, Audit, Sessions) │
│ ├─ Wallet Manager v2 (25+ chains, HD, Rotation) │
│ ├─ Security Defense (Bot Detection, WAF, DDoS) │
│ ├─ Analytics Engine (Real-time, Prometheus, Grafana) │
│ ├─ Bulletin Board (CMS, Moderation, SEO) │
│ ├─ Token Deployer (5 chains, Blacklist, Anti-bot) │
│ └─ Plugin System (Extensible architecture) │
├─────────────────────────────────────────────────────────────┤
│ Data Layer │
│ ├─ Redis — Sessions, rate limits, caching │
│ ├─ Supabase — Users, audit logs, wallet data │
│ ├─ File System — Vault, configs, backups │
│ └─ ClickHouse — Analytics time-series (optional) │
└─────────────────────────────────────────────────────────────┘
```
---
## Key Features
### 1. Role-Based Access Control (RBAC)
- **5 roles:** superadmin, admin, moderator, viewer, support
- **Permission matrix** with 30+ granular permissions
- **IP allowlists** for admin access
- **Session management** with 8-hour timeout, max 3 concurrent
- **2FA support** (TOTP-ready)
### 2. Wallet Manager v2
- **25+ chains:** Bitcoin, Ethereum, Solana, TRON, Base, BSC, Polygon, Arbitrum, Optimism, Avalanche, Fantom, Gnosis, Dogecoin, Litecoin, and more
- **HD Wallets:** BIP39/BIP44/BIP49/BIP84 mnemonic support
- **Key Rotation:** Scheduled automatic rotation with notifications
- **Payment Integration:** x402 micropayments, subscription tiers
- **Balance Monitoring:** Real-time tracking across all chains
- **AES-256-GCM encryption** with Argon2id key derivation
- **Multi-signature ready** architecture
### 3. Security Defense System
- **Bot Detection:** Behavioral analysis, fingerprinting, heuristics
- **Anomaly Detection:** Statistical analysis on request patterns
- **Honeypot Endpoints:** 10 trap endpoints that auto-ban attackers
- **DDoS Protection:** Circuit breaker pattern, rate limiting
- **IP Reputation:** Integration-ready for AbuseIPDB
- **Geo-blocking:** Country-based access control
- **Request Fingerprinting:** Canvas, WebGL, font analysis
### 4. Analytics Engine
- **Real-time Metrics:** CPU, memory, requests, errors, latency
- **4 Default Dashboards:** System Health, Financial, Security, Users
- **Trend Detection:** Automatic anomaly detection with 3-sigma analysis
- **Prometheus Export:** Compatible with Prometheus/Grafana stack
- **WebSocket-ready:** Real-time streaming data
- **Custom Dashboards:** Configurable widget layouts
### 5. Bulletin Board / CMS
- **Post Management:** CRUD with versioning, scheduling, expiry
- **Categories:** news, alert, update, promo, system, community, announcement, tutorial
- **Targeting:** Audience segmentation (free, premium, pro, admins)
- **Moderation:** Draft/review/published/archived workflow
- **SEO:** Meta tags, OpenGraph, slug generation
- **Comments:** Threaded discussions with moderation
### 6. Token Deployer (Darkroom)
- **5 Chains:** Ethereum, Base, BSC, Solana, TRON
- **Features:** Blacklist, anti-bot, anti-sniper, team allocation, vesting
- **Airdrop System:** 1:1 exact-match airdrop, multi-chain snapshots
- **Custom Snapshots:** JSON, CSV, manual upload
- **Anti-gaming:** Sybil detection, multi-account filtering
---
## API Endpoints Summary
| Category | Endpoints | Auth |
|----------|-----------|------|
| Admin Auth | 5 | Public/Session |
| Dashboard | 2 | dashboard.read |
| Users | 5 | users.read/write |
| Security | 8 | security.read/write |
| System | 5 | system.read/write |
| Content | 3 | content.read/write |
| Financial | 3 | financial.read |
| API Keys | 3 | api_keys.read/write |
| Admin Mgmt | 4 | superadmin only |
| Backups | 2 | superadmin only |
| Webhooks | 2 | webhooks.read/write |
| **Wallet Manager v2** | **19** | token_deploy.read/write |
| **Analytics** | **11** | analytics.read |
| **Bulletin Board** | **18** | content.read/write |
| **Token Deployer** | **24** | X-Admin-Key |
| **Public Bulletin** | **5** | None |
| **Total** | **757+** | Mixed |
---
## File Structure
```
/root/backend/
├── app/
│ ├── admin_backend.py # Core admin engine (RBAC, audit, sessions)
│ ├── wallet_manager_v2.py # Wallet management engine
│ ├── security_defense.py # Bot detection, WAF, DDoS protection
│ ├── analytics_engine.py # Real-time metrics and dashboards
│ ├── bulletin_board.py # CMS engine
│ ├── plugin_system.py # Plugin architecture
│ ├── token_deployer.py # Multi-chain token deployer
│ ├── multichain_airdrop.py # Airdrop engine
│ └── routers/
│ ├── admin_backend.py # Admin API (36 endpoints)
│ ├── wallet_manager_v2.py # Wallet API (19 endpoints)
│ ├── analytics.py # Analytics API (11 endpoints)
│ ├── bulletin_board.py # Bulletin API (18 endpoints)
│ ├── darkroom_tokens.py # Token deployer API
│ ├── darkroom_airdrop.py # Airdrop API
│ └── darkroom_multichain.py # Multi-chain API
├── static/
│ ├── admin.html # Admin SPA (52KB)
│ └── darkroom.html # Token deployer UI (43KB)
└── main.py # FastAPI app (757+ routes)
```
---
## Security Features
### Authentication
- bcrypt password hashing
- JWT session tokens with expiry
- Rate limiting on all endpoints
- IP blocking with auto-ban
- Failed login tracking (auto-ban after 5 attempts)
- Session invalidation on logout
- Concurrent session limits
### Authorization
- Role-based access control
- Permission matrix per endpoint
- Admin-only endpoints for sensitive operations
- Audit logging of all actions
- Before/after state tracking
### Data Protection
- AES-256-GCM encryption for wallet keys
- Argon2id key derivation
- File permissions (chmod 600) on vault files
- No private keys in memory longer than necessary
- Secure session storage in Redis
### Network Security
- Bot detection with behavioral analysis
- Honeypot endpoints (auto-ban on trigger)
- DDoS circuit breaker
- Request fingerprinting
- Anomaly detection on traffic patterns
- Geo-blocking capability
---
## Wallet Manager v2
### Supported Chains
| Chain | Family | Address Pattern | HD Path |
|-------|--------|----------------|---------|
| Bitcoin | Bitcoin | 1/3/bc1... | m/44'/0'/0'/0/0 |
| Bitcoin SegWit | Bitcoin | 3/bc1... | m/49'/0'/0'/0/0 |
| Bitcoin Native SegWit | Bitcoin | bc1... | m/84'/0'/0'/0/0 |
| Ethereum | EVM | 0x... | m/44'/60'/0'/0/0 |
| Base | EVM | 0x... | m/44'/60'/0'/0/0 |
| Polygon | EVM | 0x... | m/44'/60'/0'/0/0 |
| Arbitrum | EVM | 0x... | m/44'/60'/0'/0/0 |
| Optimism | EVM | 0x... | m/44'/60'/0'/0/0 |
| Avalanche | EVM | 0x... | m/44'/60'/0'/0/0 |
| BSC | EVM | 0x... | m/44'/60'/0'/0/0 |
| Fantom | EVM | 0x... | m/44'/60'/0'/0/0 |
| Gnosis | EVM | 0x... | m/44'/60'/0'/0/0 |
| Solana | Solana | Base58 | m/44'/501'/0'/0' |
| TRON | TRON | T... | m/44'/195'/0'/0/0 |
| Dogecoin | Secp256k1 | D... | m/44'/3'/0'/0/0 |
| Litecoin | Secp256k1 | L/M/ltc1... | m/44'/2'/0'/0/0 |
### Wallet Tiers
| Tier | Use Case | Security |
|------|----------|----------|
| hot | Active trading | Standard |
| warm | Regular operations | Enhanced |
| cold | Long-term storage | High |
| vault | Maximum security | Multi-sig ready |
### Payment Integration
- **x402:** Enable per-wallet with price in USD
- **Subscriptions:** Tier-based (free, basic, pro, enterprise)
- **Payment Types:** x402, subscription, one-time, marketplace, refund, withdrawal, deposit, fee, reward
---
## Analytics Dashboards
### System Health Dashboard
- CPU Usage (gauge + line chart)
- Memory Usage (gauge + line chart)
- Disk Usage (gauge)
- Requests/minute (counter)
- Response Latency (line chart)
- Error Rate (line chart)
### Financial Dashboard
- Total Revenue (counter)
- MRR (counter)
- ARPU (counter)
- Churn Rate (gauge)
- Revenue Trend (line chart)
- Payment Count (line chart)
### Security Dashboard
- Threats Blocked (counter)
- Bot Requests (counter)
- Attacks Detected (counter)
- Blocked IPs (counter)
- Threat Types (pie chart)
- Attack Timeline (line chart)
### User Analytics Dashboard
- DAU (counter)
- MAU (counter)
- New Users (counter)
- Retention Rate (gauge)
- User Growth (line chart)
- User Tiers (pie chart)
---
## Plugin System
### Plugin Types
- **connector** — Data sources (exchanges, APIs, oracles)
- **scanner** — Security scanners (contract, wallet, token)
- **analyzer** — Analysis engines (risk, sentiment, on-chain)
- **notifier** — Alert channels (email, telegram, webhook)
- **exporter** — Data export (CSV, PDF, API, webhook)
- **wallet** — Wallet integrations (hardware, custodial)
- **payment** — Payment processors (x402, stripe, crypto)
- **ml** — ML models (fraud detection, prediction)
- **security** — Security tools (WAF, firewall)
- **analytics** — Analytics integrations (Grafana, Prometheus)
### Built-in Plugins
- PrometheusExporter — Export metrics to Prometheus format
- WebhookNotifier — Send notifications to webhooks
- RedisCache — Redis caching and pub/sub connector
### Plugin Directory
```
/root/backend/plugins/
├── connector/
├── scanner/
├── analyzer/
├── notifier/
├── exporter/
├── wallet/
├── payment/
├── ml/
├── security/
└── analytics/
```
---
## Deployment
### Requirements
- Python 3.10+
- Redis 6.0+
- FastAPI + Uvicorn
- Optional: Supabase, ClickHouse, Prometheus, Grafana
### Environment Variables
```bash
# Core
JWT_SECRET=your-jwt-secret
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_PASSWORD=your-redis-password
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_SERVICE_KEY=your-service-key
# Wallet Vault
WALLET_VAULT_PASSWORD=your-vault-password
# Admin
ADMIN_API_KEY=your-admin-key
# x402
X402_EVM_PAY_TO=your-wallet-address
# Security
ABUSEIPDB_API_KEY=your-abuseipdb-key # optional
```
### Startup
```bash
cd /root/backend
uvicorn main:app --host 0.0.0.0 --port 8000 --workers 4
```
### Health Check
```bash
curl http://localhost:8000/health
```
---
## Admin Access
### Default Admin
- **Email:** admin@rugmunch.io
- **Password:** Darkroom2025!
- **Role:** superadmin
### Admin UI
- **URL:** https://your-domain.com/admin
- **Login:** Email + Password + optional 2FA
- **Session:** 8-hour expiry, max 3 concurrent
### Darkroom (Token Deployer)
- **URL:** https://your-domain.com/darkroom
- **Auth:** X-Admin-Key header
---
## API Usage Examples
### Generate Wallet
```bash
curl -X POST https://api.rugmunch.io/api/v1/wallets/v2/generate \
-H "X-Admin-Session: sess_xxx" \
-H "Content-Type: application/json" \
-d '{"chain": "eth", "purpose": "payments", "tier": "hot"}'
```
### Record Payment
```bash
curl -X POST https://api.rugmunch.io/api/v1/wallets/v2/payments \
-H "X-Admin-Session: sess_xxx" \
-H "Content-Type: application/json" \
-d '{
"wallet_id": "wal_eth_123",
"wallet_address": "0x...",
"chain": "eth",
"payment_type": "x402",
"amount": 0.01,
"amount_usd": 25.00,
"user_id": "user_123"
}'
```
### Get Analytics
```bash
curl https://api.rugmunch.io/api/v1/analytics/dashboards/system \
-H "X-Admin-Session: sess_xxx"
```
### Prometheus Metrics
```bash
curl https://api.rugmunch.io/api/v1/analytics/prometheus
```
---
## Monitoring & Alerting
### Prometheus Metrics
All system metrics are exportable in Prometheus format at `/api/v1/analytics/prometheus`.
### Key Metrics
- `rmi_cpu_percent` — CPU usage
- `rmi_memory_percent` — Memory usage
- `rmi_requests_per_minute` — Request rate
- `rmi_response_time_ms` — Response latency
- `rmi_error_rate` — Error percentage
- `rmi_revenue_usd` — Total revenue
- `rmi_threats_blocked` — Threats blocked
- `rmi_active_users` — Active users
### Grafana Integration
Import the Prometheus endpoint into Grafana for visualization.
---
## Backup & Recovery
### Wallet Vault
- Encrypted JSON file at `/root/.rmi/wallets/vault_v2.json`
- Keystore at `/root/.rmi/wallets/keystore.enc`
- Payment log at `/root/.rmi/wallets/payments.jsonl`
### Backup Strategy
1. Daily encrypted backups to secure storage
2. Seed phrase recovery for HD wallets
3. Multi-signature backup for vault wallets
4. Audit log retention: 90 days
---
## Development
### Adding a New Plugin
```python
from app.plugin_system import Plugin, PluginType
class MyPlugin(Plugin):
@property
def name(self): return "my_plugin"
@property
def version(self): return "1.0.0"
@property
def plugin_type(self): return PluginType.ANALYZER
@property
def description(self): return "My custom analyzer"
def _setup(self):
# Initialize your plugin
pass
```
### Adding a Dashboard Widget
```python
from app.analytics_engine import DashboardWidget
widget = DashboardWidget(
widget_id="my_widget",
widget_type="line",
title="My Metric",
metric_name="my_metric",
width=6,
height=4,
)
engine.add_widget("system", widget)
```
---
## Security Checklist
- [ ] Change default admin password
- [ ] Set strong WALLET_VAULT_PASSWORD
- [ ] Enable Redis AUTH
- [ ] Configure IP allowlists for admin access
- [ ] Set up AbuseIPDB API key
- [ ] Enable 2FA for superadmin accounts
- [ ] Configure backup schedule
- [ ] Set up Prometheus/Grafana monitoring
- [ ] Enable HTTPS only
- [ ] Review audit logs weekly
- [ ] Rotate wallet keys quarterly
- [ ] Test disaster recovery plan
---
## Support
- **Email:** admin@rugmunch.io
- **Docs:** https://docs.rugmunch.io
- **API:** https://api.rugmunch.io/docs
- **Status:** https://status.rugmunch.io
---
## License
Proprietary and confidential. Unauthorized use, distribution, or reproduction is strictly prohibited.
Copyright (c) 2026 RugMunch Intelligence. All rights reserved.
---
**Built with:** FastAPI, Redis, Supabase, Python 3.12, love for crypto security.
**The Bloomberg Terminal of Shitcoins.**

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# RMI Backend — 2026 Architecture Design
## Why this exists
The current backend (`/root/backend/`) has grown organically:
- `main.py` is 10,305 lines
- 124 router files flat in `app/routers/`
- 14 RAG modules scattered at top of `app/`
- `token_scanner.py` is 4,109 lines
- `x402_tools.py` is 5,817 lines
- Cross-cutting concerns (redis, auth, errors) duplicated across modules
- Domain logic entangled with FastAPI
- No tests, no type safety, no clear boundaries
15 mechanical refactor tasks would patch symptoms. This design fixes the architecture.
## Target Layout
```
/root/backend/
├── pyproject.toml uv + ruff + mypy + pytest config (single source)
├── .pre-commit-config.yaml ruff + mypy + size cap + gitleaks
├── Dockerfile
├── alembic/ async migrations
│
├── app/
│ ├── main.py <100 lines: app factory + lifespan + middleware ONLY
│ ├── config.py pydantic-settings, env loading
│ │
│ ├── core/ cross-cutting, NO business logic
│ │ ├── logging.py structlog JSON + correlation ID
│ │ ├── errors.py AppError hierarchy + FastAPI handlers
│ │ ├── redis.py async client + get_redis() Depends()
│ │ ├── db.py async SQLAlchemy session
│ │ ├── auth.py JWT decode + role guards
│ │ ├── lifespan.py startup/shutdown
│ │ ├── middleware.py CORS, rate limit, correlation ID
│ │ ├── websocket.py WS connection manager
│ │ ├── tracing.py OpenTelemetry + Langfuse v4 init
│ │ ├── http.py async httpx client
│ │ └── pagination.py cursor-based
│ │
│ ├── api/ HTTP transport, thin routes
│ │ ├── deps.py shared Depends (current_user, redis, etc)
│ │ ├── v1/
│ │ │ ├── public/ no auth — scanner, wallet, token, pricing, health
│ │ │ ├── auth/ JWT — portfolio, alerts, intel, profile
│ │ │ ├── admin/ admin — users, system, ops
│ │ │ ├── x402/ paid — tools, tokens, wallets, defi, security
│ │ │ └── mcp/ MCP — tools.py
│ │ └── ws/ WebSocket
│ │ └── alerts.py
│ │
│ ├── domain/ pure business logic, NO FastAPI imports
│ │ ├── scanner/ core + honeypot + rugcheck + holders + contract + deployer + models + service
│ │ ├── wallet/ analyzer + labels + behavior + models + service
│ │ ├── token/ discovery + supply + models + service
│ │ ├── rag/ embeddings + chunking + search + ingest + firehose + feedback + agentic + evaluation + tracing + router + permanence + models + service
│ │ ├── x402/ facilitator + tokens + enforcement + settlement + models + service
│ │ ├── intel/ feeds + narratives + graph + models + service
│ │ ├── scam/ classifier + patterns + models + service
│ │ ├── databus/ client + chains(96) + models + service
│ │ └── bulletin/ board + models + service
│ │
│ ├── infra/ external integrations
│ │ ├── ollama.py
│ │ ├── langfuse.py
│ │ ├── vector_store.py
│ │ ├── chains/ evm + solana + bitcoin + base + ...
│ │ ├── apis/ coingecko + etherscan + birdeye + goplus + ...
│ │ └── providers/ ollama + openrouter + huggingface + ...
│ │
│ └── workers/ background jobs (separate from API)
│ ├── firehose.py
│ ├── scanner_queue.py
│ ├── ingest_cron.py
│ └── cleanup.py
│
└── tests/
├── conftest.py
├── unit/domain/
└── integration/api/v1/
```
## Key Design Principles
1. **STRICT LAYERING.** `api → domain → infra`. Never reverse. Domain knows nothing about HTTP.
2. **ONE SOURCE OF TRUTH for cross-cutting.** redis/auth/errors/logging live in `core/` exactly once. Routes import, never redefine.
3. **HARD SIZE CAP.** 500 lines per file. Enforced in pre-commit. No 4,109-line `token_scanner.py` ever again.
4. **THIN ROUTES.** Routes parse → call service → return. No business logic in HTTP layer.
5. **DOMAIN = PURE PYTHON.** `domain/scanner/` can be unit tested without spinning up FastAPI. This is the test that proves the architecture.
6. **WORKERS SEPARATED.** Background jobs don't pollute the API. firehose, scanner_queue, ingest_cron live in `workers/`.
7. **PYDANTIC V2 EVERYWHERE.** Every domain has `models.py`. No `dict` types crossing boundaries.
8. **ASYNC-ONLY.** No sync I/O in handlers. Same shape for the whole codebase.
9. **OBSERVABILITY BY DEFAULT.** structlog JSON + correlation ID + OTel + Langfuse in `core/tracing.py`. Every endpoint instrumented without opt-in.
10. **STRANGLER FIG MIGRATION.** New skeleton co-exists with old code. Old `main.py` keeps importing the old routers. New routes added alongside. Per-domain cutover, not big-bang.
## Migration Order
| Order | Domain | Why |
|-------|--------|-----|
| 0 | `rag_engine` shim | unblock prod crash, temp until `app/rag/` lands |
| 1 | `core/` | foundation everyone depends on |
| 2 | `infra/` | external integrations domain depends on |
| 3 | `alerts` | smallest, well-bounded, has WS + JWT + redis — proves full pattern |
| 4 | `wallet` | high-value, used by frontend |
| 5 | `token` | high-value |
| 6 | `scanner` | biggest (4,109 lines), do last when pattern is mature |
| 7 | `x402` | payment system, critical, mature pattern by then |
| 8 | `intel`, `scam`, `databus`, `bulletin` | long tail |
| 9 | `rag` consolidation (was 14 files) | last because it's the most coupled |
## What Ships This Pass (Foundation)
1. Fix crash — `rag_engine` re-export shim, backend healthy
2. `pyproject.toml` — uv + ruff + mypy strict + pytest
3. `.pre-commit-config.yaml` — ruff + mypy + size cap (500) + gitleaks
4. `app/core/` — 11 modules, each <200 lines
5. `app/api/v1/__init__.py` — router aggregator that still imports OLD routers (zero breakage)
6. `app/main.py` — rewritten to ~100 lines, calls lifespan + middleware from `core/`, mounts new aggregator
7. Verify: backend boots, all 757 routes respond, health 200, no import errors
8. Commit + deploy
## What Does NOT Ship This Pass
- Migrating alerts/wallet/token/scanner to new `domain/`. That's Phase 2.
- The 15 mechanical refactors. Replaced with the layered architecture.
- Deleting old code. Strangler fig — old stays until domain is migrated.
## Phase 2: Alerts Vertical Slice (proves the pattern)
After foundation lands, migrate `alerts` end-to-end as the reference:
```
app/domain/alerts/
├── models.py # Alert, AlertRule, Notification — Pydantic v2
├── repository.py # async SQLAlchemy queries
├── service.py # business logic, pure Python
└── broadcaster.py # WebSocket broadcast helper
app/api/v1/auth/alerts.py # thin route: parse → call service → return
```
This proves the pattern works: domain is pure Python, route is <100 lines, can be unit tested without HTTP.
When alerts is shipped and verified in prod, the same pattern is applied to wallet, token, scanner, etc.

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FROM python:3.11-slim
WORKDIR /app
# System deps + solc + Foundry + Tini (consolidated for smaller layer)
RUN apt-get update && apt-get install -y --no-install-recommends \
gcc libpq-dev curl git ca-certificates && \
curl -sL https://github.com/krallin/tini/releases/download/v0.19.0/tini -o /tini && \
chmod +x /tini && \
rm -rf /var/lib/apt/lists/* /var/cache/apt/archives/*
# Solidity compiler (kept — used by contract scanners)
RUN curl -sL https://github.com/ethereum/solidity/releases/download/v0.8.26/solc-static-linux -o /usr/local/bin/solc && \
chmod +x /usr/local/bin/solc
# Foundry (cast, forge) — EVM contract analysis
RUN curl -sL https://foundry.paradigm.xyz | bash && \
export PATH="$HOME/.foundry/bin:$PATH" && \
foundryup
# Python deps (ordered for layer caching: requirements first, then app)
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt && \
pip install --no-cache-dir slither-analyzer && \
rm -rf /root/.cache/pip
# Copy app (HF models excluded via .dockerignore — they download at runtime)
COPY . .
# Health check (uses /live for liveness, not /ready)
HEALTHCHECK --interval=30s --timeout=5s --retries=3 \
CMD curl -f http://localhost:8000/live || exit 1
EXPOSE 8000
# Use Tini as PID 1 to properly reap zombie processes and forward signals
ENTRYPOINT ["/tini", "--"]
CMD ["python", "-u", "main.py"]

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FROM python:3.11-slim
WORKDIR /app
RUN apt-get update && apt-get install -y --no-install-recommends \
gcc libpq-dev && \
rm -rf /var/lib/apt/lists/*
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["python", "-u", "worker.py"]

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# Email Forwarding Setup Guide
# =============================
#
# This system forwards emails from your domains to a Gmail inbox,
# then polls that inbox and forwards emails to your Telegram admin bot.
#
# NO AUTO-REPLIES - emails appear in Telegram for admin review.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
SETUP STEPS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. CREATE GMAIL RECEIVER ACCOUNT
------------------------------
Create a new Gmail account (or use existing):
Example: rugmunch.admin@gmail.com
IMPORTANT: Enable 2FA and create an App Password:
- Go to: https://myaccount.google.com/security
- Enable 2-Step Verification (if not already)
- Go to: https://myaccount.google.com/apppasswords
- Create a new app password (select "Mail" and your device)
- COPY THE 16-CHARACTER PASSWORD (no spaces)
You'll need:
- Email: rugmunch.admin@gmail.com (or your choice)
- App Password: XXXX XXXX XXXX XXXX (16 chars)
2. CONFIGURE CLOUDFLARE EMAIL ROUTING
-----------------------------------
For rugmunch.io:
- Log in to Cloudflare dashboard
- Go to your rugmunch.io zone
- Click "Email" → "Email Routing"
- Click "Add Route"
- Create these addresses (all forward to same Gmail):
admin@rugmunch.io → rugmunch.admin@gmail.com
support@rugmunch.io → rugmunch.admin@gmail.com
contact@rugmunch.io → rugmunch.admin@gmail.com
For cryptorugmunch.com:
- Go to your cryptorugmunch.com zone
- Click "Email" → "Email Routing"
- Click "Add Route"
- Create these addresses:
admin@cryptorugmunch.com → rugmunch.admin@gmail.com
team@cryptorugmunch.com → rugmunch.admin@gmail.com
info@cryptorugmunch.com → rugmunch.admin@gmail.com
IMPORTANT: Make sure Email Routing is ENABLED (toggle ON)
3. CONFIGURE BACKEND ENVIRONMENT VARIABLES
----------------------------------------
Add these to your /root/.secrets/project_envs/rmi-backend.env:
# Email Forwarding
EMAIL_RECEIVER=rugmunch.admin@gmail.com
EMAIL_PASSWORD=xxxx xxxx xxxx xxxx (app password, no spaces)
TELEGRAM_ADMIN_CHAT_ID=123456789 (your admin Telegram chat ID)
EMAIL_POLL_INTERVAL=60 # check every 60 seconds
# Get your Telegram Chat ID:
- Message your bot: @userinfobot
- Send any message
- It will reply with your ID (e.g., 123456789)
4. START RESTART BACKEND
----------------------
Restart your backend service:
sudo systemctl restart rmi-backend
Or if running manually:
cd /srv/rmi/backend
source venv/bin/activate
python main.py
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
VERIFICATION
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Check logs for email polling startup:
sudo journalctl -u rmi-backend -f
You should see:
[RMI] 📧 Email polling enabled - starting IMAP service
Send a test email to:
admin@rugmunch.io
Wait 1-2 minutes. You should receive a Telegram message in your admin chat:
📧 New Email
From: sender@example.com
Domain: rugmunch.io
Subject: Test Email
Time: 2026-05-01 12:34:56
━━━━━━━━━━━━━━━━━━━━
This is the email body...
Inbox: rugmunch.admin@gmail.com
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
FAQ
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Q: Can users reply to these emails?
A: Emails can be replied to (standard email), but your system
won't auto-reply. Admins review in Telegram and respond manually
via their email client.
Q: What happens if Gmail IMAP fails?
A: The poller logs errors and retries on next interval. Emails
are marked as read, so they won't be re-sent to Telegram.
Q: Can I forward to a different email?
A: Yes, just change EMAIL_RECEIVER to any Gmail/IMAP-enabled
address. You'll need an app password for Gmail.
Q: How many emails can I receive?
A: Gmail free accounts: 15GB storage (roughly 10,000+ emails)
Cloudflare Email Routing: Unlimited forwards
Q: Do I need to configure MX records?
A: NO! Cloudflare Email Routing handles this automatically when
you enable Email Routing for your zone.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TROUBLESHOOTING
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
"IMAP login failed":
- Check EMAIL_PASSWORD is correct (use app password, not regular password)
- Ensure IMAP is enabled in Gmail settings
- Try logging into Gmail IMAP manually: telnet imap.gmail.com 993
"No emails appearing in Telegram":
- Verify Cloudflare Email Routing is ENABLED
- Check that emails are actually arriving in Gmail inbox
- Look at backend logs for polling errors
- Wait up to 2 minutes (poll interval)
"Duplicate emails in Telegram":
- Emails are marked as read after first poll
- Check Gmail isn't moving emails back to inbox
- Reset seen_message_ids by restarting backend
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

50
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Rug Munch Intelligence — Source Available License 1.0
Copyright (c) 2024-2026 Rug Munch Media LLC. All rights reserved.
──────────────────────────────────────────────────────────────────────
YOU MAY:
• View, fork, and study this code for educational purposes
• Submit pull requests (contributions become our property)
• Use the public API at rugmunch.io within free-tier limits
• Reference this code in security audits of our platform
──────────────────────────────────────────────────────────────────────
YOU MAY NOT:
• Use this code or any derivative for commercial purposes
• Deploy, host, or operate this software as a service
• Sell, license, or distribute this software or derivatives
• Use this code to build competing crypto security products
• Use this code for scam detection, rug pull analysis, or
blockchain forensics outside of rugmunch.io's official API
• Extract, reverse engineer, or replicate our data sourcing
methods, scam detection heuristics, or intelligence pipelines
• Redistribute this code in any form without written permission
• Use this code to train AI/ML models without a data license
• Scrape, harvest, or bulk-extract from rugmunch.io endpoints
──────────────────────────────────────────────────────────────────────
COMMERCIAL USE:
To use this software commercially, contact:
admin@rugmunch.io
We offer enterprise licenses, white-label deployments, and
custom integrations for qualified partners.
──────────────────────────────────────────────────────────────────────
DATA & METHODOLOGY:
All scam detection heuristics, intelligence pipelines, source
aggregation methods, wallet labeling techniques, and backend
infrastructure are RUG MUNCH MEDIA LLC PROPRIETARY ASSETS.
These are NOT covered by any open-source grant. Unauthorized
extraction, replication, or disclosure of these methods
constitutes intellectual property theft.
──────────────────────────────────────────────────────────────────────
NO WARRANTY: THIS SOFTWARE IS PROVIDED "AS IS" WITHOUT WARRANTY OF
ANY KIND. RUG MUNCH MEDIA LLC DISCLAIMS ALL LIABILITY FOR DAMAGES
OR LOSSES INCURRED THROUGH USE OF THIS SOFTWARE.
THE FREE TIER at rugmunch.io IS A SERVICE, NOT A RIGHT. WE RESERVE
THE RIGHT TO LIMIT, MODIFY, OR DISCONTINUE ACCESS AT ANY TIME.
──────────────────────────────────────────────────────────────────────
OFFICIAL PLATFORM:
https://rugmunch.io
@CryptoRugMunch (Telegram, X, Mastodon, Bluesky)
admin@rugmunch.io
──────────────────────────────────────────────────────────────────────

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# RUG MUNCH MEDIA LLC — LICENSING & PRICING STRATEGY
> **Complete decisions for every system.**
> Last updated: 2026-07-01
> Goal: maximize profit, maintain trust, be first-mover in the market.
---
## 1. LICENSE DECISIONS (Per System)
| System | License | Why |
|--------|---------|-----|
| **WalletConnect integration** (in WalletPress) | **MIT** | Trust requires open source. Community audits wallet security code. No competitive advantage in the protocol itself. |
| **WalletPress core** (self-hosted backend) | **BSL 1.1** (Business Source) | Readable, auditable, but can't commercially clone. Converts to MIT on 2029-01-01. |
| **WalletPress x402 marketplace** (pay-per-wallet) | **Proprietary** | Our revenue engine. Don't show competitors how we price/route payments. |
| **PryScraper** | **Proprietary** | Our competitive moat. Stealth browser, anti-detection — we don't want anyone copying. |
| **RMI (Rug Munch Intelligence)** | **Open Core** (MIT core + Proprietary pro) | Trust is #1 in crypto. MIT detector framework builds community. Proprietary platform = revenue. |
| **rmi-mcp-x402** (MCP server) | **MIT** (for community) + **x402 pay-per-call** (revenue) | MCP servers SHOULD be open source for adoption. Revenue comes from x402 usage, not licensing. |
---
## 2. WALLETPRESS — DETAILED PROFIT MODEL
WalletPress is **THREE products** that need different strategies:
### 2.1 WalletConnect Integration Layer (MIT)
**What it is:** The wallet connection protocol/dApp bridge inside WalletPress.
**License:** MIT — fully open source.
**Why:** Trust. If users connect their wallets through our code, they need to verify it's safe. Closed source = no trust = no users.
**Revenue from this:** NONE directly. This is a **loss leader** that makes the rest of WalletPress trustworthy.
**What goes in this layer:**
- dApp connector (WalletConnect v2 protocol)
- Multi-chain address derivation (BIP-44/49/84)
- Address validation
- ENS/Unstoppable Domains resolution
- Hardware wallet support (Ledger, Trezor)
### 2.2 WalletPress Self-Hosted Core (BSL 1.1)
**What it is:** The main `main.py` FastAPI backend. Users self-host on their own infrastructure.
**License:** BSL 1.1 (Business Source License). Convert to MIT on 2029-01-01.
**Why BSL not MIT:**
- If MIT, anyone can rebrand and sell "WalletPress Pro" competing with us
- BSL allows: read the code, modify for personal use, contribute back
- BSL forbids: selling the software as a competing product
**Pricing — Dual System:**
| Tier | Price | What You Get |
|------|-------|--------------|
| **Community (BSL)** | Free | Full self-hosted backend, all 14 chains, CLI, API, WP plugin |
| **Self-Hosted Pro** | $99/mo | Priority support, auto-updates, advanced features, multi-user |
| **Self-Hosted Enterprise** | $2,400/yr | SSO, audit logs, SLA, dedicated support engineer |
**Revenue projection (Year 1):**
- 200 Community users (free, builds network)
- 50 Pro users × $99 = $4,950/mo = $59,400/yr
- 10 Enterprise × $2,400 = $24,000/yr
- **Self-hosted total: $83,400/yr**
### 2.3 WalletPress x402 Marketplace (Proprietary)
**What it is:** Standalone pay-per-wallet service at `walletpress.cc`. No account, no subscription. Bots/developers pay USDC per wallet generation.
**License:** Proprietary. Don't show competitors our pricing algorithms.
**Pricing — Pay-per-wallet:**
| Service | Price | Use Case |
|---------|-------|----------|
| **Generate wallet** | $0.10/wallet | Bot needs fresh wallet per user |
| **Generate HD batch** (100 wallets) | $5.00 | Bulk wallet generation |
| **Generate HD batch** (1000 wallets) | $25.00 | Enterprise bulk |
| **Derive address from mnemonic** | $0.02/derive | Read-only address extraction |
| **Check balance** | $0.01/check | Wallet monitoring |
| **Sign message** | $0.05/sign | Bot authentication |
| **Send transaction** | $0.10 + gas | Automated payouts |
| **Full transaction suite** | $0.50/tx | Multi-sig, scheduling, batching |
**Revenue projection (Year 1):**
- Average usage: 50,000 wallet generations/mo × $0.10 = $5,000/mo
- Power users: 5 × $500/mo = $2,500/mo
- Enterprise: 2 × $2,000/mo = $4,000/mo
- **x402 marketplace total: $138,000/yr**
### 2.4 WalletPress Cloud (Hosted SaaS)
**What it is:** We host WalletPress for users who don't want to self-host. Same features, managed by us.
**License:** Proprietary SaaS.
**Pricing — Subscription tiers:**
| Tier | Price | Features |
|------|-------|----------|
| **Starter** | $29/mo | 100 wallets, 14 chains, basic API |
| **Growth** | $99/mo | 1,000 wallets, x402 enabled, priority support |
| **Business** | $299/mo | 10,000 wallets, team features, SSO |
| **Enterprise** | $999/mo | Unlimited wallets, dedicated support, custom chains |
**Revenue projection (Year 1):**
- 100 Starter × $29 = $2,900/mo
- 30 Growth × $99 = $2,970/mo
- 10 Business × $299 = $2,990/mo
- 3 Enterprise × $999 = $2,997/mo
- **Cloud total: $142,000/yr**
### 2.5 WalletPress TOTAL Revenue Projection
| Stream | Year 1 | Year 2 | Year 3 |
|--------|--------|--------|--------|
| Self-hosted Pro | $59K | $180K | $360K |
| Self-hosted Enterprise | $24K | $60K | $120K |
| x402 marketplace | $138K | $400K | $1M |
| Cloud SaaS | $142K | $500K | $1.2M |
| **TOTAL** | **$363K** | **$1.14M** | **$2.68M** |
---
## 3. PRYSCRAPER — DETAILED PROFIT MODEL
### 3.1 License: PROPRIETARY (CONFIRMED)
**Why:**
- Competitive moat is our stealth browser, anti-detection, and bypass techniques
- If competitors see our code, they can replicate in days
- Crypto scrapers are a race — whoever has the best stealth wins
### 3.2 Pricing — Three Models
**Model A: SaaS (Primary)**
- Hosted API at `pry.dev`
- Pay-per-request with x402 micropayments
- Free tier: 1,000 requests/month
- Pro: $49/mo for 100K requests
- Enterprise: Custom pricing for high volume
| Tier | Price | Volume |
|------|-------|--------|
| **Free** | $0 | 1,000 req/mo |
| **Pro** | $49/mo | 100,000 req/mo |
| **Scale** | $199/mo | 500,000 req/mo |
| **Enterprise** | Custom | 5M+ req/mo |
**Model B: x402 Pay-per-call**
- No account needed
- Pay USDC per API call
- AI agents pay automatically
| Endpoint | Price |
|----------|-------|
| `/scrape` | $0.005/call |
| `/crawl` | $0.02/call |
| `/extract` | $0.01/call |
| `/screenshot` | $0.003/call |
| `/stealth_browser` | $0.05/minute |
**Model C: White-label Enterprise**
- Deploy PryScraper on your infrastructure
- Your branding, your data
- Annual license
| Tier | Price |
|------|-------|
| **Startup** | $10K/yr (up to 1M req/mo) |
| **Growth** | $50K/yr (up to 10M req/mo) |
| **Enterprise** | $200K+/yr (unlimited) |
### 3.3 PryScraper Revenue Projection
| Stream | Year 1 | Year 2 | Year 3 |
|--------|--------|--------|--------|
| SaaS subscriptions | $80K | $300K | $600K |
| x402 pay-per-call | $30K | $120K | $400K |
| White-label Enterprise | $200K | $600K | $1.2M |
| **TOTAL** | **$310K** | **$1.02M** | **$2.2M** |
---
## 4. RMI (RUG MUNCH INTELLIGENCE) — DETAILED PROFIT MODEL
### 4.1 License: OPEN CORE (CONFIRMED)
| Layer | License |
|-------|---------|
| RUI Core (8 basic detectors, public API) | MIT |
| RUI Pro (32 detectors, x402, MCP, RAG) | Commercial |
| RUI Enterprise (on-premise) | BSL 1.1 |
| RUI Cloud (managed) | SaaS |
| Detector framework (community-built) | MIT |
| Rug Munch Verified badge | Proprietary Terms |
### 4.2 Pricing — Already Designed in LICENSING_STRATEGY.md
**Pro tier: $99/mo**
**Team tier: $499/mo**
**Enterprise: $10K+/yr**
**Cloud: Pay-per-use**
### 4.3 RMI Revenue Projection
| Stream | Year 1 | Year 2 | Year 3 |
|--------|--------|--------|--------|
| Pro subscriptions | $120K | $600K | $1.2M |
| Team subscriptions | $60K | $300K | $600K |
| Enterprise contracts | $90K | $360K | $900K |
| x402 pay-per-call | $30K | $180K | $500K |
| Cloud managed | $0 | $120K | $400K |
| Verified badges | $80K | $200K | $400K |
| **TOTAL** | **$380K** | **$1.76M** | **$4.0M** |
---
## 5. RMI-MCP-X402 — DETAILED PROFIT MODEL
### 5.1 License: MIT + x402 Pay-per-call
**Why MIT:** MCP servers are ecosystem infrastructure. The more people use them, the more the ecosystem grows. Revenue comes from x402 usage, not licensing.
### 5.2 Pricing — x402 Pay-per-call (No subscriptions)
| Tool | Price | Description |
|------|-------|-------------|
| `rugmunch_scan_token` | $0.001 | Full token scan (32 detectors) |
| `rugmunch_wallet_forensics` | $0.01 | Wallet behavior analysis |
| `rugmunch_rug_probability` | $0.005 | AI rug prediction |
| `rugmunch_contract_audit` | $0.05 | Smart contract security |
| `rugmunch_threat_intel` | $0.002 | Threat intelligence lookup |
| `rugmunch_real_time_alert` | $0.001/min | Real-time monitoring |
| `rugmunch_address_labels` | $0.001 | Wallet label lookup |
| `rugmunch_chain_info` | Free | Multi-chain info |
### 5.3 rmi-mcp-x402 Revenue Projection
| Stream | Year 1 | Year 2 | Year 3 |
|--------|--------|--------|--------|
| x402 pay-per-call | $20K | $150K | $500K |
| Enterprise MCP hosting | $0 | $50K | $200K |
| **TOTAL** | **$20K** | **$200K** | **$700K** |
---
## 6. SPECIFIC IMPROVEMENTS PER SYSTEM
### 6.1 WalletPress Improvements (Self-Hosted BSL)
**Priority 1 (This Week):**
1. **Add WalletConnect v2 integration** — dApp connector for 300+ wallets
2. **Hardware wallet support** — Ledger, Trezor, GridPlus
3. **Multi-sig wallets** — Gnosis Safe integration for 2-of-3, 3-of-5
4. **Address book encryption** — Encrypted contact storage
5. **ENS/Unstoppable Domains** — Human-readable address resolution
**Priority 2 (This Month):**
6. **x402 marketplace UI** — pricing page at walletpress.cc/x402
7. **Stripe billing** — for self-hosted Pro subscriptions
8. **Auto-update mechanism** — Pro users get automatic updates
9. **License key system** — for Pro/Enterprise features
10. **Audit log API** — for Enterprise compliance
**Priority 3 (This Quarter):**
11. **WalletConnect v2 certified** — official WC integration
12. **Multi-user teams** — Organizations, permissions, roles
13. **Transaction scheduling** — Recurring payments, vesting
14. **Gas optimization** — EIP-1559, batch transactions
15. **Mobile SDK** — React Native, Flutter
### 6.2 PryScraper Improvements (Proprietary)
**Priority 1 (This Week):**
1. **camoufox integration** — Firefox-based anti-detection
2. **TLS fingerprint randomization** — Per-request unique fingerprints
3. **Cookie warming** — Pre-aged cookies for trust signals
4. **Residential proxy pool** — 100+ rotating IPs
5. **CAPTCHA solver integration** — 2captcha, anti-captcha
**Priority 2 (This Month):**
6. **JavaScript rendering improvements** — Better React/Vue/Angular support
7. **PDF extraction upgrade** — OCR for scanned documents
8. **Structured data extraction** — Schema.org, JSON-LD, microdata
9. **Screenshot comparison** — Visual diffing for change detection
10. **Rate limiting intelligence** — Per-domain adaptive limits
**Priority 3 (This Quarter):**
11. **AI-powered extraction v2** — Better LLM prompts, structured outputs
12. **Browser extension** — Chrome/Firefox scraping tool
13. **Shopify/WooCommerce integration** — E-commerce scraping
14. **Real-time monitoring** — Webhook + Slack/Discord alerts
15. **Multi-region deployment** — US, EU, APAC for speed
### 6.3 RMI Improvements (Open Core)
**Priority 1 (This Week):**
1. **Split codebase** — core/ (MIT) + pro/ (commercial)
2. **Add LICENSE headers** — Every file has SPDX identifier
3. **MCP tool naming** — rugmunch_scan_token (clear + discoverable)
4. **Verified badge system** — Already built! ✅
5. **Live demo at rugmunch.io** — Paste address → see 32 detector scores
**Priority 2 (This Month):**
6. **Add 8 more detectors** — Currently have 32, add 8 more
7. **RAG investigation reports** — AI-powered forensic analysis
8. **Real-time webhook alerts** — Token launches, deployer activity
9. **Chrome extension "Rug Munch Shield"** — Warns before visiting phishing sites
10. **YouTube demo series** — "How to detect a rug in 30 seconds"
**Priority 3 (This Quarter):**
11. **Threat intel feeds to exchanges** — $10K/mo per exchange
12. **DAO treasury protection** — $5K/mo per DAO
13. **Verified badge at scale** — $500/token, 100 tokens = $50K/mo
14. **Bug bounty program** — $50K for finding wrong safe verdict
15. **AI agent marketplace** — Agents built on top of RMI
### 6.4 rmi-mcp-x402 Improvements (MIT + x402)
**Priority 1 (This Week):**
1. **PyPI package** — `pip install rugmunch-mcp`
2. **Register on pulsemcp.com** — MCP server directory
3. **Register on glama.ai** — Codeberg's MCP registry
4. **Register on mcp.so** — Smithery registry
5. **MCP tool names** — Clear, discoverable, consistent
**Priority 2 (This Month):**
6. **8+ MCP tools** — Already have the framework
7. **x402 payment integration** — USDC on Base, Solana
8. **Streaming responses** — For long-running scans
9. **Batch operations** — Scan multiple tokens in one call
10. **Webhook subscriptions** — Real-time alerts via MCP
**Priority 3 (This Quarter):**
11. **MCP server hosting** — Managed MCP at mcp.rugmunch.io
12. **Custom tool builder** — Let users add their own tools
13. **Tool analytics** — Usage stats, popular tools
14. **Multi-MCP routing** — One request, multiple MCPs
15. **MCP marketplace** — Third-party tools on our platform
---
## 7. UNIFIED REVENUE PROJECTION (All Systems)
| System | Year 1 | Year 2 | Year 3 |
|--------|--------|--------|--------|
| **RMI (Rug Munch Intelligence)** | $380K | $1.76M | $4.0M |
| **WalletPress** (self-hosted + x402 + cloud) | $363K | $1.14M | $2.68M |
| **PryScraper** (SaaS + x402 + white-label) | $310K | $1.02M | $2.2M |
| **rmi-mcp-x402** (x402 pay-per-call) | $20K | $200K | $700K |
| **TOTAL** | **$1.07M** | **$4.12M** | **$9.58M** |
---
## 8. GO-TO-MARKET SEQUENCE
### Phase 1: Trust Foundation (Month 1-3)
- Launch RUI Core as MIT (open source)
- Launch PryScraper as SaaS (no source)
- Launch WalletPress Community (BSL, free self-hosted)
- Goal: 1,000 GitHub stars, 100 SaaS users
### Phase 2: Revenue (Month 4-6)
- Launch RUI Pro ($99/mo)
- Launch PryScraper Pro ($49/mo)
- Launch WalletPress x402 marketplace ($0.10/wallet)
- Goal: $50K MRR
### Phase 3: Enterprise (Month 7-12)
- Launch RUI Enterprise ($10K+/yr)
- Launch WalletPress Enterprise ($2,400/yr)
- Launch PryScraper White-label ($10K+/yr)
- Goal: $1M ARR
### Phase 4: Scale (Year 2)
- Launch RUI Cloud (managed SaaS)
- Launch WalletPress Cloud (hosted)
- Launch MCP marketplace
- Goal: $4M ARR
### Phase 5: Dominate (Year 3)
- First-mover advantage compounds
- Network effects (more users = better data = better product)
- Goal: $10M ARR
---
## 9. COMPETITIVE POSITIONING
### WalletPress vs Competition
| Competitor | Our Advantage |
|------------|---------------|
| Trust Wallet | Open source, auditable, 14 chains vs 10 |
| MetaMask | Self-hostable, institutional features |
| Exodus | BSL means we can build features they can't copy |
| Coinbase Wallet | We don't have their KYC baggage |
### PryScraper vs Competition
| Competitor | Our Advantage |
|------------|---------------|
| ScrapingBee | Proprietary = we don't show them how |
| Bright Data | x402 pay-per-call, no minimums |
| ScraperAPI | $0.005/call vs $0.10/call, 20x cheaper |
| Apify | We have AI extraction built in |
### RMI vs Competition
| Competitor | Our Advantage |
|------------|---------------|
| GoPlus | Open core = verifiable, x402 = AI agents |
| De.Fi | Open source = trustworthy |
| Token Sniffer | 32 detectors vs their 5, 96 chains |
| Chainalysis | 100x cheaper |
---
## 10. THE FIRST-MOVER ADVANTAGE
Why we win in 2026:
1. **RUI is the first open-core crypto intelligence platform** — competitors are all closed
2. **PryScraper is the first x402-native scraper** — competitors charge $0.10/call, we charge $0.005
3. **WalletPress is the first BSL wallet** — community can audit, competitors can't clone
4. **rmi-mcp-x402 is the first MCP server for crypto** — AI agents will use us by default
5. **The Rug Munch Verified badge is the first honest assessment** — others are paid shills
We are in the right place at the right time. The only thing that can stop us is execution.
---
## 11. NEXT STEPS (Immediate)
### This Week
- [ ] Decide on WalletConnect = MIT (done above)
- [ ] Add WalletConnect v2 to WalletPress
- [ ] Build `pip install rugmunch-mcp` package
- [ ] Register on pulsemcp.com + glama.ai
- [ ] Split RMI code into core/ (MIT) + pro/ (commercial)
### This Month
- [ ] Launch PryScraper Pro tier ($49/mo)
- [ ] Launch WalletPress x402 marketplace UI
- [ ] Launch RUI Pro tier ($99/mo)
- [ ] Create rugmunch.io live demo
- [ ] Start content marketing (YouTube, blog)
### This Quarter
- [ ] Launch Verified Badge program
- [ ] Launch PryScraper White-label
- [ ] Launch RUI Cloud
- [ ] Launch WalletPress Cloud
- [ ] Enterprise sales (DAOs, exchanges)
---
**The decisions are made. The licenses are set. The pricing is designed. The first-mover window is open. Now we ship.**

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# Backend Port Map
## Active Services
| Port | Status | Purpose |
|------|--------|---------|
| 8002 | Running | Legacy instance |
| 8003 | Running | Legacy instance |
| 8005 | Running | Legacy instance |
| 8006 | Running | Legacy instance |
| 8010 | Running | **Current dev instance** |
## Port SelectionGuide
To find an available port:
```bash
# Check what's listening
netstat -tlnp | grep -E ':800[0-9]'
# Or use Python
python3 -c "import socket; s=socket.socket(); s.bind(('', 0)); print('Free port:', s.getsockname()[1]); s.close()"
```
## Testing Commands
```bash
# Health check
curl http://localhost:8010/health
# Ready check
curl http://localhost:8010/ready
# Status
curl http://localhost:8010/api/v1/status
# Auth endpoints (require Redis)
curl -X POST http://localhost:8010/api/v1/auth/register -H "Content-Type: application/json" -d '{"email":"test@test.com","password":"Test123!","display_name":"Test User"}'
curl -X POST http://localhost:8010/api/v1/auth/login -H "Content-Type: application/json" -d '{"email":"test@test.com","password":"Test123!"}'
# Wallet auth
curl -X POST http://localhost:8010/api/v1/auth/wallet/nonce -H "Content-Type: application/json" -d '{"address":"0x123","chain":"ethereum"}'
```
## Redis Required
The auth endpoints require Redis to be running. Start it with:
```bash
redis-server --port 6379
```
Or configure a different host/port via environment variables:
- `REDIS_HOST` (default: localhost)
- `REDIS_PORT` (default: 6379)
- `REDIS_PASSWORD` (optional)

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"""
RMI Pricing & Subscription Architecture v2
=============================================
INTELLIGENT SCAN ECONOMY — One Scan, Many Uses
-------------------------------------------------
The key insight: When a user scans a token address, that's ONE data event.
But it can power DOZENS of downstream analyses without re-fetching.
Example: User scans token 0xDEAD...BEEF
→ 1 API call fetches: price, liquidity, holders, contract bytecode
→ Powers: risk scan, holder analysis, bubble map, contract audit,
funding trace, social sentiment, whale tracking, cross-chain check
→ All from one scan input, shared across the DataBus cache
This means we can offer SCAN PACKS where one scan credit
actually delivers comprehensive intelligence across ALL our tools,
because the DataBus deduplicates and caches the underlying data.
COMPETITIVE ANALYSIS (June 2026)
---------------------------------
| Platform | Free Tier | Pro Tier | Enterprise |
|-----------------|-----------------------|--------------------|--------------------|
| GoPlus Security | 150K CU/mo | $199/mo (6M CU) | $799/mo (37.5M CU)|
| Arkham | Limited views | $99/mo | $999/mo |
| Nansen | Basic dashboard | $150/mo (Vital) | $1,000/mo (Onchain)|
| DexScreener | Free basic | — | Custom |
| Bubblemaps | Free V2 | $29/mo pro | B2B custom |
| TokenSniffer | Free basic | $99/mo (SnifferPro)| Custom |
| Honeypot.is | Free basic | — | — |
| Chainalysis KYT | None | — | $50K+/yr |
| TRM Labs | None | — | $30K+/yr |
| De.Fi | Free basic | $19.99/mo | Custom |
| RugCheck | Free token checks | — | — |
KEY INSIGHT: We're the ONLY platform that gives ONE scan = ALL intelligence.
GoPlus charges per CU. Nansen charges per month for limited chains.
Arkham gives entity data but no risk scoring. TokenSniffer gives scores only.
RMI covers 38 chains, 67 data providers, real-time caching, AND risk scoring
in a single scan. That's worth a serious premium.
PRICING TIERS (v2 — REVISED June 2026)
---------------------------------------
FREE TIER (Anonymous / Fingerprint)
- 3 basic scans per day (urlcheck, pulse, token_age)
- 1 market overview per day
- Limited data per scan (summary only, no deep analysis)
- No wallet tracking, no real-time alerts
- Powered-by branding on all outputs
SCOUT PACK — $4.99 (25 scan credits)
- 25 scan credits, each = ONE address scanned
- Each scan unlocks EVERY tool for that address for 24 hours
- Includes: risk scan, holder analysis, bubble map, funding trace,
contract audit, whale tracking, social sentiment, cross-chain
- Smart money queries: 10 per pack
- Market overview: unlimited
- Credits never expire
- PER-SCAN VALUE: $0.20 per scan (competitive with TokenSniffer's $0.01-0.05
per basic scan, but we deliver 10-20x more data per scan)
HUNTER PACK — $14.99 (150 scan credits)
- 150 scan credits, same "one scan = full intelligence" model
- 70% discount vs Scout per scan ($0.10/scan)
- Includes everything in Scout plus:
- Arkham entity intelligence (5 queries)
- Deep SENTINEL forensic scans
- Nansen smart money labels (10 queries)
- Prediction market signals (unlimited)
- Real-time alerts (24h per activation)
- Portfolio dashboard (3 wallets)
WHALE PACK — $49.99 (750 scan credits)
- 750 scan credits ($0.067/scan — bulk rate)
- 85% discount vs Scout per scan
- Includes everything in Hunter plus:
- Unlimited Arkham entity lookups
- Unlimited SENTINEL forensic scans
- Unlimited smart money queries
- 30-day real-time alerts
- Portfolio tracking (25 wallets)
- Priority queue (cache bypass)
- x402 API access for automation
MONTHLY SUBSCRIPTIONS
─────────────────────
SCOUT MONTHLY — $19.99/mo
- 75 scan credits/month (rolls over 1 month)
- All Scout Pack features
- Weekly intelligence digest email
- Community Discord access
HUNTER MONTHLY — $49.99/mo
- 350 scan credits/month (rolls over 1 month)
- All Hunter Pack features
- Daily watchlist alerts
- Priority support
WHALE MONTHLY — $149.99/mo
- 1,500 scan credits/month (rolls over 1 month)
- All Whale Pack features
- Dedicated Telegram alert channel
- Custom webhooks
- API access with higher rate limits
- Account manager
ENTERPRISE — $499/mo (or custom)
- Unlimited scans, all tools, all data
- Full API access (databus.fetch with admin key)
- WebSocket real-time streams
- Custom data pipelines
- White-label options
- Dedicated support & SLA
COMMUNITY DISCOUNT — 50% OFF for CRM / $cryptorugmunch holders
- Verify: Check wallet balance > 0 of CRM (Solana) or
$cryptorugmunch (Base/Zora) at purchase time
- Applied automatically when wallet connected
- Works on ALL tiers (packs and subscriptions)
- CRM Solana: 6pnitzwjumnzsvfyfejf9mijzpc4iuqh1xugfwvdf8wb
- $cryptorugmunch Base: 0x93c4f6f6f8a14a255e78de0273d6490719d8538e17dfcc9b72907df6a0d72bf204
PRICE JUSTIFICATION
────────────────────
Why $4.99 for 25 scans when GoPlus gives 150K calls/mo free?
- GoPlus gives RAW API calls. Most are useless without interpretation.
- Our 1 scan = 15-20 underlying API calls, all aggregated and scored.
- Real value: risk assessment, not raw data. A rug pull warning saves $1K+.
- Users don't buy API calls; they buy protection.
Why $14.99 for 150 scans?
- Cheaper than Nansen ($150/mo) for a serious trader
- More comprehensive than Arkham ($99/mo) for security
- Deep analysis that TokenSniffer can't match
Why $49.99 for 750 scans?
- Active investigators use 20-30 scans/day
- Cheaper per-scan than any competitor at this volume
- Priority access means better data freshness
Why subscriptions?
- Recurring revenue for sustainability
- Lower monthly cost vs. buying packs repeatedly
- Roll-over credits reduce purchase anxiety
WHY NOT CHEAPER?
- $0.99 for 50 scans devalues the intelligence. Our free tier already
gives 3 scans/day. The paid product must feel like a significant step up.
- Crypto security is a serious business. Users spending $500-5K on a rug
pull want serious tools, not dollar-store pricing.
- The 50% community discount already gives holders $2.50/25 or $7.50/150
scans — aggressive discount without cheapening the brand.
IMPLEMENTATION
──────────────
Scan credit tracking: Redis key x402:scan_credits:{wallet}
Community discount: Check wallet balance of CRM/$cryptorugmunch tokens
Pack purchase: x402 payment (USDC on Base or SOL)
Credit deduction: On first API call per unique address per 24h window
Address reuse: Same address within 24h = no additional credit deduction
x402 Tool Pricing (per-call, no pack):
- urlcheck: Free (loss leader)
- pulse: Free (loss leader)
- risk_scan: $0.05
- holder_analysis: $0.08
- bubble_map: $0.10
- contract_audit: $0.15
- funding_trace: $0.08
- whale_watch: $0.12
- sentiment: $0.05
- cross_chain: $0.08
- arkham_entity: $0.20
- sentinel_deep: $0.25
Pack scanning: 1 credit = all above tools for 1 address for 24h
→ Per-credit value: $1.00+ of individual tool calls
→ Effective per-scan price: $0.067-$0.20 depending on pack size
"""

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PROPRIETARY SOFTWARE REGISTRATION
================================
Software: Rug Munch Intelligence (RMI) Platform
Including: RMI Backend, RMI Frontend, RugCharts, RugMaps,
RugMuncher Telegram Bot, x402 Protocol Gateways,
Rug Munch Intelligence MCP Server
Owner: Rug Munch Media LLC
Contact: biz@rugmunch.io
Website: https://rugmunch.io
Copyright: (c) 2026 Rug Munch Media LLC. All Rights Reserved.
Type: Proprietary Commercial Software
NOT open-source. NOT free software. NOT MIT, GPL, Apache,
or any other open-source license.
Registration: This software is the confidential trade secret and
proprietary intellectual property of Rug Munch Media LLC.
All source code, algorithms, data collection methods,
detection heuristics, scam pattern databases, API designs,
and architectural decisions are protected trade secrets.
The public repositories (rugcharts, rugmaps, x402-*, mcp)
contain ONLY interface specifications and documentation.
NO implementation details, data collection methods, API
keys, or proprietary algorithms are exposed.
Core detection engines, data pipelines, and backend
implementations are maintained in PRIVATE repositories.
Rights: No rights granted. No license implied. All use, copying,
modification, distribution, or derivative works require
explicit written permission from Rug Munch Media LLC.
Enforcement: Unauthorized use will be pursued to the fullest extent
of applicable law including trade secret protection,
copyright infringement, and DMCA takedown.
Violations may be reported to: legal@rugmunch.io
Governing Law: United States of America
Date: May 22, 2026
---
Rug Munch Media LLC — Proprietary & Confidential

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# RMI RAG Modernization — 2026 Standards
# ======================================
# Design document for upgrading RMI's RAG system to production-grade
# modern standards. Based on audit of all 40+ endpoints, 4 pipelines,
# 9 collections, and 3 embedders.
## Current State Audit
### Collections (crypto_embeddings.py)
wallet_profiles, token_analysis, scam_patterns, forensic_reports,
market_intel, contract_audits, known_scams, news_articles,
transaction_patterns
### Embedders (INCONSISTENT — 3 different models)
- nomic-embed-text (768d) — rag_engine.py, smart_ai_engine.py
- bge-m3 (1024d) — rag_ingestion.py, rag_supreme.py
- bge-small-en-v1.5 (384d) — crypto_embeddings.py (primary)
### Pipelines (4 separate, overlapping)
- rag_engine.py — Qdrant REST API, nomic-embed-text, 5 collections
- rag_service.py — FAISS ANN, bge-small, 9 collections, 3-pillar search
- rag_supreme.py — 15-win pipeline, bge-m3, 5 Qdrant collections
- rag_firehose.py — continuous ingestion engine (designed, not fully wired)
### Gaps Identified
1. NO historical scam ingestion (Rekt DB, Chainabuse, DeFi hacks)
2. NO structured chunking — raw text embedding, no overlap
3. NO evaluation running (RAGAS mentioned, not active)
4. Embedding model inconsistency across pipelines
5. Firehose sources not wired (cadences defined, fetchers missing)
6. NO query transformation in production path
7. NO feedback loop active
8. Redis SCARD bug (FIXED 2026-06-17)
9. FAISS disk indexes exist but Redis backing data evicted for 7/9 collections
## Modern Standards (2025-2026 Industry Consensus)
### 1. Chunking Strategy
- DEFAULT: Recursive character splitting, 512 tokens, 15% overlap
- For code: add class/function boundary separators
- For news: sentence-based chunking preserves coherence
- For scam reports: semantic chunking on topic boundaries
- Overlap: 10-20% (test for your domain — some studies show no benefit)
### 2. Embedding Models
- STANDARDIZE on bge-m3 (1024d) — best open-source, multilingual
- Fallback: bge-small-en-v1.5 (384d) for fast/local
- Multi-head: different dims for different content types
- Contract code: 128d structural features (already in crypto_embeddings.py)
- Scam patterns: 384d behavioral embedding
- News/articles: 1024d semantic (bge-m3)
- Wallet profiles: 64d behavioral fingerprint
### 3. Retrieval Architecture
- HYBRID: Dense (70%) + BM25/Sparse (30%) — 5-15% recall improvement
- RRF fusion (Reciprocal Rank Fusion) — proven best for hybrid
- Cross-encoder rerank: top-20 → rerank → top-5
- MMR dedup: remove near-duplicate results
- Query expansion: generate 3 variants, fuse results
### 4. Ingestion Pipeline (UNIFIED)
- SINGLE entry point: POST /api/v1/rag/ingest
- Pipeline: Parse → Chunk → Dedup → Classify → Embed → Store → Index
- Dedup: content hash in Redis (MD5 of normalized text)
- Quality filter: skip docs below quality threshold
- Rate limiting: per-collection docs/minute
- Batch embedding: groups of 25-50, async
### 5. Historical Data Sources (NEW)
- Rekt DB (de.fi/rekt-database) — 3,000+ DeFi hacks since 2020
- Chainabuse — scam reports with addresses
- TRM Labs Crypto Crime Report — annual typologies
- Elliptic State of Crypto Scams — annual report
- Chainalysis Crypto Crime Report — annual trends
- SlowMist Hacked Archive — detailed exploit analysis
- Immunefi Bug Bounty Reports — vulnerability patterns
- CertiK Audit Findings — smart contract vulnerabilities
- Solana Compromised Accounts — known drained wallets
- Etherscan Labels — 115K+ labeled addresses (already have)
### 6. Evaluation Framework
- RAGAS metrics: faithfulness, answer_relevancy, context_precision, context_recall
- Golden test set: 50 known scam queries with expected answers
- Run weekly, alert on regression
- Track: Hit@5, MRR, NDCG@10
### 7. Feedback Loop
- Scanner hits → boost source weight
- False positives → penalize
- User corrections → update embeddings
- Track helpful docs, boost in future searches
## Implementation Plan
### Phase 1: Standardize & Consolidate (NOW)
1. Standardize embedder: bge-m3 (1024d) primary, bge-small (384d) fallback
2. Add recursive chunking to ingest pipeline
3. Wire firehose sources (Rekt DB, Chainabuse, Etherscan labels)
4. Add content hash dedup to all ingestion paths
### Phase 2: Historical Data Ingestion (THIS WEEK)
5. Build Rekt DB scraper → forensic_reports collection
6. Build Chainabuse scraper → known_scams collection
7. Ingest TRM/Elliptic/Chainalysis annual reports → market_intel
8. Ingest SlowMist/Immunefi/CertiK findings → contract_audits
### Phase 3: Evaluation & Feedback (NEXT WEEK)
9. Activate RAGAS evaluation pipeline
10. Build golden test set (50 queries)
11. Wire feedback loop (scanner hits → boost)
12. Add query transformation (HyDE, expansion)
### Phase 4: Advanced Retrieval (ONGOING)
13. Cross-encoder reranking (bge-reranker-v2-m3)
14. Parent-child retrieval for long documents
15. Multi-modal: code + text + transaction patterns
16. Streaming response for agentic investigation
## New Unified Ingestion Pipeline
```
POST /api/v1/rag/ingest
{
"documents": [...],
"collection": "known_scams",
"source": "rekt_db",
"chunking": "recursive" // or "semantic", "sentence", "none"
}
Pipeline:
1. PARSE — extract text, metadata, entities
2. CHUNK — recursive split (512 tokens, 15% overlap)
3. DEDUP — MD5 hash check against Redis
4. QUALITY — score content, skip if < threshold
5. CLASSIFY — route to correct collection
6. EMBED — batch embed via bge-m3 (Ollama)
7. STORE — Redis (hot) + FAISS (index) + R2 (cold)
8. INDEX — update ANN index version
```
## New Collections to Add
| Collection | Source | Dims | Purpose |
|-----------|--------|------|---------|
| defi_hacks | Rekt DB, SlowMist | 1024d | Historical DeFi exploits |
| rug_timeline | Chainabuse, SENTINEL | 1024d | Rug pull chronology |
| vuln_patterns | Immunefi, CertiK | 1024d | Smart contract vulnerabilities |
| crime_reports | TRM, Elliptic, Chainalysis | 1024d | Annual crime typologies |
| compromised_wallets | Solana, Etherscan | 384d | Known drained addresses |
| exploit_techniques | All sources | 1024d | How hacks were executed |
## Success Metrics
- RAG total_docs: 2,473 → 50,000+ (20x)
- Collections with data: 2/9 → 9/9 + 6 new
- Embedding consistency: 3 models → 1 primary + 1 fallback
- Ingestion cadence: ad-hoc → continuous (firehose)
- Evaluation: none → weekly RAGAS
- Chunking: none → recursive 512-token
- Dedup: none → content hash
- Cold storage: partial → full R2 permanence

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# RAG R2 Storage — Setup Required
## One-time Cloudflare setup:
1. Create R2 bucket "rmi-rag-storage" in Cloudflare dashboard
2. Generate R2 API token with Object Read & Write permissions
3. Set environment variables:
- R2_ACCESS_KEY (the Access Key ID from R2 token)
- R2_SECRET_KEY (the Secret Access Key from R2 token)
## Architecture (already deployed):
Hot → Redis (in-memory, fast queries, always available)
Warm → Local /data/rag-storage (7-day cache, auto-cleaned)
Cold → Cloudflare R2 (permanent, 10GB free, zero egress)
## Endpoints (all working, all bypass write middleware):
POST /api/v1/rag/permanence/snapshot → Save all collections to R2
POST /api/v1/rag/permanence/restore → Pull latest from R2 into Redis
POST /api/v1/rag/permanence/nightly → Full cycle: snapshot→R2, clean local, rebuild ANN
GET /api/v1/rag/permanence/stats → R2 usage + local cache stats
## Cron (active):
cd0f23b963f2 — runs nightly at 3 AM UTC — full RAG persistence cycle

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# RMI — Rug Munch Intelligence
> **The first open-source crypto intelligence platform.**
> Bloomberg-grade security and market intelligence for the on-chain world.
---
## About Rug Munch Media LLC
We are **Rug Munch Media LLC**, a Wyoming-based software company building open-source tools that make the crypto economy safer — for consumers, builders, and institutions.
Crypto moves fast, and bad actors move faster. Phishing sites clone trusted brands within hours of a certificate being issued. Token launches rug in seconds. Wallets drain before a user knows what happened. The existing security stack is fragmented, expensive, and gated behind paywalls. **We are building the open alternative.**
RMI (Rug Munch Intelligence) is the **first open-source crypto intelligence platform** — a unified, AI-powered command center for token risk analysis, wallet forensics, multi-chain market data, phishing detection, and regulatory-grade intelligence. Our ambition is to become the **Bloomberg terminal of crypto**: one trusted surface where the entire industry can see what is happening, what is risky, and what is real.
**Who we serve:**
- **Consumers** — real-time protection against rug pulls, phishing, and wallet drainers
- **Industry** — exchanges, custodians, compliance teams, and researchers who need defensible on-chain intelligence
- **AI agents** — first-class Model Context Protocol (MCP) integration and x402 micropayments for autonomous tooling
We are an open-source company. Our code, our data, and our threat intelligence are public. Our business model is delivering the managed product, the API, and the institutional tier — not locking away the safety net.
---
## What RMI does
### Threat detection
- **Token scanning** — real-time rug-pull, honeypot, and scam detection across 18+ EVM chains + Solana
- **Wallet forensics** — track wallet behavior and transaction patterns, flag known bad actors
- **Phishing detection** — Certificate Transparency monitoring catches phishing domains within hours of registration, before they go live
- **Bayesian reputation** — statistically grounded deployer trust scoring, not vibes
### Market intelligence
- **2,500+ assets** — full coverage across major chains
- **News aggregation** — 1,800+ sources with clustering, dedup, and sentiment
- **Whale alerts** — large wallet movements and trading patterns in real time
- **Multi-chain market data** — OHLCV, liquidity, and volume from a single API
### AI-native platform
- **RAG-grounded reports** — every claim cited, no hallucination
- **MCP server** — drop RMI into any AI agent or LLM workflow
- **x402 micropayments** — pay-per-call pricing for autonomous agents
- **Open weights and open data** — no vendor lock-in
---
## Architecture
RMI is a single FastAPI backend with modular domain services:
```
Backend (FastAPI + Python)
├── DataBus — single data plane, 18 chains, 4-layer defense (labels → scanner → RAG → prices)
├── SENTINEL — multi-chain token security scanner
├── Wallet Bank — 39M+ labeled wallets, federated from open sources
├── RAG Engine — 3-pillar hybrid retrieval (dense + sparse + entity)
├── x402 Gateway — AI-agent micropayments
├── MCP Server — 221 tools for AI agents
└── Observability — Prometheus, GlitchTip, OpenTelemetry
```
Storage: PostgreSQL, Redis, Neo4j, Qdrant, ClickHouse, DuckDB.
---
## Repositories
| Platform | URL |
|----------|-----|
| 🐙 GitHub (canonical) | https://github.com/Rug-Munch-Media-LLC/rugmuncher-backend |
| 🦊 GitLab (mirror) | https://gitlab.com/cryptorugmuncher/rugmuncher-backend |
| 🤗 HuggingFace (canonical) | https://huggingface.co/cryptorugmuncher/rugmuncher-backend |
---
## Links
- 🌐 **Website**: https://rugmunch.io
- 📧 **Contact**: info@rugmunch.io
- 💬 **Telegram**: https://t.me/cryptorugmuncher
- 🐦 **X / Twitter**: https://x.com/cryptorugmunch
- 🐙 **GitHub org**: https://github.com/Rug-Munch-Media-LLC
- 🦊 **GitLab**: https://gitlab.com/cryptorugmuncher
- 🤗 **HuggingFace org**: https://huggingface.co/cryptorugmuncher
---
## License
**MIT** — see `LICENSE.md`.
RMI is open source because the safety of the crypto economy cannot depend on a single vendor. We compete on the quality of the product, not on who is allowed to see the threat.
---
**Built by Rug Munch Media LLC · Wyoming, USA**
*Open-source crypto intelligence. AI-native. Built to last.*

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---
tags:
- rugmunch
- rmi
- crypto
- blockchain
- intelligence
- security
- scam-detection
- web3
- defi
- mcp
- fastapi
- open-source
- multi-chain
- wallet-forensics
- token-scanner
library_name: fastapi
---
# 🥬 RMI (Rug Munch Intelligence)
**Open-Source Real-Time Crypto Intelligence Platform**
*The Bloomberg Terminal of Crypto Security*
## Overview
RMI is the first open-source crypto intelligence platform, delivering real-time token scanning, wallet forensics, multi-chain market data, and scam detection across **96 blockchains**.
## Key Features
### 🔍 Threat Detection
- **Token Scanning**: Real-time rugpull, honeypot, and scam detection
- **Wallet Forensics**: Track wallet behavior and transaction patterns
- **Risk Scoring**: AI-powered threat classification (0-30 safe, 30-70 warning, 70+ danger)
### 📊 Market Intelligence
- **2500+ Assets**: Full market coverage across major chains (ETH, SOL, BTC, TRX)
- **Real-Time Data**: OHLCV, volume analytics, liquidity tracking
- **News Aggregation**: 1800+ sources with real-time sentiment analysis
### 🛠️ Developer Tools
- **MCP Server**: Model Context Protocol integration for AI agents
- **x402 Marketplace**: Micropayment gateway for premium tools
- **REST API**: FastAPI-powered endpoints with full documentation
- **WebSocket Support**: Live price and alert streaming
### 🔐 Infrastructure
- **96 Chains Supported**: Multi-chain architecture with extensible provider system
- **Federated Label System**: 2.7M+ wallet addresses tagged from multiple sources
- **Graph Database**: Neo4j-powered relationship tracking
- **Vector Search**: Qdrant-based semantic intelligence retrieval
## Tech Stack
- **Backend**: FastAPI + Python 3.12 + Redis + PostgreSQL + Neo4j + Qdrant + ClickHouse
- **Deployment**: Docker, Cloudflare Workers
- **Chains**: ETH, SOL, BTC, TRX, BSC, and 91+ more
## Links
| Platform | URL |
|----------|-----|
| 🌐 Website | https://rugmunch.io |
| 💬 Telegram Group | https://t.me/cryptorugmuncher |
| 🐦 Twitter/X | https://x.com/cryptorugmunch |
| 📱 Personal Telegram | @cryptorugmunch |
| 📧 Email | info@rugmunch.io |
## License
MIT License
---
Built by [Rug Munch Media](https://rugmunch.io) • Open Source Crypto Intelligence

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# Rug Munch Intelligence (RMI) — System Map & Build Status
## LIVE SYSTEM OVERVIEW
```
┌─────────────────────────────────────────────────────────┐
│ FRONTEND (React) │
│ /root/frontend/ — 20 pages, dist/index.html │
│ RugMaps, RugCharts, Alerts, Markets, News, │
│ Intelligence, Investigation, ScamSchool, MCP Docs │
└────────────────────┬────────────────────────────────────┘
│ Supabase + REST API
┌────────────────────▼────────────────────────────────────┐
│ BACKEND (FastAPI) │
│ /root/backend/ — 379 endpoints, 6 routers │
│ Docker: rmi_backend (volume-mounted /app/app) │
│ 66 backend modules, 8 data connectors │
│ │
│ WALLET-CLUSTERING ROUTER (14 endpoints) │
│ POST /contract-scan → holders → clusters → bundles │
│ POST /cluster/detect → 7-method detection │
│ POST /cluster/analyze → behavioral fingerprinting │
│ GET /health → cache + GNN + spam stats │
│ │
│ FORENSICS ROUTER (12 endpoints) │
│ POST /threat-check → CryptoScamDB + GoPlus + Januus │
│ POST /deep-scan → full wallet forensics │
│ POST /cross-chain → multi-chain correlation │
│ │
│ RUGMAPS ROUTER (8 endpoints) │
│ GET /analyze/{address} → bubble map generation │
│ GET /health │
│ │
│ CROSS-TOKEN ROUTER (8 endpoints) │
│ GET /connections/{wallet} → cross-project links │
│ │
│ DISCOVERY ROUTER (8 endpoints) │
│ GET /tokens → new token discovery │
│ │
│ X402 TOOLS ROUTER (142 endpoints) │
└────────────────────┬────────────────────────────────────┘
│
┌──────────────┼──────────────┐
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────────┐
│ Helius x3 │ │QuickNode │ │ DexScreener │
│ (primary) │ │(fallback)│ │ (free tier) │
└──────────┘ └──────────┘ └──────────────┘
▼ ▼ ▼
┌─────────────────────────────────────────────────────────┐
│ UNIFIED PROVIDER (7-source cascade) │
│ Helius → Birdeye → Solscan → GMGN → DexScreener → │
│ QuickNode → Blockchair Rate-limited 5 req/sec │
└─────────────────────────────────────────────────────────┘
```
## DATA SOURCES (18 API Keys)
| Source | Key File | Purpose | Status |
|---|---|---|---|
| Helius x3 | helius_api_key, _2, _3 | Solana RPC primary | LIVE |
| QuickNode | quicknode_api_key | Solana RPC fallback | LIVE |
| Birdeye | birdeye_api_key | Token data, whale tracking | LIVE |
| GMGN | gmgn_api_key | Token discovery | LIVE |
| Moralis | moralis_api_key | Multi-chain EVM data | CONFIGURED |
| Arkham | arkham_api_key | Entity labeling | CONFIGURED |
| CoinGecko | coingecko_api_key | Price data | CONFIGURED |
| Dune | dune_api_key | SQL queries on-chain | CONFIGURED |
| Nansen | nansen_api_key | Smart money tracking | CONFIGURED |
| Solscan | solscan_api_key | Solana transaction data | CONFIGURED |
| NVIDIA | nvidia_api_key, dev_api_key | AI inference | CONFIGURED |
| OpenRouter | openrouter_api_key | LLM routing | CONFIGURED |
| Groq | groq_api_key | Fast LLM inference | CONFIGURED |
| SiliconFlow | siliconflow_api_key, _2 | LLM inference | CONFIGURED |
| Kimi | kimi_api_key | LLM (Moonshot) | CONFIGURED |
| Mistral | mistral_api_key | LLM inference | CONFIGURED |
| Gemini | gemini_api_key | Google AI | CONFIGURED |
| HuggingFace | huggingface_token | Model downloads | CONFIGURED |
| Cloudflare | cloudflare_api_token | Workers, DNS | LIVE |
| Telegram | telegram_bot_token | Bot integration | LIVE |
## DETECTION PIPELINE
```
Token/Address Input
│
▼
┌──────────────────┐
│ ENTITY REGISTRY │ ← 50+ CEX/DeFi/Mixer addresses
│ filter_infra() │ ← 100K+ Solana labels from CSV
└────────┬─────────┘
▼
┌──────────────────┐
│ SPAM REGISTRY │ ← 2,530 Scam Sniffer addresses
│ check_token() │ ← GoldRush 8M spam tokens (6 chains)
└────────┬─────────┘ ← OpenSanctions OFAC, Guardian phishing
▼
┌──────────────────┐
│ HOLDER ANALYSIS │ ← Helius getProgramAccounts (228K JTO)
│ (unified_provider)│ ← Multi-source fallback cascade
└────────┬─────────┘
▼
┌──────────────────┐
│ BUNDLE DETECTION │ 5 signals:
│ (bundle_detector)│ ← atomic_block, common_funder, temporal,
└────────┬─────────┘ ← distribution_anomaly, concentration
▼
┌──────────────────┐
│ CLUSTER DETECTION│ 7 methods:
│ (wallet_clustering)│ ← temporal, counterparty, behavioral,
└────────┬─────────┘ ← funding, pattern, ML similarity, sleeper
▼
┌──────────────────┐
│ GNN FRAUD SCORE │ ← Random Forest fallback (CPU-only)
│ (fraud_gnn) │ ← HuggingFace sklearn (gated, not loaded)
└────────┬─────────┘
▼
┌──────────────────┐
│ THREAT INTEL │ ← CryptoScamDB (MIT, free)
│ (threat_feeds) │ ← GoPlus Security (free tier)
└────────┬─────────┘ ← Januus risk scores (open-source)
▼
┌──────────────────┐
│ CROSS-CHAIN │ ← Behavioral fingerprinting
│ (correlator) │ ← CEX deposit pattern matching
└────────┬─────────┘ ← Union-find entity grouping
▼
RISK SCORE OUTPUT
```
## LOCAL DATA FILES
| File | Lines | Purpose |
|---|---|---|
| wallet-labels/solana_cex_labels.csv | 100,001 | All known CEX hot wallets on Solana |
| wallet-labels/solana_defi_labels.csv | 1,481 | DeFi protocol addresses (Jupiter, Raydium, etc.) |
| wallet-labels/solana_dapp_labels.csv | 1,791 | Dapp addresses |
| wallet-labels/etherscan_malicious_labels.csv | 7,781 | Etherscan-flagged malicious contracts |
| wallet-labels/malicious_smart_contracts.csv | 754 | Additional malicious contracts |
| wallet-labels/ofac_sanctions.json | 0 | (Empty - needs seeding) |
| spam/scamsniffer_blacklist.json | 2,531 | Scam Sniffer address blacklist |
| SOSANA-CRM-2024.json | 101,916 | Full CRM data dump |
| wallet_database.json | 364 | Wallet profiles DB |
| rmi.db | 9 | SQLite state |
## SUPABASE TABLES (8 tables)
- profiles — user profiles
- wallet_labels — labeled wallet data
- token_analysis — token analysis results
- scam_reports — scam report submissions
- alerts — alert configurations
- market_intel — market intelligence cache
- news — news article cache
- forensic_reports — forensic analysis results
## INFRASTRUCTURE
| Service | Container | Status | Purpose |
|---|---|---|---|
| Backend API | rmi_backend | UP (healthy) | FastAPI, 379 endpoints |
| n8n Automation | rmi_n8n | UP | Workflow automation |
| Worker | rmi_worker | UP (healthy) | Background job processing |
| Telegram Bot | telegram-mcp | UP | Telegram bot integration |
| Listmonk | rmi-listmonk | UP | Email newsletters |
| Dragonfly (Redis) | rmi_dragonfly | UP (healthy) | Caching |
| Cloudflare Worker | rmi_cloudflare | UP | CF tunnel/edge |
| Ghost CMS | rmi-ghost | UP | Blog/content |
| MySQL | rmi-mysql | UP | Database |
| Langfuse | langfuse stack | UP | LLM observability |
| CF Edge Worker | rag.rugmunch.io | LIVE | RAG caching |
## WHAT'S WORKING (VERIFIED)
- [x] Helius RPC — 228K JTO holders detected via getProgramAccounts
- [x] Multi-source cascade — Helius → QuickNode → DexScreener fallback
- [x] Entity Registry — Binance/Uniswap/Tornado correctly identified
- [x] Bundle Detection — JTO = 0.09 confidence (correctly low)
- [x] Spam Registry — 2,530 Scam Sniffer addresses loaded
- [x] GNN Scoring — Random Forest fallback active (HuggingFace model gated)
- [x] Threat Feeds — GoPlus + CryptoScamDB + Januus integrated
- [x] All 5 health endpoints returning "ok"
- [x] All 10 core modules importing clean
- [x] RAG Edge Worker at rag.rugmunch.io returning health ok
- [x] n8n running with database
- [x] Telegram bot connected to Telegram servers
## WHAT'S BROKEN / NEEDS WORK
### Critical
- [ ] **RAG collections empty** — wallet_profiles, scam_patterns, forensic_reports all 0 docs. n8n needs to feed these. Only news_articles has 4 docs.
- [ ] **OFAC sanctions empty** — wallet-labels/ofac_sanctions.json is 0 lines. Needs seeding from opensanctions.org
- [ ] **563 uncommitted files** — backend has substantial uncommitted changes (Dockerfile, x402, entity_labeler, portfolio_tracker, etc.)
- [ ] **Frontend only has index.html** — 20 page source files exist but dist/ only has index.html. Other pages (docs, pricing, tools, x402) were deleted.
### High Priority
- [ ] **HuggingFace model gated** — fraud_gnn.py falls back to heuristic Random Forest because the sklearn model requires auth. Need to either get access or train a proper model.
- [ ] **n8n workflows not queryable** — 6 workflows claimed but API returned empty. Need to verify they're running.
- [ ] **CryptoGuard/GoPlus integration testing** — threat_feeds.py has the code but needs live testing with known scam addresses.
- [ ] **Entity labeler refactoring** — entity_labeler.py has 1084 lines of changes uncommitted, needs cleanup.
- [ ] **Exchange flow analyzer** — exchange_flow_analyzer.py reworked but uncommitted.
### Medium Priority
- [ ] **Frontend build pipeline** — Need to build and deploy the React frontend properly with all 20 pages.
- [ ] **Telegram bot features** — Bot is connected but needs command handlers for RMI features (scan, alert, etc.)
- [ ] **Email alerts** — Listmonk is running but not wired to RMI alert system.
- [ ] **CF Worker source** — rag.rugmunch.io is live but worker source code not in repo.
- [ ] **DexScreener connector** — Listed in unified_provider but not tested in cascade.
- [ ] **Blockchair connector** — Exists but not wired into unified_provider cascade.
- [ ] **EVM connector** — File exists but not tested against real EVM chains.
### Low Priority / Nice-to-Have
- [ ] **x402 payment system** — 142+ endpoints in x402_tools but uncommitted changes.
- [ ] **GNN model training** — Train a local sklearn model on known fraud data instead of HF gated model.
- [ ] **Cross-chain EVM testing** — cross_chain_correlator has Ethereum CEX addresses but Solana-only testing.
- [ ] **Mempool sentinel** — mempool_sentinel.py exists but unclear if active.
- [ ] **Wallet monitor** — wallet_monitor.py exists but not connected to alerts.
## BUILD PLAN — NEXT STEPS
### Phase 1: Stabilize & Commit (Day 1)
1. Commit all 563 uncommitted backend files
2. Seed RAG collections (wallet profiles from labels, known scam patterns)
3. Seed OFAC sanctions data from OpenSanctions
4. Test all threat feeds end-to-end with known scam addresses
### Phase 2: Frontend & Bot (Day 2-3)
5. Build frontend properly — `npm run build` in /root/frontend
6. Wire Telegram bot commands: /scan, /alert, /watch, /status
7. Deploy frontend to CF Pages or VPS
### Phase 3: Data Pipeline (Day 3-4)
8. Wire n8n workflows to feed RAG collections continuously
9. Set up scheduled GoldRush spam token sync (6 chains)
10. Set up OpenSanctions daily sync
11. Set up Scam Sniffer blacklist auto-update
### Phase 4: Testing & Hardening (Day 4-5)
12. End-to-end test with known scam tokens (not just JTO)
13. EVM chain testing with Ethereum addresses
14. Load testing on /contract-scan endpoint
15. Documentation: API docs, setup guide, architecture diagram
### Phase 5: Production (Day 5+)
16. Set up GitHub Actions CI/CD
17. Auto-deploy on merge to main
18. Monitoring (Langfuse, uptime checks)
19. Rate limiting on public endpoints
20. Authentication on sensitive endpoints
## KEY FILES QUICK REFERENCE
```
/root/backend/app/
├── chain_client.py # Rate-limited Solana RPC (Helius + QuickNode)
├── chain_cache.py # LRU cache with TTL (500 entries)
├── chain_feeder.py # Wallet TX feeding into clustering engine
├── unified_provider.py # 7-source data cascade
├── bundle_detector.py # 5-signal bundle detection
├── entity_registry.py # 50+ CEX/DeFi/Mixer address exclusion
├── threat_feeds.py # CryptoScamDB + GoPlus + Januus
├── fraud_gnn.py # Random Forest fraud scoring (CPU-only)
├── spam_registry.py # 2,530 scam addresses + GoldRush integration
├── cross_chain_correlator.py # Multi-chain entity resolution
├── wallet_clustering.py # 7-method clustering engine
├── cluster_detection.py # Cluster detection orchestrator
├── bubble_maps.py # RugMaps visualization engine
├── token_discovery.py # New token scanning
├── rag_service.py # RAG query service
├── routers/
│ ├── wallet_clustering_router.py # 14 endpoints
│ ├── forensics_router.py # 12 endpoints
│ ├── bubble_maps_router.py # 8 endpoints
│ ├── cross_token_router.py # 8 endpoints
│ ├── discovery_router.py # 8 endpoints
│ └── ... (admin, chat, x402, etc.)
├── data/
│ ├── spam/scamsniffer_blacklist.json # 2,531 lines
│ ├── wallet-labels/ # 100K+ Solana labels
│ └── SOSANA-CRM-2024.json # Full CRM dump
```

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# Security Policy
## Supported Versions
| Version | Supported |
| ------- | ------------------ |
| 2.x | ✅ Active support |
| 1.x | ❌ End of life |
## Reporting a Vulnerability
**DO NOT OPEN A PUBLIC ISSUE.** This is a commercial security product. Vulnerabilities in our code directly affect our customers' safety.
**Email:** security@rugmunch.io
**PGP Key:** [Available on request]
**Response time:** Within 24 hours
**Disclosure:** Coordinated disclosure after fix deployment (max 90 days)
### What to include:
- Type of vulnerability (RCE, auth bypass, data exposure, etc.)
- Affected endpoint/component
- Steps to reproduce
- Proof of concept (if available)
- Impact assessment
### What you'll receive:
- Confirmation within 24 hours
- Regular status updates
- Credit in release notes (unless you request anonymity)
- Bug bounty at our discretion (contact us for current program details)
## Security Best Practices for Contributors
1. **Never commit secrets** — API keys, tokens, passwords, private keys go in environment variables only
2. **Use `.env` (gitignored)** for local development credentials
3. **Sign your commits** with GPG (`git config commit.gpgsign true`)
4. **Review your own diffs** before pushing — check for accidental credential exposure
5. **Use branch protection** — all changes to main must go through PR review
6. **Run `git-sync.py --dry-run`** before pushing to verify no secrets are staged
## Our Security Stack
- Pre-commit hooks scan every staged file for secrets
- Pre-push hooks block force pushes and re-scan for secrets
- GitHub Actions CI runs secret scanning on every PR
- Dependabot monitors dependencies for known CVEs
- Production secrets stored in GitHub Secrets vault + environment variables
- Backend .env never committed (in .gitignore)

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# RugMunch Intelligence — Security Stack Summary
# Generated: 2026-05-08
# WARNING: This file describes the security tooling deployed on this host.
# Do NOT share externally — contains system architecture details.
# Access: chmod 600, root-only.
══════════════════════════════════════════════════════════════════
INSTALLED OPEN-SOURCE SECURITY TOOLS
══════════════════════════════════════════════════════════════════
SAST (Static Analysis):
bandit 1.9.4 Python security linter → pipx install bandit
semgrep 1.162.0 Cross-language static analysis → pipx install semgrep
Secret Detection:
gitleaks 8.25.1 Git + filesystem secret scanning → binary download
Dependency Scan:
pip-audit 2.10.0 PyPI vulnerability audit → pipx install pip-audit
Container Scanning:
trivy 0.70.0 Container + filesystem + secrets → binary download
IPS / WAF:
crowdsec 1.7.7 Collaborative intrusion detection → apt install
fail2ban active IP-based brute-force blocker → apt install
Pre-commit:
pre-commit 4.6.0 Git hook automation → pipx install pre-commit
══════════════════════════════════════════════════════════════════
NEW FILES CREATED
══════════════════════════════════════════════════════════════════
/srv/rmi/backend/.bandit.yaml Bandit config (excludes B105/B311 false-posit, dirs)
/srv/rmi/backend/.gitleaks.toml Gitleaks allowlist (public SOL addresses + static/)
/srv/rmi/backend/.trivyignore Trivy ignore (investigation evidence files)
/srv/rmi/backend/.pre-commit-config.yaml Pre-commit: bandit + gitleaks + isort + black + pip-audit
/srv/rmi/backend/run-security.sh Full security suite runner
/srv/rmi/backend/tmp/ fail2ban templates + GitHub Actions template
══════════════════════════════════════════════════════════════════
CODE FIXES APPLIED
══════════════════════════════════════════════════════════════════
DOCKERFILE:
- FROM python:3.12-slim (was 3.11)
- Added non-root `rmi` user + USER rmi
- Upgraded known-vulnerable packages: jaraco.context + wheel
CODE (md5 → sha256 - CWE-327):
app/fallback_engine.py:83 Cache key hash
app/routers/news_feed.py:237 Article deduplication hash
app/routers/rugmaps.py:462 Token cluster seed
app/routers/social.py:435 Like hash
app/rugmaps_analyzer.py:99 Analyzer seed
BUG FIXES:
app/routers/daily_briefing.py:280 Fixed unterminated string literal syntax error
══════════════════════════════════════════════════════════════════
SCAN RESULTS (latest run)
══════════════════════════════════════════════════════════════════
Bandit: 0 HIGH, 42 MEDIUM (excludes B105 false-positives)
Semgrep: 4 findings (2 INFO + 2 WARNING — all in x402-gateway/*.ts, not backend)
pip-audit: 0 known dependency vulnerabilities
Gitleaks: 0 leaks (after allowlist for public SOL addresses + dist/)
Trivy fs: 0 HIGH/CRITICAL (after .trivyignore for investigation evidence)
══════════════════════════════════════════════════════════════════
MANUAL DEPLOY STEPS
══════════════════════════════════════════════════════════════════
Deploy fail2ban API abuse protection:
sudo cp /srv/rmi/backend/tmp/rmi-api.conf /etc/fail2ban/filter.d/rmi-api.conf
sudo cp /srv/rmi/backend/tmp/rmi-api-jail.conf /etc/fail2ban/jail.d/rmi-api.conf
sudo systemctl restart fail2ban
Deploy GitHub Actions when repo hooks up:
mkdir -p .github/workflows
cp /srv/rmi/backend/tmp/github-workflow.yml .github/workflows/security.yml
══════════════════════════════════════════════════════════════════
COMMAND REFERENCE
══════════════════════════════════════════════════════════════════
Quick scan: cd /srv/rmi/backend && ./run-security.sh
Full scan: cd /srv/rmi/backend && ./run-security.sh --full
Bandit only: bandit -r app/ -c .bandit.yaml
Semgrep only: semgrep --config=auto
pip-audit: pip-audit -r requirements.txt
Gitleaks: gitleaks detect --source . --no-git --config .gitleaks.toml
Trivy fs: trivy fs --scanners vuln,secret,misconfig .
Trivy image: trivy image --severity HIGH,CRITICAL rmi-backend:latest
Pre-commit: pre-commit run --all-files
CrowdSec stats: cscli metrics + cscli decisions list
Fail2ban ban: sudo fail2ban-client status rmi-api

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# RMI Development Standards — AI-Forward + Web3 Best Practices
## ⚠️ FIRST: Read /root/DEVELOPERS.md for canonical paths.
---
## BACKEND DEVELOPMENT
### Pre-commit checklist (run before every commit):
```bash
bash /root/backend/scripts/pre-commit.sh
```
Checks: Python syntax, hardcoded secrets, env var consistency, stale path references.
### Environment variables:
```bash
# Auto-generate from Hermes config:
python3 /root/backend/generate_env.py --force
# Then fill in missing values:
nano /root/backend/.env
```
### Live development (no rebuild needed):
```bash
# Volume mount means code changes are instant:
docker restart rmi-backend
# Verify:
curl http://localhost:8000/health
```
### Adding new env vars:
1. Add to code: `os.getenv("MY_VAR")`
2. Add to `/root/backend/.env.example` with comment
3. Run `python3 /root/backend/generate_env.py --force`
4. Add to `/srv/rugmuncher-backend/docker-compose.yml` if container needs it
---
## AI AGENT DEVELOPMENT WORKFLOW
This system is designed for AI-assisted development. Here's the stack:
```
hermes-agent (CLI)
│
├── Terminal tool → docker exec, git, curl, python
├── Web tool → API testing, research
├── File tool → Edit /root/backend/ directly
├── Delegate → Spawn sub-agents for parallel work
└── Cron jobs → Automated tasks
```
### How Hermes develops the backend:
1. **Discover**: Reads AGENTS.md in /root/backend/
2. **Edit**: Patches files directly (volume mount = live)
3. **Test**: `curl localhost:8000/health` after changes
4. **Rebuild**: `docker compose build && docker compose up -d`
5. **Verify**: Checks logs, API responses
### n8n workflow development:
- UI: http://localhost:5678 (admin / RugMuncher2024)
- Direct DB: `sqlite3 /root/n8n-data/database.sqlite`
- Import: Copy workflow JSONs into `/root/n8n-workflows/`
- Test: Check execution history in UI
### Orchestrator swarm:
- API: http://localhost:8081
- Health: `curl http://localhost:8081/health`
- Bots: `curl http://localhost:8081/orchestrator/bots`
- Create task: `POST /orchestrator/task`
---
## WEB3 SECURITY BEST PRACTICES
### Secrets management:
- **NO hardcoded secrets** in any `.py` file
- All secrets in `/root/.secrets/` or `/root/.hermes/.env`
- App passwords preferred over account passwords
- Rotate API keys quarterly
### Key scanning:
```bash
# Run before any commit:
grep -rn '0x[0-9a-fA-F]\{64\}\|sk-[a-zA-Z0-9]\{20,\}' /root/backend/app/ --include='*.py'
```
### RPC security:
- Use dedicated RPC URLs, never public endpoints in production
- Rate limit all on-chain queries
- Cache blockchain data aggressively (Redis)
---
## CODE QUALITY
### Python:
- Type hints on all public functions
- Docstrings for modules and classes
- Async/await for all I/O operations
- Use Pydantic for data models
### TypeScript (Frontend):
- Components in `/srv/rugmuncher-backend/rmi-frontend/src/components/`
- Services in `/srv/rugmuncher-backend/rmi-frontend/src/services/`
- Types shared via `/srv/rugmuncher-backend/rmi-frontend/src/types.ts`
---
## MONITORING
### Health checks:
```bash
# All services:
curl http://localhost:8000/health # Backend
curl http://localhost:8081/health # Orchestrator
curl http://localhost:5678/healthz # n8n
curl http://localhost:9001/api/health # Listmonk
```
### Logs:
```bash
docker logs rmi-backend --tail 50
docker logs rmi-n8n --tail 50
journalctl -u hermes -n 50
```
### Cron jobs:
```bash
# List all:
cronjob action='list'
# Check status of specific job:
cronjob action='list' # look for last_status
```
---
## DEPLOYMENT
### Full stack restart:
```bash
cd /srv/rugmuncher-backend
docker compose down
docker compose up -d
```
### Rebuild with cache clear:
```bash
docker compose build --no-cache backend worker orchestrator
docker compose up -d
```
### Rollback (if something breaks):
```bash
# Restore backup:
cp /root/backups/n8n/$(date +%Y-%m)/database.sqlite /root/n8n-data/
docker restart rmi-n8n
# Rebuild from known-good commit:
cd /root/backend && git checkout <commit-hash>
docker restart rmi-backend
```

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# RMI Supabase Integration — AI-First, Web3-Forward
## Architecture
```
┌─────────────────────────────────────────────────────────┐
│ RMI Backend (FastAPI) │
│ │
│ supabase_router.py supabase_oauth_router.py │
│ supabase_auth_router.py supabase_service.py │
│ supabase_rag.py db_client.py │
└──────────────┬──────────────────────────────────────────┘
│ httpx + service_role key
▼
┌─────────────────────────────────────────────────────────┐
│ Supabase │
│ │
│ ┌───────────┐ ┌───────────┐ ┌───────────────────┐ │
│ │ Auth │ │ Postgres │ │ Row Level │ │
│ │ (JWT+OAuth)│ │ (Database)│ │ Security (RLS) │ │
│ └───────────┘ └───────────┘ └───────────────────┘ │
│ │
│ ┌───────────┐ ┌───────────┐ ┌───────────────────┐ │
│ │ Storage │ │ Edge │ │ Real-time │ │
│ │ (Files) │ │ Functions │ │ Subscriptions │ │
│ └───────────┘ └───────────┘ └───────────────────┘ │
└─────────────────────────────────────────────────────────┘
```
## Key Integration Points
### 1. Authentication (Web3 + Traditional)
- **JWT auth** via `supabase-auth` skill + `auth.py`
- **OAuth providers**: GitHub, Google (configured in `supabase_oauth_router.py`)
- **Wallet auth**: EVM + Solana wallet connection (non-custodial)
- **x402 trial tracking**: Device fingerprint + wallet-based quotas
### 2. Database (Postgres via Supabase)
- **User profiles**: `users` table with premium tiers, notification prefs
- **Intelligence data**: whale_movements, market_trending_tokens, scam_alerts
- **Content**: content posts, comments, upvotes, gamification events
- **x402 payments**: transaction logs, tool usage, trial tracking
- **Retention**: 90-day auto-cleanup for non-security data
### 3. RAG / Vector Store
- Redis-based vector store for crypto intelligence (`rag_service.py`)
- Lightweight SQLite+TF-IDF fallback (`rag_lightweight.py`)
- Collections: wallet_profiles, token_analysis, scam_patterns, forensic_reports, market_intel
- n8n workflow ingests news articles into RAG
### 4. Env Vars Required
```
SUPABASE_URL=https://<project>.supabase.co
SUPABASE_ANON_KEY=eyJh...
SUPABASE_SERVICE_KEY=eyJh...
SUPABASE_JWT_SECRET=...
```
## MCP (Model Context Protocol) Integration
- **`/api/v1/x402/tools-catalog`** — Full MCP catalog of 51 (44 MCP + 7 bundles) tools
- **`app/mcp/x402_mcp_server.py`** — MCP server implementation
- **`app/mcp_router.py`** — Routes MCP tool calls to backend functions
- **GitHub repo**: `Rug-Munch-Media-LLC/rug-munch-intelligence-mcp` (public)
## x402 Payment Protocol
- **`/.well-known/x402`** — Protocol discovery document
- **7 chains**: Solana (Facilitator), Base (Facilitator), ETH/BSC/ARB/OPT/POL (Self-verify)
- **Trial**: 1 free call (no wallet), 3 free calls (with wallet)
- **Payment**: USDC micropayments via HTTP 402
- **Repos**: `x402-gateway-solana`, `x402-gateway-base`, `x402-twitter-view`
## AI-Forward Architecture
```
User Request → Backend API → Orchestrator (9 agents)
│ │
├── Supabase ├── Wallet clustering
├── Redis RAG ├── Scam detection
├── News Agg ├── Threat intel
└── x402 Gate └── Cross-chain analysis
```
## Web3 Best Practices Applied
- **Non-custodial**: No private keys stored server-side
- **RLS**: Row Level Security on all Supabase tables
- **Device fingerprinting**: Anti-abuse for trial system
- **On-chain verification**: Self-verify mode for 5 chains
- **Facilitator mode**: Cloudflare Workers for Base/Solana

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# x402 Protocol — Complete System Architecture
## MUST READ for all future RMI developers
### Auto-audited: May 23, 2026 — 59 tools, 7 chains, all endpoints verified
---
## SYSTEM OVERVIEW
```
INTERNET
│
▼
Cloudflare Tunnel (rmi-cloudflare)
┌─────────────────────────────┐
│ rugmunch.io │
│ mcp.rugmunch.io │
│ n8n.rugmunch.io │
└─────────────┬───────────────┘
│
┌─────────────▼───────────────┐
│ nginx (:80, :443) │
│ Routes: │
│ /api/* → :8000 │
│ /.well-known/* → :8000 │
│ /mcp/* → :8000 │
│ /health → :8000 │
│ / → static │
└─────────────┬───────────────┘
│
┌───────────────────┼───────────────────┐
│ │ │
▼ ▼ ▼
┌─────────┐ ┌──────────┐ ┌──────────┐
│ Backend │ │ Orchestrator│ │ n8n │
│ :8000 │ │ :8081 │ │ :5678 │
│ 59 tools│ │ 9 agents │ │ 2 flows │
└────┬────┘ └──────────┘ └──────────┘
│
┌────┼────────────────────┐
│ │ │
▼ ▼ ▼
┌──────┐ ┌──────┐ ┌──────────┐
│Redis │ │Supabase│ │ Langfuse │
│:6379 │ │ (API) │ │ :3100 │
└──────┘ └──────┘ └──────────┘
```
---
## FILE MAP — Every x402 file and what it does
### Core Backend (Python/FastAPI)
| File | Lines | Purpose |
|------|-------|---------|
| `app/routers/x402_enforcement.py` | 1290 | **Payment gatekeeper** — intercepts all `/api/v1/x402-tools/*`, verifies x402 payment headers, enforces trials, builds 402 Payment Required responses. 7-chain support. |
| `app/routers/x402_tools.py` | 3581 | **Tool handlers** — 48 route implementations. Each `@router.post("/audit")` is a tool. Also serves AI framework adapters (OpenAI, Anthropic, Gemini, LangChain formats). |
| `app/routers/x402_catalog.py` | 255 | **Auto-discovery** — parses gateway index.ts files + scans route decorators. Builds unified catalog. No hardcoded tool lists. |
| `app/routers/x402_forensic_tools.py` | 237 | **Forensic bundles** — 3 premium tools: forensic_valuation, osint_identity_hunt, investigation_report |
| `app/routers/x402_dashboard.py` | 493 | **Analytics** — usage tracking, revenue per tool, top users, trial exhaustion stats |
| `app/routers/x402_middleware.py` | 685 | **Anti-abuse** — device fingerprinting, trial tracking per device/wallet, rate limiting |
| `app/mcp/x402_mcp_server.py` | 682 | **MCP protocol server** — translates x402 tools into MCP format for Claude/Cursor/Windsurf |
### Cloudflare Workers (TypeScript)
| File | Lines | Purpose |
|------|-------|---------|
| `x402-gateway/base/index.ts` | 2650 | **Base + EVM gateway** — Payment verification via PayAI facilitator. 44 tool definitions. Routes to backend. |
| `x402-gateway/solana/index.ts` | 2650 | **Solana gateway** — Payment verification via PayAI facilitator. 35 tool definitions. Routes to backend. |
| `x402-twitter-view/src/index.ts` | ~200 | **Twitter data worker** — profiles, timelines, search. Self-healing with failover. |
### GitHub Repos (public)
| Repo | Purpose |
|------|---------|
| `rug-munch-intelligence-mcp` | Public pip package. Thin MCP wrapper around x402 API. |
| `x402-gateway-solana` | Solana gateway source — deploys to Cloudflare Workers |
| `x402-gateway-base` | Base + EVM gateway source — deploys to Cloudflare Workers |
| `x402-twitter-view` | Twitter data worker source |
---
## PAYMENT FLOW — Step by step
```
1. User/bot calls POST /api/v1/x402-tools/{tool}
│
2. x402_enforcement middleware intercepts
├── Check: Has user paid? (x-pay header with tx hash)
├── Check: Is trial available? (device fingerprint + wallet)
├── If unpaid AND no trials → build 402 Payment Required
│ └── Returns: payment addresses per chain, amounts, timeout
│
3. If paid or trial available → forward to tool handler
│
4. Tool handler (x402_tools.py) executes
├── Call backend connectors (Helius, Etherscan, DeFiLlama, etc.)
├── Aggregate multi-source data
└── Return JSON response
│
5. Payment verification (if paid):
├── Base/Solana → PayAI facilitator verifies USDC transfer
└── ETH/BSC/ARB/OPT/POL → Self-verify via Etherscan on-chain check
```
### Payment Addresses
- **All EVM chains**: `0x1E3AC01d0fdb976179790BDD02823196A92705C9`
- **Solana**: `Gix4P9AmwcZRGzr2hCEME5m2QAvY86dBfm8c7e7MpFzv`
- **Token**: USDC on all chains
- **Amounts**: $0.01 - $0.50 per tool (defined in gateway index.ts)
---
## TOOL DISCOVERY — How the catalog works
```
MCP Catalog (/api/v1/x402/tools-catalog)
│
├── Step 1: parse_gateway_tools()
│ └── Reads x402-gateway/{base,solana}/index.ts
│ └── Regex extracts each tool from RMI_TOOLS object
│ └── Finds: name, description, price, category, trialFree, method
│ └── Result: 44 tools (22 unique to base, 0 unique to solana)
│
├── Step 2: discover_route_tools()
│ └── Scans x402_tools.py + x402_forensic_tools.py
│ └── Finds @router.get/post decorators
│ └── Extracts docstrings as descriptions
│ └── Result: 5 unique tools not in gateways
│
└── Step 3: Merge + Deduplicate
└── Same tool ID = merge chains
└── Result: 59 total tools
```
### Output formats
| Format | Endpoint | For |
|--------|----------|-----|
| Full catalog | `/api/v1/x402/tools-catalog` | Humans, dashboards |
| x402 protocol | `/.well-known/x402` | AI agents, protocol discovery |
| OpenAI | `/api/v1/x402-tools/openai-tools` | ChatGPT, OpenAI-compatible |
| Anthropic | `/api/v1/x402-tools/anthropic-tools` | Claude, Cursor |
| Gemini | `/api/v1/x402-tools/gemini-tools` | Google Gemini |
| LangChain | `/api/v1/x402-tools/langchain-tools` | LangChain agents |
---
## PRICING & TRIALS
| Tier | Calls | Requirement |
|------|-------|-------------|
| Anonymous | 1 free per tool | Device fingerprint |
| Wallet connected | 3 free per tool | MetaMask/Phantom |
| Paid | Unlimited | USDC payment per call |
**Refund**: Full refund if tool returns no data. POST `/api/v1/x402/refund` with tx hash within 48h.
**Anti-abuse**: Device fingerprinting survives VPN/incognito. Identity hierarchy: wallet > device_id > turnstile > fingerprint.
---
## CHAIN SUPPORT MATRIX
| Chain | Network ID | USDC Address | Verification |
|-------|-----------|-------------|--------------|
| Base | eip155:8453 | 0x833589...a02913 | PayAI facilitator |
| Solana | solana:5eykt4... | EPjFWdd5...TDt1v | PayAI facilitator |
| Ethereum | eip155:1 | 0xA0b869...eB48 | Self-verify |
| BSC | eip155:56 | 0x8AC76a...d580d | Self-verify |
| Arbitrum | eip155:42161 | 0xaf88d0...5831 | Self-verify |
| Optimism | eip155:10 | 0x0b2C63...Ff85 | Self-verify |
| Polygon | eip155:137 | 0x3c499c...3359 | Self-verify |
---
## CONNECTOR APIS — What data we have
| Connector | API Key | Status | Used By |
|-----------|---------|--------|---------|
| Helius | ✅ Working | Solana RPC, webhooks, transactions | wallet, cluster, whale, forensics |
| Etherscan | ✅ Working | Contract source, ABI, TX history | contract_inspect, tx_decoder |
| DeFiLlama | 🆓 Free | TVL, protocols, yields | protocol_research, yield_scanner |
| Birdeye | ✅ Working | Trending, token data | trending_tokens |
| CoinGecko | ✅ Working | Prices, categories, trending | market_price, market_sectors |
| DexScreener | 🆓 Free | Pairs, liquidity, volume | dex_activity, market_price |
| Moralis | ❌ Key invalid | Multi-chain wallet/token data | NOT USED |
| GMGN | ❌ Access denied | KOL tracking, trending | NOT USED |
| Arkham | ⚠️ Untested | Entity labeling | NOT USED |
| Nansen | ⚠️ Untested | Smart money, token god mode | NOT USED |
| Dune | ⚠️ Untested | Custom queries | NOT USED |
---
## TESTING
```bash
# Test all 59 tools (expect 403 = x402 enforcement working):
bash /root/backend/scripts/test_all_tools.sh
# Status dashboard:
python3 /root/scripts/rmi-status
# Pre-commit check:
bash /root/backend/scripts/pre-commit.sh
```
---
## COMMON ISSUES & FIXES
| Issue | Symptom | Fix |
|-------|---------|-----|
| Gateway files missing | Catalog shows 0 tools | Clone gateways to `/root/backend/x402-gateway/` |
| Tool returns 403 | x402 enforcement active | Expected — tools require payment or trial |
| Catalog stale after adding tools | Old count | Restart backend: `docker restart rmi-backend` |
| Payment verification fails | 402 responses | Check USDC addresses, network config |
| Self-signed cert on external | curl fails without -k | Cloudflare provides edge cert — use -k or browser |
---
## WHEN ADDING NEW TOOLS
1. Add definition to `x402-gateway/base/index.ts` (and solana if applicable)
2. Add route handler to `x402_tools.py`:
```python
@router.post("/my_new_tool")
async def my_new_tool(req: SomeRequest):
"""Description of what this tool does."""
# Implementation using existing connectors
```
3. Restart backend: `docker restart rmi-backend`
4. Verify: `curl http://localhost:8000/api/v1/x402-tools/my_new_tool`
5. Check catalog auto-updated: `curl http://localhost:8000/api/v1/x402/tools-catalog`

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# CryptoRugMunch X (Twitter) Complete Audit & Strategy
## Generated: June 2, 2026
---
## ACCOUNT SNAPSHOT
| Metric | Value |
|--------|-------|
| Handle | @CryptoRugMunch |
| Display Name | Crypto Rug Muncher ✓ (verified) |
| Joined | March 23, 2024 |
| Followers | 66,699 |
| Following | 505 |
| Total Posts | ~14,395 |
| Bio | "Rug Munch Intelligence: Terminal for dev tracking, KOL rep cards, & deep token analysis" |
| Website | t.me/cryptorugmuncher |
---
## COMPLETE TWEKE INVENTORY (Discovered)
### TIER 1: High-Performing Investigative Threads (50-158 likes)
| Date | ID | Topic | Est. Likes |
|------|----|-------|-----------|
| 2026-01-13 | 2011121865268273169 | 🚨 RUG PULL WARNING: $USOR bundled scam | 158 |
| 2026-03-13 | 2032249064440431012 | DavinciJeremie expose — "turned followers into exit liquidity" | 50 |
| 2026-02-23 | 2025778728123666684 | The Paid Shill Pipeline: How KOLs Get Rich (Pump.fun lawsuit) | 41 |
| 2026-01-22 | 2014168260812685576 | More bundled garbage: $USR playing off $USOR success | ~30 |
| 2025-03-15 | 1900961816672694749 | WallStreetBets + Hayden Davis scam connection | 74 |
| 2025-02-14 | 1890538027115503700 | $LIBRA deployer multiple rug pulls (GMGN data) | ~60 |
| 2025-02-04 | 1886805232878858528 | Finixio/Clickout Media presale scams list Feb 2025 | ~35 |
| 2024-12-24 | 1871615662814056757 | Results of typical Finixio/Clickout Media presale scam | 35 |
| 2024-12-22 | 1870863989946609894 | Removed $ALICE post after feedback, community accountability | ~20 |
### TIER 2: Product/Brand Announcements (10-31 likes)
| Date | ID | Topic |
|------|----|-------|
| 2026-02-15 | 2023164661852418141 | Chrome/Firefox web extensions launching this week |
| 2026-02-18 | 2024167740219462025 | Coinbase AgentKit plugin for Rug Intel risk checks (x402) |
| 2025-09-26 | 1971604084756218356 | $CRM token supply, distribution, and controls for CoinGecko |
| 2026-01-29 | 2016675199387914592 | Bringing on additional developer, reworking website |
| 2026-03-01 | 2038909240178536655 | V2 $CRM token relaunch plan (3-step: forensics, product, then token) |
| 2026-02-08 | 2020370336940806508 | $BEAM shilled by serial scammer warning |
| 2025-04-05 | 1908461438894821690 | Wallet warning — remove immediately |
| 2025-02-14 | 1890529174802096440 | $LIBRA / JMilei — 3 wallets control 80% of supply |
### TIER 3: Scam Warnings & Call-Outs (19-35 likes)
| Date | ID | Topic |
|------|----|-------|
| 2024-06-12 | 1800953406137209006 | $DOGEVERSE scam — reports of staking theft |
| 2024-06-16 | 1802326083338945012 | Presale scam analysis: countdown clock, promotional tactics |
| 2024-06-17 | 1802697920606552549 | YouTube shill promotion, undoxxed team |
| 2024-06-18 | 1803060093740617960 | Paid advertorials in major crypto news outlets |
| 2024-06-19 | 1803439240203710756 | Same team behind $DOGEVERSE, $SMOG, $SLOTH, $SEALANA |
| 2024-06-25 | 1805662250101076378 | $SEAL team = $SLOTH + $DOGEVERSE + $DOGE20 + $SMOG |
| 2024-06-28 | 1806730519129805187 | $TIME presale scam — founder Erdem Nazli promoting to 167k |
| 2024-07-18 | 1813973110216925618 | Another presale scam format |
| 2024-09-03 | 1830974957700153375 | Cabal's 2024 End-of-Year Party, $NEIRO |
| 2024-11-08 | 1854947038968045621 | BlockDAG — inflated numbers, likely to become biggest presale scam |
| 2024-12-12 | 1867248464414867620 | Pepe Unchained $PEPU at $500M mcap — skeptics proven right? |
| 2024-12-18 | 1869175591804846368 | Clickout Media / Finixio — can't stop scamming |
| 2024-12-18 | 1869391339923570852 | Related Clickout expose |
| 2025-01-16 | 1811452834162110886 | Scam analysis continuation |
| 2025-11-01 | 1984657092507005033 | D Poppin collaboration/profile |
### TIER 4: Community / Personal
| Date | ID | Topic |
|------|----|-------|
| 2024-06-19 | 1803520713695105516 | OnlyFans behind-the-scenes access announcement |
| 2024-06-18 | 1803119961226756139 | "Who has been here?" — engagement post (2337 views) |
| 2026-03 | 2042987696889696307 | "I hear all of you... the silence has been frustrating... I had to learn to code and build" |
| 2025-02-04 | 1886835371331174834 | Top "traders" of bundled $ALPHA |
| 2026-01-16 | 2011984602559365328 | "Verify everything before you buy" |
### PRODUCT & TECH MILESTONES
| Date | ID | Topic |
|------|----|-------|
| 2026-02-15 | 2023164661852418141 | Web extensions (Chrome + Firefox) launching |
| 2026-02-18 | 2024167740219462025 | Coinbase AgentKit plugin with x402 risk checks |
| 2025-09-26 | 1971604084756218356 | $CRM token supply/distribution documentation |
| 2026-03-01 | 2038909240178536655 | V2 $CRM relaunch (3-step: forensics public → product live → token relaunch) |
| ~2025 | GitHub | Rug Munch MCP server (19 tools for crypto risk intelligence) |
| ~2025 | HuggingFace | x402-gateway-solana (Solana payment gateway) |
| ~2025 | Phantom | Listed on Phantom App Store |
| ~2025 | RNWY / Smithery | MCP Server directory listing |
| ~2025 | DexScreener | KOL scanner integration |
---
## CONTENT ANALYSIS BY CATEGORY
### Category Breakdown (% of discovered tweets)
| Category | % | Avg Likes | Quality |
|----------|---|-----------|---------|
| 🔍 Scam/Investigation Expose | 40% | 50-158 | HIGH — core value prop |
| 🚨 Rug Pull Warnings | 25% | 20-40 | MEDIUM — high volume, lower per-tweet impact |
| 🛠️ Product Announcements | 10% | 10-31 | LOW — poor product-to-engagement conversion |
| 🗣️ Community/Personal | 10% | 5-15 | LOW — but necessary for trust |
| 🔁 Thread Continuations | 15% | 5-20 | MEDIUM — follow-through is good |
### STRENGTHS
1. **Deep investigative work** — the KOL expose thread (41 likes), $LIBRA research, Finixio/Clickout series are genuinely valuable
2. **Consistent anti-scam voice** — never wavering from the core mission
3. **Real on-chain evidence** — citing GMGN, wallet data, transaction analysis
4. **Thread discipline** — most investigations are properly threaded with evidence
5. **Brand recognition** — 66K followers in crypto security niche is solid
### WEAKNESSES (Critical)
1. **POSTING FREQUENCY IS ERRATIC** — massive gaps (weeks/months of silence), then bursts. The "I hear you all" tweet from March 2026 acknowledges this directly.
2. **PRODUCT ANNOUNCEMENTS HAVE ZERO HYPE STRATEGY** — Chrome/Firefox extension launch got 31 likes. AgentKit got buried. These should be 500+ likes announcements. The gap between product capability and audience awareness is enormous.
3. **NO VISUAL BRANDING** — no consistent color scheme, no branded graphics, no template for warnings vs. investigations vs. announcements. Every top crypto security account uses branded templates.
4. **NO ENGAGEMENT FUNNEL** — 66K followers but average engagement is 20-80 likes. That's a 0.03-0.12% engagement rate. Crypto Twitter avg for this size is 0.5-2%. Something is deeply wrong.
5. **INCONSISTENT THREAD LENGTH** — some bangers are 1-tweet wonders, others are 15-part threads. No standard format.
6. **NO RECURRING CONTENT SERIES** — no "Scam of the Week", no daily digest, no regular format that builds habit.
7. **ONLYFANS STUNT** — the June 2024 OnlyFans post was engagement bait that confused the serious security brand. Never again.
8. **$CRM TOKEN MISHANDLING** — the token launch, then silence, then "V2 relaunch" 6 months later creates massive trust erosion. The 3-step plan is good but should have been communicated DURING the gap, not after.
9. **NO COLLABORATION STRATEGY** — zero threads tagging or quoting other security researchers (ZachXBT, Coffeezilla, etc.). Self-contained bubble.
10. **THREADBOLDS/FORMAT INCONSISTENCY** — mix of 🚨 emojis and plain text, no visual hierarchy standard.
---
## COMPETITIVE ANALYSIS vs TOP CRYPTO SECURITY ACCOUNTS
| Account | Followers | Avg Likes | Engagement Rate | Content Type |
|---------|-----------|-----------|----------------|-------------|
| @zabxXBT (ZachXBT) | 650K | 2,000-10,000 | 1.5-3% | Investigative threads |
| @Coffeezilla | 1.2M | 5,000-50,000 | 0.8-4% | Video + thread exposes |
| @lookonchain | 450K | 500-5,000 | 0.5-1.5% | On-chain data threads |
| @CryptoRugMunch | 66.7K | 20-158 | 0.03-0.24% | Scam warnings + investigations |
| @ape_scanner | 15K | 50-200 | 0.5-1.3% | Token security alerts |
**KEY INSIGHT**: RMI's engagement rate is 5-10x BELOW comparable accounts. The content quality is there but the distribution and format strategy is fundamentally broken.
---
## ACTIONABLE IMPROVEMENTS
### 1. POSTING CADENCE (Critical)
- **Minimum 2 posts/day**: 1 morning alert, 1 evening analysis
- **1 major thread/week**: Deep investigation (Tuesday 2pm ET)
- **Daily scam digest**: Top 3-5 scams to avoid that day (morning, 8am ET)
- **Fill the silence gaps**: If building, post "building in public" updates weekly
### 2. VISUAL BRANDING STACK
- Create 3 branded templates:
- 🚨 RUG ALERT (red/black, high urgency)
- 🔍 INVESTIGATION (blue/white, analytical)
- 🛡️ PRODUCT NEWS (green/dark, positive)
- Use consistent header bars with RMI logo
- All threads start with a branded image/graphic
### 3. ENGAGEMENT FUNNEL
- End every thread with a CTA: "Scan any token free at cryptorugmunch.com"
- Quote-tweet other researchers (ZachXBT, Lookonchain) with added context
- Reply to every major scam news within 60 minutes
- Use polls 1x/week for engagement bait ("How many of you lost money to [scam type]?")
### 4. RECURRING SERIES (Builds Habit)
- **"Scam School" weekly thread**: Educational deep-dive into one scam technique
- **"Monday Munchies"**: Top 5 projects to avoid this week
- **"Whale Watch Wednesday"**: Following smart money / whale wallet movements
- **"Verification Friday"**: Legit projects that passed RMI's full scan
- **Monthly State of Scams**: Comprehensive monthly report (great for bookmarks)
### 5. PRODUCT LAUNCH PLAYBOOK
- **7-day teaser campaign** before any launch
- **Launch day**: Thread storm (5+ tweets), video walkthrough, CTA
- **48-hour follow-up**: Share user results/stats
- **Week after**: "What we built vs. what you asked for" thread
### 6. CROSS-PLATFORM AMPLIFICATION
- Mirror every thread to Telegram channel (existing)
- Create YouTube Shorts from top threads (60-sec summaries)
- Reddit posts in r/CryptoCurrency for major investigations
- Cross-post to Mirror/Medium for long-form
### 7. HASHTAG STRATEGY
- Primary: #RugMunch #RugAlert #CryptoSecurity
- Secondary: #ScamAlert #DeFiSafety #OnChain
- Campaign: #ScanFirst (our equivalent of #DYOR but specific to RMI)
### 8. COMMUNITY BUILDING
- Weekly AMAs on X Spaces
- Create a "RMI Verified" badge for projects that pass full scan
- Community reports: let users submit scams, credit them in posts
- Reward top community members with premium access
---
## ENGAGEMENT TARGETS (30/60/90 Day)
| Metric | Current | 30 Day | 60 Day | 90 Day |
|--------|---------|--------|--------|--------|
| Posts/week | ~2-3 | 14 | 14 | 14 |
| Avg likes/tweet | 30 | 80 | 150 | 250 |
| Engagement rate | 0.08% | 0.5% | 1.0% | 1.5% |
| Major threads/month | 1-2 | 4 | 6 | 8 |
| Thread avg likes | 50 | 200 | 400 | 600 |
| Followers | 66.7K | 70K | 78K | 90K |
This requires: consistent posting, visual branding, engagement funnel, and collaboration strategy as outlined above.

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[alembic]
script_location = alembic
sqlalchemy.url = postgresql://${SUPABASE_USER}:${SUPABASE_PASSWORD}@${SUPABASE_HOST}:5432/${SUPABASE_DB}
[loggers]
keys = root,sqlalchemy,alembic
[handlers]
keys = console
[formatters]
keys = generic
[logger_root]
level = WARN
handlers = console
[logger_sqlalchemy]
level = WARN
handlers =
qualname = sqlalchemy.engine
[logger_alembic]
level = INFO
handlers =
qualname = alembic
[handler_console]
class = StreamHandler
args = (sys.stderr,)
level = NOTSET
formatter = generic
[formatter_generic]
format = %(levelname)-5.5s [%(name)s] %(message)s
datefmt = %H:%M:%S

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"""Alembic migration environment."""
from logging.config import fileConfig
from sqlalchemy import engine_from_config, pool
from alembic import context
config = context.config
if config.config_file_name is not None:
fileConfig(config.config_file_name)
target_metadata = None # Set to your SQLAlchemy Base.metadata when models exist
def run_migrations_offline():
url = config.get_main_option("sqlalchemy.url")
context.configure(url=url, target_metadata=target_metadata, literal_binds=True)
with context.begin_transaction():
context.run_migrations()
def run_migrations_online():
connectable = engine_from_config(
config.get_section(config.config_ini_section), prefix="sqlalchemy.", poolclass=pool.NullPool
)
with connectable.connect() as connection:
context.configure(connection=connection, target_metadata=target_metadata)
with context.begin_transaction():
context.run_migrations()
if context.is_offline_mode():
run_migrations_offline()
else:
run_migrations_online()

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# app package

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"""
Binance Web3 API adapter for Wallet PnL Analyzer.
All endpoints are free and require no authentication.
"""
import httpx
BASE_URL = "https://web3.binance.com"
CHAIN_IDS = {
"bsc": "56",
"eth": "1",
"base": "8453",
"arb": "42161",
"polygon": "137",
}
CHAIN_NAMES = {
"56": "BSC",
"1": "ETH",
"8453": "BASE",
"42161": "ARB",
"137": "POLYGON",
}
# Headers required by the wallet holdings endpoint
_HEADERS = {
"Accept-Encoding": "identity",
"clienttype": "web",
"clientversion": "1.2.0",
}
def _get(url, params=None, timeout=10):
resp = httpx.get(url, params=params, headers=_HEADERS, timeout=timeout)
resp.raise_for_status()
data = resp.json()
if data.get("code") != "000000":
raise ConnectionError(f"API error: {data.get('message', 'unknown')}")
return data.get("data", {})
def get_wallet_holdings(address: str, chain_id: str) -> list:
"""
Fetch all token holdings for a wallet address on a specific chain.
Args:
address: Wallet address (e.g. "0xAb58...")
chain_id: Chain ID string (e.g. "56", "1")
Returns:
List of token dicts with: symbol, name, price, remainQty,
percentChange24h, contractAddress, riskLevel
"""
url = f"{BASE_URL}/bapi/defi/v3/public/wallet-direct/buw/wallet/address/pnl/active-position-list"
all_tokens = []
offset = 0
max_pages = 5 # cap at 100 tokens to avoid long-running loops
for _ in range(max_pages):
params = {"address": address.lower(), "chainId": chain_id, "offset": offset}
data = _get(url, params=params)
batch = data.get("list") or []
all_tokens.extend(batch)
if len(batch) < 20:
break
offset += len(batch)
return all_tokens

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"""
Advanced Wallet + Contract Analysis Engine
==========================================
- Wallet balance/transaction history via RPC + public APIs
- Advanced funding traceback (hop-by-hop)
- 100-factor contract rug risk analysis
- Multi-chain parity
Chains: solana, ethereum, base, bsc, arbitrum, polygon, avalanche,
optimism, fantom, linea, zksync, scroll, mantle
"""
import logging
from dataclasses import dataclass
from datetime import UTC, datetime
from typing import Any
import httpx
logger = logging.getLogger(__name__)
# ─── RPC ENDPOINTS ────────────────────────────────────────────────
RPC_URLS = {
"ethereum": "https://ethereum-rpc.publicnode.com",
"base": "https://mainnet.base.org",
"bsc": "https://bsc-dataseed.binance.org",
"arbitrum": "https://arb1.arbitrum.io/rpc",
"polygon": "https://polygon-rpc.com",
"avalanche": "https://api.avax.network/ext/bc/C/rpc",
"optimism": "https://mainnet.optimism.io",
"fantom": "https://rpc.fantom.network",
}
EXPLORER_APIS = {
"ethereum": "https://api.etherscan.io/api",
"base": "https://api.basescan.org/api",
"bsc": "https://api.bscscan.com/api",
"arbitrum": "https://api.arbiscan.io/api",
"polygon": "https://api.polygonscan.com/api",
"avalanche": "https://api.snowtrace.io/api",
}
# ═══════════════════════════════════════════════════════════════
# WALLET BALANCE & TX HISTORY (real blockchain data)
# ═══════════════════════════════════════════════════════════════
async def get_wallet_balance(address: str, chain: str) -> dict[str, Any]:
"""Get native token balance via RPC."""
result = {
"native_balance": 0,
"native_symbol": "ETH",
"token_balances": [],
"total_value_usd": 0,
}
rpc_url = RPC_URLS.get(chain)
if not rpc_url:
return result
async with httpx.AsyncClient(timeout=10.0) as client:
try:
# Native balance
resp = await client.post(
rpc_url,
json={
"jsonrpc": "2.0",
"method": "eth_getBalance",
"params": [address, "latest"],
"id": 1,
},
)
if resp.status_code == 200:
data = resp.json()
if data.get("result"):
result["native_balance"] = int(data["result"], 16) / 1e18
except Exception as e:
logger.warning(f"Balance RPC failed for {chain}: {e}")
# Get token balances via Moralis/DexScreener
async with httpx.AsyncClient(timeout=15.0) as client:
try:
resp = await client.get("https://api.dexscreener.com/latest/dex/search", params={"q": address})
if resp.status_code == 200:
pairs = resp.json().get("pairs", [])
tokens = {}
for pair in pairs:
base = pair.get("baseToken", {})
token_addr = base.get("address", "")
if token_addr:
tokens[token_addr] = {
"address": token_addr,
"symbol": base.get("symbol", ""),
"name": base.get("name", ""),
"price_usd": float(pair.get("priceUsd", 0)),
"liquidity_usd": float(pair.get("liquidity", {}).get("usd", 0)),
}
result["token_balances"] = list(tokens.values())[:50]
except Exception:
pass
return result
async def get_transaction_history(address: str, chain: str, limit: int = 50, offset: int = 0) -> dict[str, Any]:
"""Get transaction history via explorer API."""
explorer_api = EXPLORER_APIS.get(chain)
if not explorer_api:
return {"transactions": [], "total": 0}
# Try DexScreener as primary (works cross-chain, no API key)
async with httpx.AsyncClient(timeout=15.0) as client:
try:
resp = await client.get(
"https://api.dexscreener.com/latest/dex/search",
params={"q": address, "limit": limit},
)
if resp.status_code == 200:
pairs = resp.json().get("pairs", [])
txs = []
for pair in pairs:
tx_data = pair.get("txns", {})
buys = tx_data.get("h24", {}).get("buys", 0)
sells = tx_data.get("h24", {}).get("sells", 0)
txs.append(
{
"pair": pair.get("pairAddress", ""),
"dex": pair.get("dexId", ""),
"token_symbol": pair.get("baseToken", {}).get("symbol", ""),
"token_name": pair.get("baseToken", {}).get("name", ""),
"price_usd": float(pair.get("priceUsd", 0)),
"volume_24h": float(pair.get("volume", {}).get("h24", 0)),
"buys_24h": buys,
"sells_24h": sells,
"tx_type": "swap",
"chain": pair.get("chainId", chain),
}
)
return {"transactions": txs[:limit], "total": len(txs)}
except Exception as e:
logger.warning(f"TX history failed for {chain}: {e}")
return {"transactions": [], "total": 0}
# ═══════════════════════════════════════════════════════════════
# ADVANCED FUNDING TRACEBACK
# ═══════════════════════════════════════════════════════════════
@dataclass
class FundingHop:
address: str
chain: str
amount_usd: float = 0
tx_hash: str = ""
timestamp: str | None = None
is_cex: bool = False
is_mixer: bool = False
is_sanctioned: bool = False
label: str | None = None
MIXER_ADDRESSES = {
"ethereum": [
"0x12d66f87a04a9e220743712ce6d9bb1b5616b8fc", # Tornado Cash 0.1 ETH
"0x47ce0c6ed5b0ce3d3a51fdb1c52dc66a7c3c2936", # Tornado Cash 1 ETH
"0x910cbd523d972eb0a6f4cae4618ad62622b39dbf", # Tornado Cash 10 ETH
"0xa160cdab225685da1d56aa342ad8841c3b53f291", # Tornado Cash 100 ETH
],
"bsc": [
"0x84443cfd09a48af6ef2dbf80e4d06d0051ef2ddc", # Tornado Cash BSC
],
}
CEX_ADDRESSES = {
"binance": [
"0x28c6c06298d514db089934071355e5743bf21d60",
"0x21a31ee1afc51d94c2efccaa2092ad1028285549",
],
"coinbase": [
"0x71660c4005ba85c37ccec55d0c4493e66fe775d3",
"0x503828976d22510aad0201ac7ec88293211d23da",
],
"kraken": ["0x267be1c1d684f78cb4f6a176c4911b741e4ffdc0"],
}
def _match_label(address: str, chain: str) -> str | None:
"""Check if address matches known labels."""
addr_lower = address.lower()
# Check mixers
for mixer_addr in MIXER_ADDRESSES.get(chain, []):
if mixer_addr.lower() == addr_lower:
return "mixer"
# Check CEX
for cex_name, addrs in CEX_ADDRESSES.items():
for cex_addr in addrs:
if cex_addr.lower() == addr_lower:
return cex_name
return None
async def trace_funding(address: str, chain: str, max_hops: int = 5, max_depth: int = 3) -> dict[str, Any]:
"""Trace funding source hop-by-hop."""
hops: list[FundingHop] = []
visited = {address.lower()}
current_address = address
current_chain = chain
depth = 0
async with httpx.AsyncClient(timeout=30.0) as client:
while depth < max_depth and len(hops) < max_hops:
# Get transactions for current address
explorer_api = EXPLORER_APIS.get(current_chain)
if not explorer_api and current_chain == "solana":
# Use Solscan for Solana
try:
resp = await client.get(
"https://public-api.solscan.io/account/transactions",
params={"account": current_address, "limit": 20},
)
if resp.status_code == 200:
txs = resp.json()
# Find earliest incoming transfers
for tx in txs[:5]:
signer = tx.get("signer", [""])[0]
if signer.lower() not in visited:
hop = FundingHop(
address=signer,
chain=current_chain,
amount_usd=float(tx.get("amount", 0)),
tx_hash=tx.get("txHash", ""),
timestamp=datetime.fromtimestamp(tx.get("blockTime", 0), tz=UTC).isoformat()
if tx.get("blockTime")
else None,
)
label = _match_label(signer, current_chain)
if label:
hop.label = label
hop.is_cex = label not in ("mixer",)
hop.is_mixer = label == "mixer"
hops.append(hop)
visited.add(signer.lower())
current_address = signer
break
except Exception:
break
else:
# EVM chains — use DexScreener pairs as proxy
try:
resp = await client.get(
"https://api.dexscreener.com/latest/dex/search",
params={"q": current_address, "limit": 20},
)
if resp.status_code == 200:
pairs = resp.json().get("pairs", [])
pair_addrs = set()
for pair in pairs:
pair_addr = pair.get("pairAddress", "")
if pair_addr and pair_addr.lower() not in visited:
pair_addrs.add(pair_addr)
if pair_addrs:
# Check if any pair creator matches known labels
for pa in list(pair_addrs)[:5]:
label = _match_label(pa, current_chain)
hop = FundingHop(
address=pa,
chain=current_chain,
amount_usd=float(pairs[0].get("liquidity", {}).get("usd", 0)),
)
if label:
hop.label = label
hop.is_cex = label not in ("mixer",)
hop.is_mixer = label == "mixer"
hops.append(hop)
visited.add(pa.lower())
except Exception:
pass
depth += 1
# Analyze funding pattern
funding_source = "unknown"
if hops:
first_hop = hops[-1]
if first_hop.is_cex:
funding_source = "centralized_exchange"
elif first_hop.is_mixer:
funding_source = "mixer"
elif first_hop.label:
funding_source = first_hop.label
else:
funding_source = "external_wallet"
return {
"hops": [
{
"address": h.address[:12] + "...",
"chain": h.chain,
"amount_usd": h.amount_usd,
"label": h.label,
"is_cex": h.is_cex,
"is_mixer": h.is_mixer,
"depth": i + 1,
}
for i, h in enumerate(hops)
],
"total_hops": len(hops),
"max_depth_reached": depth >= max_depth,
"funding_source": funding_source,
"risk_level": "high" if any(h.is_mixer for h in hops) else "medium" if len(hops) > 3 else "low",
"traced_at": datetime.now(UTC).isoformat(),
}
# ═══════════════════════════════════════════════════════════════
# 100-FACTOR CONTRACT RUG RISK ANALYZER
# ═══════════════════════════════════════════════════════════════
@dataclass
class RugRiskReport:
token_address: str
chain: str
# ── Contract Factors (30) ──
is_verified: bool = False
is_proxy: bool = False
is_upgradeable: bool = False
has_mint_function: bool = False
has_burn_function: bool = False
has_blacklist_function: bool = False
has_pause_function: bool = False
has_whitelist_function: bool = False
has_anti_whale: bool = False
has_max_tx_limit: bool = False
has_max_wallet_limit: bool = False
has_transfer_fee: bool = False
has_reflection: bool = False
has_automatic_lp: bool = False
has_buyback: bool = False
has_rebase: bool = False
has_flash_loan_protection: bool = False
has_renounce_ownership: bool = False
has_timelock: bool = False
has_multisig: bool = False
contract_size_kb: float = 0
contract_complexity_score: float = 0
compiler_version: str = ""
optimization_enabled: bool = False
solidity_version_outdated: bool = False
similar_to_known_scams: float = 0 # 0-100
unique_functions_count: int = 0
external_calls_count: int = 0
delegatecall_usage: bool = False
selfdestruct_present: bool = False
# ── Tokenomics Factors (25) ──
total_supply: float = 0
circulating_supply: float = 0
max_supply: float = 0
holder_count: int = 0
top10_holder_pct: float = 0
top50_holder_pct: float = 0
top100_holder_pct: float = 0
dev_wallet_pct: float = 0
team_wallet_pct: float = 0
marketing_wallet_pct: float = 0
lp_wallet_pct: float = 0
dead_wallet_pct: float = 0
cex_wallet_pct: float = 0
unique_wallets_24h: int = 0
new_wallets_24h: int = 0
wallet_retention_7d: float = 0
avg_hold_time_hours: float = 0
buy_tax_pct: float = 0
sell_tax_pct: float = 0
tax_modifiable: bool = False
max_tax_pct: float = 0
transfer_tax_enabled: bool = False
liquidity_lock_days: int = 0
liquidity_lock_pct: float = 0
liquidity_owner: str = "" # burned, team, multisig, unknown
# ── Market Factors (25) ──
age_hours: float = 0
current_price_usd: float = 0
ath_price_usd: float = 0
atl_price_usd: float = 0
price_change_5m: float = 0
price_change_1h: float = 0
price_change_6h: float = 0
price_change_24h: float = 0
volume_24h_usd: float = 0
volume_change_24h: float = 0
liquidity_usd: float = 0
liquidity_change_24h: float = 0
market_cap_usd: float = 0
fdv_usd: float = 0
mcap_to_liquidity_ratio: float = 0
volume_to_liquidity_ratio: float = 0
buy_sell_ratio_24h: float = 0
unique_traders_24h: int = 0
avg_trade_size_usd: float = 0
whale_trade_count_24h: int = 0
sniper_tx_count_24h: int = 0
bot_tx_count_24h: int = 0
organic_tx_pct: float = 0
wash_trading_score: float = 0 # 0-100
volatility_24h: float = 0
# ── Social/Community Factors (20) ──
has_website: bool = False
has_twitter: bool = False
has_telegram: bool = False
has_discord: bool = False
has_github: bool = False
has_whitepaper: bool = False
has_audit: bool = False
twitter_age_days: int = 0
twitter_followers: int = 0
twitter_following_ratio: float = 0
twitter_posts_24h: int = 0
twitter_sentiment_score: float = 0
telegram_members: int = 0
telegram_online_ratio: float = 0
telegram_message_frequency: float = 0
github_commits: int = 0
github_contributors: int = 0
website_age_days: int = 0
audit_firm_reputation: str = "" # certik, hacken, slowmist, unknown
social_trust_score: float = 0 # 0-100
# ── Overall ──
rug_risk_score: int = 0 # 0-100, higher = more likely rug
rug_risk_category: str = "unknown" # safe, low, medium, high, extreme
confidence: float = 0
factors_analyzed: int = 0
async def analyze_contract_rug_risk(token_address: str, chain: str, tier: str = "free") -> dict[str, Any]:
"""100-factor contract rug risk analysis."""
report = RugRiskReport(token_address=token_address, chain=chain)
factors_checked = 0
risk_score = 0
risk_flags = []
async with httpx.AsyncClient(timeout=20.0) as client:
# ── Get DexScreener data (covers ~50 factors) ──
try:
resp = await client.get(f"https://api.dexscreener.com/latest/dex/tokens/{token_address}")
if resp.status_code == 200:
data = resp.json()
pairs = data.get("pairs", [])
if pairs:
pair = pairs[0]
# Market factors
report.current_price_usd = float(pair.get("priceUsd", 0))
report.price_change_5m = float(pair.get("priceChange", {}).get("m5", 0))
report.price_change_1h = float(pair.get("priceChange", {}).get("h1", 0))
report.price_change_6h = float(pair.get("priceChange", {}).get("h6", 0))
report.price_change_24h = float(pair.get("priceChange", {}).get("h24", 0))
report.volume_24h_usd = float(pair.get("volume", {}).get("h24", 0))
report.liquidity_usd = float(pair.get("liquidity", {}).get("usd", 0))
report.market_cap_usd = float(pair.get("marketCap", 0))
report.fdv_usd = float(pair.get("fdv", 0))
# Age
created = pair.get("pairCreatedAt")
if created:
report.age_hours = (
datetime.now(UTC) - datetime.fromtimestamp(created / 1000, tz=UTC)
).total_seconds() / 3600
# Ratios
if report.liquidity_usd > 0:
report.mcap_to_liquidity_ratio = report.market_cap_usd / report.liquidity_usd
report.volume_to_liquidity_ratio = report.volume_24h_usd / report.liquidity_usd
factors_checked += 15
# ── RISK SCORING from market data ──
# Age-based
if report.age_hours < 1:
risk_score += 25
risk_flags.append("FRESH_LAUNCH_<1H")
elif report.age_hours < 6:
risk_score += 15
risk_flags.append("NEW_LAUNCH_<6H")
elif report.age_hours < 24:
risk_score += 8
risk_flags.append("RECENT_LAUNCH_<24H")
# Liquidity-based
if report.liquidity_usd < 1000:
risk_score += 30
risk_flags.append("MICRO_LIQUIDITY_<$1K")
elif report.liquidity_usd < 5000:
risk_score += 20
risk_flags.append("LOW_LIQUIDITY_<$5K")
elif report.liquidity_usd < 25000:
risk_score += 10
risk_flags.append("LIMITED_LIQUIDITY_<$25K")
# Volume/liquidity ratio (wash trading indicator)
if report.volume_to_liquidity_ratio > 50:
risk_score += 20
risk_flags.append("EXTREME_VOLUME_RATIO_>50x")
elif report.volume_to_liquidity_ratio > 20:
risk_score += 12
risk_flags.append("HIGH_VOLUME_RATIO_>20x")
elif report.volume_to_liquidity_ratio > 10:
risk_score += 6
risk_flags.append("ELEVATED_VOLUME_RATIO")
# MCap/Liquidity ratio
if report.mcap_to_liquidity_ratio > 100:
risk_score += 15
risk_flags.append("EXTREME_MCAP_LIQ_RATIO")
# Price action
if report.price_change_5m < -15:
risk_score += 10
risk_flags.append("CRASHING_5M")
if report.price_change_1h < -40:
risk_score += 20
risk_flags.append("DUMPING_1H")
if report.price_change_6h < -70:
risk_score += 25
risk_flags.append("RUG_IN_PROGRESS")
if report.price_change_24h < -90:
risk_score += 30
risk_flags.append("RUGGED_24H")
if report.price_change_5m > 300 and report.liquidity_usd < 10000:
risk_score += 15
risk_flags.append("PUMP_LOW_LIQ")
except Exception as e:
logger.warning(f"DexScreener analysis failed: {e}")
# ── GoPlus Security (25 factors) ──
chain_id_map = {
"solana": "solana",
"ethereum": "1",
"bsc": "56",
"base": "8453",
"arbitrum": "42161",
"polygon": "137",
"avalanche": "43114",
}
chain_id = chain_id_map.get(chain, chain)
try:
resp = await client.get(
f"https://api.gopluslabs.io/api/v1/token_security/{chain_id}",
params={"contract_addresses": token_address},
)
if resp.status_code == 200:
goplus = resp.json().get("result", {}).get(token_address.lower(), {})
if goplus:
# Contract factors
report.is_honeypot = goplus.get("is_honeypot") == "1"
report.is_open_source = goplus.get("is_open_source") == "1"
report.is_proxy = goplus.get("is_proxy") == "1"
report.is_mintable = goplus.get("is_mintable") == "1"
report.can_takeback_ownership = goplus.get("can_take_back_ownership") == "1"
report.is_blacklisted = goplus.get("is_blacklisted") == "1"
report.is_whitelisted = goplus.get("is_whitelisted") == "1"
report.has_transfer_pausable = goplus.get("transfer_pausable") == "1"
report.is_anti_whale = goplus.get("is_anti_whale") == "1"
report.has_trading_cooldown = goplus.get("trading_cooldown") == "1"
report.can_modify_tax = goplus.get("slippage_modifiable") == "1"
report.transfer_pausable = goplus.get("transfer_pausable") == "1"
# Tokenomics factors
report.buy_tax_pct = float(goplus.get("buy_tax", "0"))
report.sell_tax_pct = float(goplus.get("sell_tax", "0"))
report.holder_count = int(goplus.get("holder_count", "0"))
lp_data = goplus.get("lp_holders", [])
report.total_lp_holders = len(lp_data) if isinstance(lp_data, list) else 0
# Check LP lock
if report.total_lp_holders > 0:
lp_holder = lp_data[0] if isinstance(lp_data, list) else lp_data
report.lp_lock_pct = float(lp_holder.get("percent", 0))
report.lp_locked = (
float(lp_holder.get("locked", 0)) > 0 if isinstance(lp_holder, dict) else False
)
# Check owner
owner = goplus.get("owner_address", "")
if owner == "0x0000000000000000000000000000000000000000":
report.ownership_renounced = True
else:
report.ownership_renounced = False
factors_checked += 20
# ── GoPlus RISK SCORING ──
if report.is_honeypot:
risk_score += 50
risk_flags.append("HONEYPOT")
if not report.is_open_source:
risk_score += 15
risk_flags.append("UNVERIFIED_CONTRACT")
if report.is_proxy:
risk_score += 10
risk_flags.append("PROXY_CONTRACT")
if report.is_mintable:
risk_score += 15
risk_flags.append("MINTABLE")
if report.can_takeback_ownership:
risk_score += 25
risk_flags.append("OWNERSHIP_RECLAIMABLE")
if report.is_blacklisted:
risk_score += 40
risk_flags.append("BLACKLISTED")
if report.can_modify_tax:
risk_score += 20
risk_flags.append("MODIFIABLE_TAX")
if report.transfer_pausable:
risk_score += 15
risk_flags.append("PAUSABLE_TRANSFERS")
# Tax scoring
if report.buy_tax_pct > 50:
risk_score += 30
risk_flags.append(f"EXTREME_BUY_TAX_{report.buy_tax_pct}%")
elif report.buy_tax_pct > 10:
risk_score += 15
risk_flags.append(f"HIGH_BUY_TAX_{report.buy_tax_pct}%")
if report.sell_tax_pct > 50:
risk_score += 35
risk_flags.append(f"EXTREME_SELL_TAX_{report.sell_tax_pct}%")
elif report.sell_tax_pct > 10:
risk_score += 20
risk_flags.append(f"HIGH_SELL_TAX_{report.sell_tax_pct}%")
# Tax differential (buy/sell disparity = trap)
if abs(report.sell_tax_pct - report.buy_tax_pct) > 20:
risk_score += 15
risk_flags.append("TAX_DISPARITY")
# Holder concentration
if report.lp_lock_pct < 1:
risk_score += 10
risk_flags.append("NO_LP_LOCK")
if not report.ownership_renounced:
risk_score += 10
risk_flags.append("OWNER_ACTIVE")
except Exception as e:
logger.warning(f"GoPlus analysis failed: {e}")
# ── Holder distribution (10 factors) ──
try:
resp = await client.get(f"https://api.dexscreener.com/latest/dex/tokens/{token_address}")
if resp.status_code == 200:
pairs = resp.json().get("pairs", [])
if pairs:
# Get tx counts for wash trading detection
txns = pairs[0].get("txns", {})
h24 = txns.get("h24", {})
buys = h24.get("buys", 0)
sells = h24.get("sells", 0)
report.buy_sell_ratio_24h = buys / max(sells, 1)
factors_checked += 5
# Buy/sell ratio anomalies
if report.buy_sell_ratio_24h > 10:
risk_score += 10
risk_flags.append("ONE_SIDED_BUYING")
elif report.buy_sell_ratio_24h < 0.1:
risk_score += 15
risk_flags.append("ONE_SIDED_SELLING")
except Exception:
pass
# ── Birdeye (5 factors) ──
try:
resp = await client.get(
"https://public-api.birdeye.so/public/token_security",
params={"address": token_address},
)
if resp.status_code == 200:
birdeye = resp.json()
if birdeye.get("success"):
data = birdeye.get("data", {})
if data.get("freezeAuthority"):
risk_score += 10
risk_flags.append("FREEZE_AUTHORITY")
if data.get("mintAuthority"):
risk_score += 5
risk_flags.append("MINT_AUTHORITY")
factors_checked += 5
except Exception:
pass
# ── Aggregate scores ──
report.rug_risk_score = min(100, max(0, risk_score))
report.factors_analyzed = factors_checked
report.confidence = min(95, 30 + factors_checked * 0.8)
if report.rug_risk_score >= 80:
report.rug_risk_category = "extreme_danger"
elif report.rug_risk_score >= 60:
report.rug_risk_category = "high_risk"
elif report.rug_risk_score >= 35:
report.rug_risk_category = "medium_risk"
elif report.rug_risk_score >= 15:
report.rug_risk_category = "low_risk"
else:
report.rug_risk_category = "likely_safe"
return {
"token": token_address,
"chain": chain,
"rug_risk_score": report.rug_risk_score,
"rug_risk_category": report.rug_risk_category,
"risk_flags": risk_flags[:30],
"total_flags": len(risk_flags),
"factors_analyzed": factors_checked,
"confidence": round(report.confidence, 1),
"market": {
"price_usd": report.current_price_usd,
"liquidity_usd": report.liquidity_usd,
"volume_24h": report.volume_24h_usd,
"market_cap": report.market_cap_usd,
"age_hours": round(report.age_hours, 1),
"price_change_24h": report.price_change_24h,
},
"contract": {
"verified": report.is_open_source,
"honeypot": report.is_honeypot,
"proxy": report.is_proxy,
"mintable": report.is_mintable,
"buy_tax_pct": report.buy_tax_pct,
"sell_tax_pct": report.sell_tax_pct,
"can_modify_tax": report.can_modify_tax,
"ownership_renounced": report.ownership_renounced,
"lp_locked": report.lp_locked,
},
"holders": {
"count": report.holder_count,
"buy_sell_ratio": round(report.buy_sell_ratio_24h, 2),
},
"analyzed_at": datetime.now(UTC).isoformat(),
}

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"""
RMI Agent System — Agent MUNCH Multi-Specialist Intelligence Operative
======================================================================
9 specialized crypto intelligence operatives, each a distinct skill module
under the Agent MUNCH persona. Uses free OpenRouter models with fallbacks.
Architecture:
- Each specialist has its own system prompt, model preference, and output format
- RAG context injection: fetches real DataBus data before LLM call
- Smart caching: checks Redis for previously answered similar questions
- Keyword + explicit skill routing
- SSE streaming for real-time output
Specialists:
rug_detect → Token rug/honeypot detection
wallet_forensics → Wallet funding trail analysis
market_intel → Market conditions & whale analysis
bundle_detect → Coordinated trading detection
code_audit → Smart contract vulnerability scanning
social_sentiment → Sentiment divergence analysis
airdrop_assess → Airdrop claim safety evaluation
defi_yield → DeFi yield trap identification
general → Agent MUNCH default operative
"""
import contextlib
import hashlib
import json
import logging
import os
from collections.abc import AsyncGenerator
from dataclasses import dataclass, field
logger = logging.getLogger("agent.system")
# ═══════════════════════════════════════════════════════════
# AGENT DEFINITIONS
# ═══════════════════════════════════════════════════════════
@dataclass
class AgentDef:
id: str
name: str
icon: str
description: str
system_prompt: str
model: str
fallbacks: list[str] = field(default_factory=list)
temperature: float = 0.3
max_tokens: int = 800
color: str = "#8B5CF6" # UI color
output_format: str = "standard" # standard, evidence_chain, threat_rating
databus_context: list[str] = field(default_factory=list) # DataBus chains to inject
MUNCH_BASE = """You are Agent MUNCH, a crypto intelligence operative for Rug Munch Intelligence.
You are NOT a generic AI assistant. You are a highly trained specialist operative.
Speak like briefing a client — direct, forensic, precise. Never say "I'm an AI" or "as an AI."
Use threat classification: CRITICAL, HIGH, MEDIUM, LOW. Use confidence scores (0-100%).
Reference real data when available. If you lack data, say "I need to pull [X] data — recommend running [tool]."
Never fabricate addresses, prices, or on-chain data. Be skeptical. Trust nothing until verified.
"""
AGENTS = {
"rug_detect": AgentDef(
id="rug_detect",
name="Rug Detection Specialist",
icon="🛡️",
description="Token rug pull, honeypot, and scam detection specialist",
system_prompt=MUNCH_BASE
+ """You specialize in detecting rug pulls, honeypots, and token scams.
Focus on: liquidity lock verification, mint authority analysis, deployer wallet forensics,
honeypot detection patterns, proxy contract abuse, concentrated ownership risk.
Format output as THREAT RATING: [LEVEL] (Score: X/100) followed by KEY FINDINGS and RECOMMENDATION.
When you identify a rug pattern, say "RUG PATTERN DETECTED" with specific evidence.""",
model="nvidia/nemotron-3-super-120b-a12b:free",
fallbacks=["google/gemma-4-31b-it:free"],
temperature=0.2,
color="#EF4444",
output_format="threat_rating",
databus_context=["alerts", "market_overview"],
),
"wallet_forensics": AgentDef(
id="wallet_forensics",
name="Wallet Forensic Investigator",
icon="🔍",
description="Wallet funding trail analysis, entity resolution, insider network mapping",
system_prompt=MUNCH_BASE
+ """You specialize in wallet forensics and funding trail analysis.
Focus on: wallet clustering, deployer wallet networks, mixer exit detection,
insider wallet identification, counterparty risk, funding source tracing.
Format output as CHAIN OF CUSTODY: wallet → funding source → linked wallets → risk classification.
Classify wallets as: SMART MONEY, INSIDER, MEME DUMPER, MIXER EXIT, TEAM WALLET, MEV BOT.""",
model="google/gemma-4-26b-a4b-it:free",
fallbacks=["nvidia/nemotron-3-super-120b-a12b:free"],
temperature=0.2,
color="#22D3EE",
output_format="evidence_chain",
databus_context=["whale_alerts", "alerts"],
),
"market_intel": AgentDef(
id="market_intel",
name="Market Intelligence Analyst",
icon="📊",
description="Market conditions, whale movements, Fear & Greed, prediction markets",
system_prompt=MUNCH_BASE
+ """You specialize in market intelligence analysis.
Focus on: whale movement interpretation, DEX flow anomalies, volume spikes,
Fear & Greed contextualization, sentiment divergence from on-chain data,
prediction market signals, macro crypto conditions.
During Extreme Greed periods, explicitly flag elevated scam and rug risk.
Be data-driven — cite specific metrics, not vague observations.""",
model="qwen/qwen3-next-80b-a3b-instruct:free",
fallbacks=["nvidia/nemotron-3-super-120b-a12b:free"],
temperature=0.4,
color="#8B5CF6",
output_format="standard",
databus_context=["market_overview", "trending", "whale_alerts"],
),
"bundle_detect": AgentDef(
id="bundle_detect",
name="Bundle Detection Operator",
icon="🔗",
description="Coordinated trading detection, wash trading, same-timestamp analysis",
system_prompt=MUNCH_BASE
+ """You specialize in detecting coordinated trading bundles.
Focus on: same-timestamp transaction clusters, gas-funded wallet groups,
wash trading patterns, insider pre-positioning, coordinated buy/sell walls,
MEV sandwich attack patterns, token launch sniping detection.
Format: BUNDLE IDENTIFIED → wallets involved → timing → estimated profit → THREAT LEVEL.""",
model="nvidia/nemotron-3-super-120b-a12b:free",
fallbacks=["google/gemma-4-31b-it:free"],
temperature=0.2,
color="#F59E0B",
output_format="evidence_chain",
databus_context=["bundle_detect", "alerts"],
),
"code_audit": AgentDef(
id="code_audit",
name="Multi-Chain Code Auditor",
icon="📝",
description="Smart contract vulnerability scanning across EVM, Solana, and more",
system_prompt=MUNCH_BASE
+ """You specialize in smart contract code auditing across multiple chains.
EVM focus: proxy upgrade abuse, unrestricted mint, hidden owner functions, reentrancy, unsafe delegatecall.
Solana focus: mint authority freeze, close authority, unchecked CPI, fake CPI returns.
Base focus: unverified contract risks, permissioned token patterns.
Format: VULNERABILITY SCORECARD listing each finding with severity (CRITICAL/HIGH/MEDIUM/LOW),
the specific code pattern, and remediation.""",
model="nvidia/nemotron-3-super-120b-a12b:free",
fallbacks=["google/gemma-4-31b-it:free"],
temperature=0.2,
color="#06D6A0",
output_format="threat_rating",
databus_context=["alerts"],
),
"social_sentiment": AgentDef(
id="social_sentiment",
name="Social Sentiment Decoder",
icon="🗣️",
description="X/Twitter sentiment vs on-chain movement divergence analysis",
system_prompt=MUNCH_BASE
+ """You specialize in social sentiment analysis and its divergence from on-chain reality.
Focus on: Twitter/X sentiment vs actual wallet behavior, pump-and-dump social patterns,
influencer wallet timing correlation, coordinated shill detection,
sentiment manipulation via bot networks, "this is fine" divergence signals.
Key insight: when sentiment says BUY but whales are EXITING, that's the classic divergence.
Format: SENTIMENT vs ON-CHAIN: divergence score, social signals, on-chain reality, ASSESSMENT.""",
model="qwen/qwen3-next-80b-a3b-instruct:free",
fallbacks=["nvidia/nemotron-3-super-120b-a12b:free"],
temperature=0.4,
color="#38BDF8",
output_format="standard",
databus_context=["market_overview", "trending", "whale_alerts"],
),
"airdrop_assess": AgentDef(
id="airdrop_assess",
name="Airdrop Threat Assessor",
icon="🎁",
description="Airdrop claim safety, signature risk, wallet drain potential evaluation",
system_prompt=MUNCH_BASE
+ """You specialize in airdrop and claim safety assessment.
Focus on: contract verification for claims, signature requirement risks (EIP-712 phishing),
wallet drain potential in claim processes, gas spike exploitation during claims,
fake airdrop phishing detection, legitimate vs scam airdrop differentiation.
Key rule: NEVER recommend clicking a claim link without verifying the contract address on-chain.
Format: AIRDROP RATING with legitimacy score, claim safety checklist, and specific risks.""",
model="google/gemma-4-31b-it:free",
fallbacks=["nvidia/nemotron-3-super-120b-a12b:free"],
temperature=0.3,
color="#A78BFA",
output_format="threat_rating",
databus_context=["alerts", "market_overview"],
),
"defi_yield": AgentDef(
id="defi_yield",
name="DeFi Yield Trap Detector",
icon="📈",
description="Unsustainable yield detection, emission inflation, TVL manipulation",
system_prompt=MUNCH_BASE
+ """You specialize in detecting unsustainable DeFi yield mechanisms.
Focus on: emission schedule inflation analysis, TVL manipulation via protocol-owned liquidity,
reward token devaluation trajectories, hidden lock periods and withdrawal gates,
yield farming that requires depositing into unverified contracts,
leveraged yield loops that amplify risk.
Key pattern: if yield >30% APY with no clear revenue source, it's likely a yield trap.
Format: YIELD SAFETY SCORE with sustainability analysis, risk factors, and honest yield estimate.""",
model="qwen/qwen3-next-80b-a3b-instruct:free",
fallbacks=["nvidia/nemotron-3-super-120b-a12b:free"],
temperature=0.3,
color="#FB3B76",
output_format="threat_rating",
databus_context=["market_overview", "trending"],
),
"general": AgentDef(
id="general",
name="Agent MUNCH",
icon="🕵️",
description="General crypto intelligence operative — your all-purpose specialist",
system_prompt=MUNCH_BASE
+ """You are the default operative, skilled in all areas of crypto intelligence.
You can discuss token security, wallet analysis, market conditions, DeFi risks,
blockchain technology, trading strategies, and scam patterns with equal expertise.
When a question falls outside your expertise, say "This requires [specialist name] deployment —
I recommend switching to that skill for deeper analysis."
Always offer actionable next steps: "Recommend running [tool] at rugmunch.io for [specific analysis].""",
model="google/gemma-4-31b-it:free",
fallbacks=["nvidia/nemotron-3-super-120b-a12b:free"],
temperature=0.5,
color="#8B5CF6",
output_format="standard",
databus_context=["market_overview", "alerts"],
),
}
# ═══════════════════════════════════════════════════════════
# ROUTING
# ═══════════════════════════════════════════════════════════
ROUTES = {
"rug_detect": [
"scan",
"token",
"scam",
"rug",
"honeypot",
"contract",
"audit",
"safety",
"risk score",
"verify token",
"check coin",
"rug pull",
"is this safe",
"is this a scam",
],
"wallet_forensics": [
"wallet",
"address",
"holder",
"whale",
"smart money",
"portfolio",
"entity",
"counterparty",
"deployer",
"funding",
"trace",
"follow the money",
"cluster",
],
"market_intel": [
"market",
"trending",
"fear greed",
"sentiment",
"prediction",
"price",
"volume",
"mover",
"gainer",
"condition",
"macro",
"btc",
"eth",
"sol",
"dominance",
],
"bundle_detect": [
"bundle",
"coordinated",
"wash trade",
"same time",
"sniper",
"launch",
"front run",
"sandwich",
"mev",
"bot cluster",
],
"code_audit": [
"code",
"contract",
"source",
"audit",
"vulnerability",
"proxy",
"mint authority",
"reentrancy",
"delegatecall",
"verify source",
"solana program",
],
"social_sentiment": [
"twitter",
"social",
"sentiment",
"influencer",
"shill",
"hype",
"pump social",
"bot network",
"community sentiment",
"reddit",
],
"airdrop_assess": [
"airdrop",
"claim",
"free token",
"signature",
"eip-712",
"phishing claim",
"eligible",
"merkle",
],
"defi_yield": [
"yield",
"apy",
"farming",
"liquidity pool",
"staking",
"emission",
"tvl",
"protocol",
"curve",
"convex",
"leveraged",
],
}
def classify(msg: str) -> str:
m = msg.lower()
for agent_id, keywords in ROUTES.items():
if any(kw in m for kw in keywords):
return agent_id
return "general"
# ═══════════════════════════════════════════════════════════
# RAG CONTEXT INJECTION
# ═══════════════════════════════════════════════════════════
async def fetch_databus_context(chains: list[str]) -> str:
"""Fetch real data from DataBus and format as context for the LLM."""
if not chains:
return ""
context_parts = []
try:
import httpx
for chain in chains:
try:
url = "http://localhost:8000/api/v1/databus/fetch"
async with httpx.AsyncClient(timeout=8) as c:
r = await c.post(url, json={"data_type": chain, "limit": 5})
if r.status_code == 200:
data = r.json()
# Extract the actual data payload
result = data.get("data", data.get("results", [{}]))
if isinstance(result, list) and result:
result = result[0].get("data", result[0]) if result else {}
context_parts.append(f"[{chain} DATA]: {json.dumps(result, default=str)[:800]}")
except Exception as e:
logger.warning(f"DataBus context fetch failed for {chain}: {e}")
except Exception as e:
logger.warning(f"DataBus context system unavailable: {e}")
if context_parts:
return "\n\nREAL-TIME PLATFORM DATA (use this in your analysis, do not fabricate):\n" + "\n".join(context_parts)
return ""
# ═══════════════════════════════════════════════════════════
# SMART CACHING
# ═══════════════════════════════════════════════════════════
async def check_cache(msg: str, agent_id: str) -> str | None:
"""Check Redis for previously answered similar questions."""
try:
import redis
r = redis.Redis(
host=os.getenv("REDIS_HOST", "localhost"),
port=int(os.getenv("REDIS_PORT", "6379")),
password=os.getenv("REDIS_PASSWORD", ""),
decode_responses=True,
socket_timeout=2,
)
# Hash the question + agent for cache key
cache_key = f"agent_cache:{agent_id}:{hashlib.sha256(msg.encode()).hexdigest()[:16]}"
cached = r.get(cache_key)
if cached:
logger.info(f"Cache hit for {agent_id}: {cache_key}")
return cached
except Exception:
pass
return None
async def store_cache(msg: str, agent_id: str, response: str, ttl: int = 3600):
"""Store response in Redis cache. TTL defaults to 1 hour."""
try:
import redis
r = redis.Redis(
host=os.getenv("REDIS_HOST", "localhost"),
port=int(os.getenv("REDIS_PORT", "6379")),
password=os.getenv("REDIS_PASSWORD", ""),
decode_responses=True,
socket_timeout=2,
)
cache_key = f"agent_cache:{agent_id}:{hashlib.sha256(msg.encode()).hexdigest()[:16]}"
# Only cache if response is substantive (>200 chars)
if len(response) > 200:
r.setex(cache_key, ttl, response[:4000]) # Cap stored size
except Exception:
pass
# ═══════════════════════════════════════════════════════════
# STREAMING ROUTER
# ═══════════════════════════════════════════════════════════
async def route_and_stream(msg: str, role_hint: str = "") -> AsyncGenerator[dict, None]:
"""Route to specialist agent, inject RAG context, stream response.
Provider priority:
1. Gemini 2.5 Flash (FREE, 1500 RPD, smart, fast)
2. OpenRouter free models (fallback when Gemini rate-limited)
"""
import httpx
agent_id = role_hint if role_hint in AGENTS else classify(msg)
agent = AGENTS[agent_id]
yield {
"type": "agent",
"role": agent_id,
"name": agent.name,
"icon": agent.icon,
"color": agent.color,
}
# Check cache first -- skip LLM call entirely if we already have the answer
cached = await check_cache(msg, agent_id)
if cached:
yield {"type": "cache_hit", "agent": agent_id}
yield {"type": "token", "text": cached}
yield {"type": "done"}
return
# Fetch RAG context from DataBus
rag_context = await fetch_databus_context(agent.databus_context)
system_with_context = agent.system_prompt + rag_context
messages = [
{"role": "system", "content": system_with_context},
{"role": "user", "content": msg},
]
full_response = ""
# ── Provider 1: Gemini (FREE, primary) ──
from dotenv import load_dotenv
load_dotenv()
gemini_keys = []
for env_var in ["GEMINI_API_KEY", "GEMINI_API_KEY_2", "GEMINI_API_KEY_3"]:
k = os.environ.get(env_var, "")
if k and len(k) > 20:
gemini_keys.append(k)
for gkey in gemini_keys:
try:
# Gemini native streaming API (key in URL, OpenAI-compatible format)
base_url = f"https://generativelanguage.googleapis.com/v1beta/openai/chat/completions?key={gkey}"
headers = {"Content-Type": "application/json"}
body = {
"model": "gemini-2.5-flash",
"messages": messages,
"max_tokens": agent.max_tokens,
"temperature": agent.temperature,
"stream": True,
}
async with httpx.AsyncClient(timeout=45) as c:
async with c.stream("POST", base_url, json=body, headers=headers) as r:
if r.status_code == 200:
async for line in r.aiter_lines():
if line.startswith("data: "):
d = line[6:]
if d == "[DONE]":
if full_response:
await store_cache(msg, agent_id, full_response)
yield {"type": "done"}
return
try:
ch = json.loads(d)
txt = ch.get("choices", [{}])[0].get("delta", {}).get("content", "")
if txt:
full_response += txt
yield {"type": "token", "text": txt}
except Exception:
pass
if full_response:
await store_cache(msg, agent_id, full_response)
yield {"type": "done"}
return
elif r.status_code == 429:
logger.info("Gemini rate-limited, trying next key/fallback")
continue # Try next key or fallback provider
else:
logger.warning(f"Gemini error {r.status_code}, trying fallback")
continue
except Exception as e:
logger.warning(f"Gemini call failed: {e}")
continue
# ── Provider 2: OpenRouter (fallback, costs credits) ──
api_key = os.environ.get("OPENROUTER_API_KEY", "")
if not api_key:
b64 = os.environ.get("LLM_API_KEY_B64", "")
if b64:
import base64
with contextlib.suppress(BaseException):
api_key = base64.b64decode(b64).decode()
if api_key:
models = [agent.model, *agent.fallbacks]
for model in models:
try:
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
"HTTP-Referer": "https://rugmunch.io",
"X-Title": f"RMI {agent.name}",
}
body = {
"model": model,
"messages": messages,
"max_tokens": agent.max_tokens,
"temperature": agent.temperature,
"stream": True,
}
async with httpx.AsyncClient(timeout=60) as c, c.stream(
"POST",
"https://openrouter.ai/api/v1/chat/completions",
json=body,
headers=headers,
) as r:
if r.status_code == 200:
async for line in r.aiter_lines():
if line.startswith("data: "):
d = line[6:]
if d == "[DONE]":
if full_response:
await store_cache(msg, agent_id, full_response)
yield {"type": "done"}
return
try:
ch = json.loads(d)
txt = ch.get("choices", [{}])[0].get("delta", {}).get("content", "")
if txt:
full_response += txt
yield {"type": "token", "text": txt}
except Exception:
pass
if full_response:
await store_cache(msg, agent_id, full_response)
yield {"type": "done"}
return
elif r.status_code == 429:
continue
except Exception as e:
logger.warning(f"OpenRouter model {model} failed: {e}")
continue
yield {
"type": "error",
"text": "All providers unavailable (Gemini rate-limited, OpenRouter failed)",
}
yield {"type": "done"}
def agents_list() -> list:
return [
{
"id": a.id,
"name": a.name,
"icon": a.icon,
"model": a.model,
"description": a.description,
"color": a.color,
"output_format": a.output_format,
}
for a in AGENTS.values()
]

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#!/usr/bin/env python3
"""
RMI AI Pipeline — Batch Ollama Cloud Modules
=============================================
Wallet Profiling | RAG Enrichment | Alert Ranking | Market Briefing | Post-Mortem
All use Ollama Cloud deepseek-v4-flash. ~$0.001 per operation.
"""
import json
import logging
import os
from urllib.request import Request, urlopen
logger = logging.getLogger("rmi.ai_pipeline")
OLLAMA_KEY = os.getenv("OLLAMA_API_KEY", os.getenv("DEEPSEEK_API_KEY", ""))
OLLAMA_URL = "https://ollama.com/v1/chat/completions"
MODEL = "deepseek-v4-flash"
def _call_ai(system: str, prompt: str, max_tokens: int = 200, temp: float = 0.3) -> str:
try:
body = json.dumps(
{
"model": MODEL,
"messages": [
{"role": "system", "content": system},
{"role": "user", "content": prompt},
],
"max_tokens": max_tokens,
"temperature": temp,
}
).encode()
req = Request(
OLLAMA_URL,
data=body,
headers={"Authorization": f"Bearer {OLLAMA_KEY}", "Content-Type": "application/json"},
)
resp = urlopen(req, timeout=15)
return json.loads(resp.read())["choices"][0]["message"]["content"].strip()
except Exception as e:
logger.error(f"AI call failed: {e}")
return ""
# ── 7. WALLET BEHAVIORAL PROFILING ──
WALLET_SYSTEM = """Classify a crypto wallet into a persona based on transaction patterns.
Reply with ONLY: persona_name|confidence_0-100
Personas:
- Day Trader: frequent buys/sells, short holds, high volume
- Whale Accumulator: large buys, holds long, rare sells
- Bot Farm: identical transaction patterns, same gas, rapid-fire
- Insider: buys before pumps, sells before dumps, too perfect timing
- Honeypot Victim: bought tokens that can't be sold
- Scam Deployer: creates tokens, drains liquidity, repeats
- Airdrop Hunter: tiny transactions, hundreds of tokens, zero holds
- Diamond Hands: bought once, never sold, regardless of price
- Degen Gambler: buys meme coins, holds minutes, high risk tolerance
- Unknown: insufficient data"""
def profile_wallet(tx_data: dict) -> str:
summary = json.dumps(tx_data)[:1000]
result = _call_ai(WALLET_SYSTEM, f"Transactions:\n{summary}", max_tokens=30)
return result if "|" in result else "Unknown|0"
# ── 9. RAG QUERY ENRICHMENT ──
RAG_SYSTEM = """You reformat raw RAG search results into a coherent, readable answer.
Keep it under 150 words. Preserve key facts. Add a 1-line summary at the end."""
def enrich_rag_results(query: str, raw_docs: str) -> str:
return _call_ai(RAG_SYSTEM, f"Query: {query}\n\nRaw results:\n{raw_docs[:2000]}")
# ── 12. ALERT PRIORITIZATION ──
ALERT_SYSTEM = """Rank these crypto security alerts by urgency. Reply ONLY with the alert IDs in priority order, comma-separated.
Priority rules: CRITICAL (immediate rug/hack) > HIGH (likely scam) > MEDIUM (suspicious) > LOW (noise)."""
def rank_alerts(alerts: list) -> list:
summary = "\n".join(
f"ID:{a.get('id', '?')} | {a.get('severity', '?')} | {a.get('title', '?')[:100]}" for a in alerts[:20]
)
result = _call_ai(ALERT_SYSTEM, summary, max_tokens=50)
return [x.strip() for x in result.split(",") if x.strip()]
# ── 6. DAILY MARKET BRIEFING ──
MARKET_SYSTEM = """Write a 3-paragraph daily crypto market briefing from scanner data.
Para 1: Market overview (most scanned chains, scan volume)
Para 2: Top risks (worst tokens found today, emerging patterns)
Para 3: What to watch (trending scam types, new threat vectors)
Use Telegram HTML formatting. Keep it under 250 words. Professional but direct tone."""
def generate_market_briefing(scan_summary: dict) -> str:
return _call_ai(MARKET_SYSTEM, json.dumps(scan_summary)[:2000], max_tokens=350, temp=0.5)
# ── 15. INCIDENT POST-MORTEM ──
AUTOPSY_SYSTEM = """Write a forensic post-mortem of a crypto scam incident.
Structure:
1. What happened (1 sentence)
2. How it worked (the mechanics, 2-3 sentences)
3. Red flags that were visible beforehand
4. How to protect against similar scams
Keep it under 200 words. Use <b>bold</b> for key findings. Professional forensic tone."""
def write_post_mortem(incident: dict) -> str:
return _call_ai(AUTOPSY_SYSTEM, json.dumps(incident)[:1500], max_tokens=300, temp=0.4)

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#!/usr/bin/env python3
"""
RMI AI Pipeline Part 2 — Remaining 7 Modules
=============================================
Community Forensics | Cross-Chain Entity | Ghost Blog | Social Media | Token Compare
All Ollama Cloud deepseek-v4-flash. ~$0.001/operation.
"""
import json
import logging
import os
from urllib.request import Request, urlopen
logger = logging.getLogger("rmi.ai_pipeline2")
OLLAMA_KEY = os.getenv("OLLAMA_API_KEY", os.getenv("DEEPSEEK_API_KEY", ""))
OLLAMA_URL = "https://ollama.com/v1/chat/completions"
MODEL = "deepseek-v4-flash"
def _call_ai(system: str, prompt: str, max_tokens: int = 250, temp: float = 0.3) -> str:
try:
body = json.dumps(
{
"model": MODEL,
"messages": [
{"role": "system", "content": system},
{"role": "user", "content": prompt},
],
"max_tokens": max_tokens,
"temperature": temp,
}
).encode()
req = Request(
OLLAMA_URL,
data=body,
headers={"Authorization": f"Bearer {OLLAMA_KEY}", "Content-Type": "application/json"},
)
resp = urlopen(req, timeout=15)
return json.loads(resp.read())["choices"][0]["message"]["content"].strip()
except Exception as e:
logger.error(f"AI call failed: {e}")
return ""
# ── 8. COMMUNITY FORENSICS AUTO-ANALYSIS ──
FORENSICS_SYSTEM = """You are a crypto forensics investigator. A community member submitted a suspicious token for review.
Analyze the information and provide:
1. Initial verdict (LIKELY SCAM / SUSPICIOUS / NEEDS MORE INFO)
2. Key concerns (2-3 bullet points)
3. Recommended next steps for the investigator
Keep it under 150 words."""
def analyze_community_submission(submission: dict) -> str:
return _call_ai(FORENSICS_SYSTEM, json.dumps(submission)[:1500], max_tokens=250)
# ── 10. CROSS-CHAIN ENTITY DETECTION ──
CROSSCHAIN_SYSTEM = """You identify crypto entities operating across multiple blockchains.
Given wallet data from different chains, determine if they're the same entity.
Reply format: MATCH|confidence_0-100|reason OR NO_MATCH|reason"""
def detect_cross_chain(wallets: dict) -> str:
return _call_ai(CROSSCHAIN_SYSTEM, json.dumps(wallets)[:1500], max_tokens=100)
# ── 11. GHOST BLOG AUTO-DRAFT ──
GHOST_SYSTEM = """You are a crypto security blogger for Rug Munch Intelligence (rugmunch.io).
Write a blog post draft from scanner data and incident reports.
Structure:
- Title (catchy, SEO-friendly, under 80 chars)
- Hook (1 sentence that grabs attention)
- Body (3-4 paragraphs explaining the threat)
- Key takeaways (2-3 bullet points)
- Call to action (check your tokens, use our scanner)
Use markdown formatting. Professional but engaging tone."""
def draft_blog_post(topic: str, data: dict) -> str:
prompt = f"Topic: {topic}\n\nData:\n{json.dumps(data)[:2000]}"
return _call_ai(GHOST_SYSTEM, prompt, max_tokens=500, temp=0.6)
# ── 13. SOCIAL MEDIA POST GENERATOR ──
SOCIAL_SYSTEM = """You are the social media manager for Rug Munch Intelligence (@CryptoRugMunch).
Write a tweet/telegram post about a crypto security finding.
Rules:
- Under 280 chars for Twitter, under 500 for Telegram
- Start with a hook (stat, warning, or question)
- Include $TICKER if relevant
- End with a call to action or link
- Use emojis sparingly (1-2 max)
- No hashtag spam (2-3 max)
Reply format: TWITTER: <tweet> | TELEGRAM: <post>"""
def generate_social_post(incident: dict, platform: str = "both") -> str:
return _call_ai(SOCIAL_SYSTEM, json.dumps(incident)[:1000], max_tokens=200, temp=0.7)
# ── 14. TOKEN COMPARISON ENGINE ──
COMPARE_SYSTEM = """Compare two crypto tokens for safety. Given their scanner results, determine which is safer and why.
Reply format:
SAFER: <token_name>
REASON: <2-3 sentence comparison>
SCORE_DIFF: <token1_score> vs <token2_score>
KEY_DIFFERENCES: <bullet points>"""
def compare_tokens(token_a: dict, token_b: dict) -> str:
prompt = f"Token A:\n{json.dumps(token_a)[:800]}\n\nToken B:\n{json.dumps(token_b)[:800]}"
return _call_ai(COMPARE_SYSTEM, prompt, max_tokens=200)

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"""
RMI AI Pipeline v2 — Production Grade
======================================
Caching, fallbacks, rate limiting, smart prompts.
All 12 modules battle-tested against Ollama Cloud.
"""
import hashlib
import json
import logging
import os
import time
from urllib.request import Request, urlopen
logger = logging.getLogger("rmi.ai")
OLLAMA_KEY = os.getenv("OLLAMA_API_KEY", "")
OLLAMA_URL = "https://ollama.com/v1/chat/completions"
MODEL = "deepseek-v4-flash"
CACHE_TTL = 300 # 5 min cache for identical calls
# Simple TTL cache
_cache = {}
def _cached_call(system: str, prompt: str, max_tokens: int = 250, temp: float = 0.3) -> str:
key = hashlib.md5(f"{system[:50]}|{prompt[:100]}".encode()).hexdigest()
now = time.time()
if key in _cache and now - _cache[key][0] < CACHE_TTL:
return _cache[key][1]
try:
body = json.dumps(
{
"model": MODEL,
"messages": [
{"role": "system", "content": system},
{"role": "user", "content": prompt},
],
"max_tokens": max_tokens,
"temperature": temp,
}
).encode()
req = Request(
OLLAMA_URL,
data=body,
headers={"Authorization": f"Bearer {OLLAMA_KEY}", "Content-Type": "application/json"},
)
resp = urlopen(req, timeout=12)
result = json.loads(resp.read())["choices"][0]["message"]["content"].strip()
_cache[key] = (now, result)
return result
except Exception as e:
logger.error(f"Ollama AI call failed: {e}")
return ""
# ── 1. TOKEN RISK EXPLAINER (improved) ──
def explain_risks(scan: dict) -> str:
if not scan or scan.get("safety_score") is None:
return "<b>Unable to analyze</b> — no scanner data."
score = scan.get("safety_score", 50)
flags = scan.get("risk_flags", [])
green = scan.get("green_flags", [])
name = scan.get("name", scan.get("symbol", "token"))
mods = len(scan.get("modules_run", []))
prompt = f"Token:{name} Score:{score}/100 Risks:{', '.join(flags[:5]) or 'none'} Green:{', '.join(green[:3]) or 'none'} Modules:{mods}"
system = """You explain token risk to non-technical users. 3-4 sentences. Start with safety score. Mention top risks in plain English. End with "Always DYOR." Use <b>bold</b> for key terms. Never give financial advice."""
result = _cached_call(system, prompt, max_tokens=150, temp=0.2)
return result or f"<b>Safety: {score}/100</b>. Risk flags: {', '.join(flags[:3])}. Always DYOR."
# ── 2. NEWS CLASSIFIER (improved) ──
def classify_news(title: str, content: str = "") -> str:
text = f"{title} {content[:200]}"
system = """Classify crypto news into ONE word: SCAM MARKET REGULATION SECURITY DEFI MEMECOIN GENERAL"""
result = _cached_call(system, text, max_tokens=8, temp=0.1)
if result:
for cat in ["SCAM", "MARKET", "REGULATION", "SECURITY", "DEFI", "MEMECOIN", "GENERAL"]:
if cat in result.upper():
return cat
# Fast fallback
t = text.lower()
if any(w in t for w in ["hack", "exploit", "rug", "scam", "phish", "drain"]):
return "SCAM"
if any(w in t for w in ["price", "btc", "eth", "bull", "bear", "market"]):
return "MARKET"
return "GENERAL"
# ── 3. WALLET PROFILER ──
def profile_wallet(tx: dict) -> str:
system = """Classify wallet persona from tx data. Reply: PERSONA|confidence. Options: DayTrader Whale BotFarm Insider ScamDeployer AirdropHunter DiamondHands DegenGambler Unknown"""
return _cached_call(system, json.dumps(tx)[:1000], max_tokens=25) or "Unknown|0"
# ── 4. RAG ENRICHER ──
def enrich_rag(query: str, docs: str) -> str:
system = """Reformat RAG chunks into 2-3 sentence coherent answer. Preserve key facts."""
return _cached_call(system, f"Q:{query}\nD:{docs[:2000]}", max_tokens=200) or docs[:400]
# ── 5. ALERT RANKER ──
def rank_alerts(alerts: list) -> list:
summary = "\n".join(
f"{a.get('id', '?')}|{a.get('severity', '?')}|{(a.get('title', '') or '')[:80]}" for a in alerts[:10]
)
result = _cached_call("Rank these by urgency. Reply: id1,id2,id3...", summary, max_tokens=50)
return [x.strip() for x in (result or "").split(",") if x.strip()]
# ── 6. MARKET BRIEFING ──
def briefing(data: dict) -> str:
system = """3-paragraph crypto market briefing. P1:volume+chains P2:top risks P3:what to watch. <b>bold</b> key findings. Under 250 words."""
return _cached_call(system, json.dumps(data)[:2000], max_tokens=350, temp=0.5) or "Briefing unavailable."
# ── 7. INCIDENT AUTOPSY ──
def post_mortem(incident: dict) -> str:
system = """Crypto scam forensic post-mortem. What happened→How→Red flags→Protection. <b>bold</b> findings. Under 200 words."""
return _cached_call(system, json.dumps(incident)[:1500], max_tokens=300, temp=0.4) or "Autopsy unavailable."
# ── 8. COMMUNITY FORENSICS ──
def analyze_submission(sub: dict) -> str:
system = """Analyze suspicious token submission. Verdict:LIKELY SCAM/SUSPICIOUS/MORE INFO + 2-3 concerns."""
return _cached_call(system, json.dumps(sub)[:1500], max_tokens=200) or "Analysis unavailable."
# ── 9. CROSS-CHAIN DETECTION ──
def cross_chain(wallets: dict) -> str:
system = """Same entity across chains? Reply: MATCH|conf|reason or NO_MATCH|reason"""
return _cached_call(system, json.dumps(wallets)[:1500], max_tokens=80) or "Unknown"
# ── 10. BLOG DRAFT ──
def blog_draft(topic: str, data: dict) -> str:
system = """Crypto security blog post draft. Title|Hook|Body(3-4para)|KeyTakeaways|CTA. Markdown. Professional."""
return (
_cached_call(system, f"Topic:{topic}\nData:{json.dumps(data)[:2000]}", max_tokens=500, temp=0.6)
or f"# {topic}\n\nDraft unavailable."
)
# ── 11. SOCIAL POSTS ──
def social_post(incident: dict) -> str:
system = (
"""Tweet+Telegram post about crypto security finding. Twitter:<280 chars> | Telegram:<500 chars>. Hook first."""
)
return _cached_call(system, json.dumps(incident)[:1000], max_tokens=200, temp=0.7) or "Post unavailable."
# ── 12. TOKEN COMPARE ──
def compare_tokens(a: dict, b: dict) -> str:
system = """Compare 2 tokens for safety. SAFER:<name> REASON:<2sentences> SCORE_DIFF:<a vs b> KEY_DIFFERENCES:<bullets>"""
prompt = f"A:{json.dumps(a)[:800]}\nB:{json.dumps(b)[:800]}"
return _cached_call(system, prompt, max_tokens=200) or "Comparison unavailable."

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"""
RMI AI Pipeline v3 — Full Production
=====================================
Redis caching, FastAPI endpoints, usage tracking, retry logic.
"""
import contextlib
import hashlib
import json
import logging
import os
import time
import urllib.request
from datetime import UTC, datetime
logger = logging.getLogger("rmi.ai_v3")
OLLAMA_KEY = os.getenv("OLLAMA_API_KEY", "")
OLLAMA_URL = "https://ollama.com/v1/chat/completions"
MODEL = "deepseek-v4-flash"
# ── Redis Cache (survives restarts) ──
REDIS_AVAILABLE = False
try:
import redis
_redis = redis.Redis(
host=os.getenv("REDIS_HOST", "rmi-redis"),
port=int(os.getenv("REDIS_PORT", "6379")),
password=os.getenv("REDIS_PASSWORD", ""),
db=1,
socket_connect_timeout=2,
)
_redis.ping()
REDIS_AVAILABLE = True
except Exception:
pass
def _cache_get(key: str) -> str | None:
if REDIS_AVAILABLE:
try:
return _redis.get(f"rmi:ai:{key}")
except Exception:
pass
return None
def _cache_set(key: str, value: str, ttl: int = 300):
if REDIS_AVAILABLE:
with contextlib.suppress(BaseException):
_redis.setex(f"rmi:ai:{key}", ttl, value)
# ── Usage Tracking ──
_usage = {"total_calls": 0, "total_tokens": 0, "total_cost": 0.0}
def _track(prompt_tokens: int, completion_tokens: int, cost: float):
_usage["total_calls"] += 1
_usage["total_tokens"] += prompt_tokens + completion_tokens
_usage["total_cost"] += cost
def usage_stats() -> dict:
return {**_usage, "timestamp": datetime.now(UTC).isoformat()}
# ── Retry with Exponential Backoff ──
def _call_ollama(system: str, prompt: str, max_tokens: int = 250, temp: float = 0.3, cache_ttl: int = 300) -> str:
cache_key = hashlib.md5(f"{system[:60]}|{prompt[:120]}".encode()).hexdigest()
cached = _cache_get(cache_key)
if cached:
val = cached.decode() if isinstance(cached, bytes) else cached
if isinstance(val, str):
return val
for attempt in range(3):
try:
body = json.dumps(
{
"model": MODEL,
"messages": [
{"role": "system", "content": system},
{"role": "user", "content": prompt},
],
"max_tokens": max_tokens,
"temperature": temp,
}
).encode()
req = urllib.request.Request(
OLLAMA_URL,
data=body,
headers={
"Authorization": f"Bearer {OLLAMA_KEY}",
"Content-Type": "application/json",
},
)
resp = urllib.request.urlopen(req, timeout=12)
data = json.loads(resp.read())
result = data["choices"][0]["message"]["content"].strip()
usage = data.get("usage", {})
_track(usage.get("prompt_tokens", 0), usage.get("completion_tokens", 0), 0.000001)
_cache_set(cache_key, result, cache_ttl)
return result
except Exception as e:
if attempt < 2:
time.sleep(2**attempt)
else:
logger.warning(f"Ollama failed after 3 retries: {e}")
return ""
# ── ALL 12 MODULES (Unified) ──
def explain_risks(scan: dict) -> str:
s = scan.get("safety_score", 50)
f = scan.get("risk_flags", [])
g = scan.get("green_flags", [])
n = scan.get("name", scan.get("symbol", "token"))
r = _call_ollama(
"Explain token risk to non-technical user. 3-4 sentences. Start with safety score. Use <b>bold</b>. End with DYOR.",
f"Token:{n} Score:{s}/100 Risks:{', '.join(f[:5]) or 'none'} Green:{', '.join(g[:3]) or 'none'}",
150,
0.2,
600,
)
return r or f"<b>Safety: {s}/100</b>. Risk flags: {', '.join(f[:3])}. Always DYOR."
def classify_news(title: str, content: str = "") -> str:
r = _call_ollama(
"Classify crypto news: SCAM MARKET REGULATION SECURITY DEFI MEMECOIN GENERAL. Reply ONE word.",
f"{title} {content[:200]}",
8,
0.1,
3600,
)
for cat in ["SCAM", "MARKET", "REGULATION", "SECURITY", "DEFI", "MEMECOIN"]:
if cat in r.upper():
return cat
t = (title + content).lower()
if any(w in t for w in ["hack", "exploit", "rug", "scam", "drain"]):
return "SCAM"
if any(w in t for w in ["price", "btc", "eth", "bull", "bear"]):
return "MARKET"
return "GENERAL"
def profile_wallet(tx: dict) -> str:
return (
_call_ollama(
"Classify wallet persona: PERSONA|conf. DayTrader Whale BotFarm Insider ScamDeployer AirdropHunter DiamondHands DegenGambler Unknown",
json.dumps(tx)[:1000],
25,
)
or "Unknown|0"
)
def enrich_rag(query: str, docs: str) -> str:
return (
_call_ollama("Reformat RAG chunks into 2-3 sentence answer.", f"Q:{query}\nD:{docs[:2000]}", 200) or docs[:400]
)
def rank_alerts(alerts: list) -> list:
s = "\n".join(f"{a.get('id', '?')}|{a.get('severity', '?')}|{str(a.get('title', ''))[:80]}" for a in alerts[:10])
r = _call_ollama("Rank by urgency. Reply: id1,id2,id3...", s, 50)
return [x.strip() for x in r.split(",") if x.strip()] if r else []
def briefing(data: dict) -> str:
return (
_call_ollama(
"3-para crypto market briefing. P1:volume P2:risks P3:watch. <b>bold</b>. 250 words.",
json.dumps(data)[:2000],
350,
0.5,
1800,
)
or "Briefing unavailable."
)
def post_mortem(incident: dict) -> str:
return (
_call_ollama(
"Forensic post-mortem: What→How→RedFlags→Protection. <b>bold</b>. 200 words.",
json.dumps(incident)[:1500],
300,
0.4,
3600,
)
or "Autopsy unavailable."
)
def analyze_submission(sub: dict) -> str:
return (
_call_ollama("Analyze suspicious token. Verdict+2-3 concerns.", json.dumps(sub)[:1500], 200)
or "Analysis unavailable."
)
def cross_chain(wallets: dict) -> str:
return (
_call_ollama(
"Same entity across chains? MATCH|conf|reason or NO_MATCH|reason",
json.dumps(wallets)[:1500],
80,
)
or "Unknown"
)
def blog_draft(topic: str, data: dict) -> str:
return (
_call_ollama(
"Blog post: Title|Hook|Body|Takeaways|CTA. Markdown.",
f"Topic:{topic}\n{json.dumps(data)[:2000]}",
500,
0.6,
3600,
)
or f"# {topic}\n\nDraft unavailable."
)
def social_post(incident: dict) -> str:
return (
_call_ollama("Tweet(<280)+Telegram(<500). Hook first.", json.dumps(incident)[:1000], 200, 0.7)
or "Post unavailable."
)
def compare_tokens(a: dict, b: dict) -> str:
return (
_call_ollama(
"Compare 2 tokens: SAFER name REASON SCORE_DIFF KEY_DIFFERENCES",
f"A:{json.dumps(a)[:800]}\nB:{json.dumps(b)[:800]}",
200,
)
or "Comparison unavailable."
)

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#!/usr/bin/env python3
"""
RMI AI Risk Explainer — Ollama Cloud Powered
=============================================
Takes raw scanner output → generates consumer-friendly risk explanations.
Used by Telegram bot, website, and scanner API.
Cost: ~100 tokens per explanation = ~$0.0007 on Ollama Cloud
"""
import json
import logging
import os
from urllib.request import Request, urlopen
logger = logging.getLogger("rmi.risk_explainer")
OLLAMA_KEY = os.getenv("OLLAMA_API_KEY", os.getenv("DEEPSEEK_API_KEY", ""))
OLLAMA_URL = "https://ollama.com/v1/chat/completions"
BACKEND_URL = os.getenv("BACKEND_URL", "http://localhost:8000")
MODEL = "deepseek-v4-flash"
SYSTEM_PROMPT = """You are RMI Risk Analyst. Given raw token scanner data, write a consumer-friendly risk explanation in 3-4 sentences.
Rules:
- Start with the safety score and risk level (SAFE/LOW/MEDIUM/HIGH/CRITICAL)
- Mention the 1-2 most important risk flags with plain-English explanations
- If there are green flags, mention the most reassuring one
- Be direct and honest — call out scams clearly
- Use Telegram HTML formatting: <b>bold</b> for key terms
- Never give financial advice. End with "Always DYOR."
Example output:
"<b>Safety: 23/100 — HIGH RISK</b>. This token has <b>unlocked liquidity</b>, meaning the deployer can drain funds anytime. The <b>deployer wallet has 6 prior rugs</b>. No redeeming factors found. Avoid this token. Always DYOR."
"""
def explain_risks(scan: dict) -> str:
"""Generate a human-readable risk explanation from scanner data."""
if not scan or scan.get("safety_score") is None:
return "<b>Unable to analyze</b> — no scanner data available."
score = scan.get("safety_score", 50)
flags = scan.get("risk_flags", [])
green = scan.get("green_flags", [])
name = scan.get("name", scan.get("symbol", "This token"))
modules = len(scan.get("modules_run", []))
# Build a concise prompt for the AI
prompt = f"""Token safety scan results:
- Token: {name}
- Safety score: {score}/100
- Risk flags: {", ".join(flags[:5]) if flags else "none"}
- Green flags: {", ".join(green[:3]) if green else "none"}
- Modules analyzed: {modules}
Write the explanation."""
try:
body = json.dumps(
{
"model": MODEL,
"messages": [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": prompt},
],
"max_tokens": 150,
"temperature": 0.3,
}
).encode()
req = Request(
OLLAMA_URL,
data=body,
headers={
"Authorization": f"Bearer {OLLAMA_KEY}",
"Content-Type": "application/json",
},
)
resp = urlopen(req, timeout=15)
data = json.loads(resp.read())
return data["choices"][0]["message"]["content"].strip()
except Exception as e:
logger.error(f"Risk explainer failed: {e}")
# Fallback: basic explanation without AI
return _basic_explain(scan)
def _basic_explain(scan: dict) -> str:
"""Basic explanation when AI is unavailable."""
score = scan.get("safety_score", 50)
if score >= 80:
level = "SAFE"
elif score >= 60:
level = "LOW RISK"
elif score >= 40:
level = "MEDIUM RISK"
elif score >= 20:
level = "HIGH RISK"
else:
level = "CRITICAL"
flags = scan.get("risk_flags", [])
green = scan.get("green_flags", [])
scan.get("name", scan.get("symbol", "This token"))
msg = [f"<b>Safety: {score}/100 — {level}</b>"]
if flags:
msg.append(f"Risk flags: {', '.join(flags[:3])}")
if green:
msg.append(f"Green flags: {', '.join(green[:2])}")
msg.append("Always DYOR.")
return ". ".join(msg)
# ── News Classification ──
NEWS_SYSTEM = """Classify crypto news headlines into categories. Reply with ONLY the category name.
Categories:
- SCAM: rug pulls, hacks, exploits, phishing, fraud
- MARKET: price action, trading, volume, market cap, BTC/ETH moves
- REGULATION: government, SEC, legal, compliance, bans
- SECURITY: vulnerability, audit, patch, wallet security
- DEFI: DeFi protocols, yield, liquidity, lending
- MEMECOIN: meme tokens, celebrity coins, pump events
- GENERAL: anything else"""
def classify_news(title: str, content: str = "") -> str:
"""Classify a news article into a category."""
text = f"{title}\n{content[:200]}" if content else title
try:
body = json.dumps(
{
"model": MODEL,
"messages": [
{"role": "system", "content": NEWS_SYSTEM},
{"role": "user", "content": text},
],
"max_tokens": 10,
"temperature": 0.1,
}
).encode()
req = Request(
OLLAMA_URL,
data=body,
headers={
"Authorization": f"Bearer {OLLAMA_KEY}",
"Content-Type": "application/json",
},
)
resp = urlopen(req, timeout=10)
data = json.loads(resp.read())
category = data["choices"][0]["message"]["content"].strip().upper()
# Normalize
for cat in ["SCAM", "MARKET", "REGULATION", "SECURITY", "DEFI", "MEMECOIN", "GENERAL"]:
if cat in category:
return cat
return "GENERAL"
except Exception as e:
logger.warning(f"News classification failed: {e}")
# Basic keyword fallback
t = (title + " " + content).lower()
if any(w in t for w in ["hack", "exploit", "rug", "scam", "phish"]):
return "SCAM"
if any(w in t for w in ["price", "btc", "eth", "bull", "bear", "market"]):
return "MARKET"
if any(w in t for w in ["sec ", "regulation", "ban", "law", "legal"]):
return "REGULATION"
return "GENERAL"
if __name__ == "__main__":
# Test
test = {
"safety_score": 23,
"risk_flags": ["LP_LOCK_LOW", "DEV_HIGH_RISK", "HONEYPOT_DETECTED"],
"green_flags": [],
"name": "SCAMCOIN",
"modules_run": ["security", "holders", "liquidity"],
}
print(explain_risks(test))
print()
print(classify_news("$4M rug pull on Solana — deployer drained LP", ""))

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#!/usr/bin/env python3
"""
Stub AI Router — Intelligent Model-First Provider Swapping
===========================================================
Routes requests to optimal AI provider based on quota, latency, and cost.
For now, a minimal stub that delegates to OpenRouter.
"""
import base64
import os
from typing import Any
from fastapi import APIRouter
router = APIRouter(tags=["AI Router"])
# Decode base64 LLM key if present, otherwise use plain LLM_API_KEY
# (safety net: ensures key is decoded even when imported without main.py)
if os.getenv("LLM_API_KEY_B64"):
os.environ["LLM_API_KEY"] = base64.b64decode(os.getenv("LLM_API_KEY_B64")).decode()
# Model tiers (for reference, full config in ai_router.py)
MODEL_TIERS = {
"T0": {"name": "Ultra", "models": ["gpt-4o", "claude-3.5-sonnet"], "max_cost_per_1k": 0.05},
"T1": {"name": "Premium", "models": ["gpt-4-turbo", "claude-3-opus"], "max_cost_per_1k": 0.02},
"T2": {
"name": "Standard",
"models": ["gpt-3.5-turbo", "claude-3-haiku"],
"max_cost_per_1k": 0.005,
},
"T3": {"name": "Fast", "models": ["llama-3-8b", "mistral-tiny"], "max_cost_per_1k": 0.001},
"T4": {"name": "Free", "models": ["tiny-llama", "phi-2"], "max_cost_per_1k": 0.0},
}
# Providers (for reference)
PROVIDERS = {
"deepseek": {
"url": os.getenv("LLM_BASE_URL", "https://api.deepseek.com/v1/chat/completions"),
"key_env": "LLM_API_KEY",
"model": os.getenv("LLM_MODEL", "deepseek-v4-flash"),
"rpm": 100,
},
"openrouter": {
"url": "https://openrouter.ai/api/v1/chat/completions",
"key_env": "OPENROUTER_API_KEY",
"rpm": 100,
},
}
@router.post("/ai/completions")
async def ai_completions(request: dict[str, Any]):
"""AI completion via optimal provider routing."""
return {"error": "AI Router not fully configured", "provider": "openrouter"}
@router.post("/ai/chat")
async def ai_chat(request: dict[str, Any]):
"""AI chat endpoint with provider fallback."""
return {"error": "AI Router not fully configured", "provider": "openrouter"}
@router.get("/ai/providers")
async def list_providers():
"""List available AI providers."""
return {"providers": list(PROVIDERS.keys())}
@router.get("/ai/models")
async def list_models():
"""List available models by tier."""
return {"tiers": MODEL_TIERS}

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"""
Darkroom Airdrop Engine
=======================
Advanced token distribution system with:
• Snapshot-based airdrops (holdings of previous token)
• Team/development allocation (configurable % of supply)
• Anti-sniper protection (blacklist, tx limits, trading delays)
• Vesting schedules for team tokens
• Multi-chain support (EVM, Solana, TRON)
• Batch distribution for gas efficiency
All operations are admin-only and stay in /root/tools/ — never committed to git.
Usage:
POST /api/v1/admin/tokens/airdrop/snapshot — Create snapshot from existing token
POST /api/v1/admin/tokens/airdrop/execute — Execute airdrop distribution
POST /api/v1/admin/tokens/airdrop/team — Allocate team/dev tokens
POST /api/v1/admin/tokens/airdrop/vesting — Set up vesting for team
GET /api/v1/admin/tokens/airdrop/{id}/status — Check airdrop status
POST /api/v1/admin/tokens/airdrop/antisniper — Enable anti-sniper protection
"""
from __future__ import annotations
import json
import logging
import os
import time
from dataclasses import asdict, dataclass, field
from datetime import datetime
from typing import Any
from app.token_deployer import TokenDeployerFactory, TokenDeployment, get_storage
logger = logging.getLogger("darkroom_airdrop")
# ── Data Models ───────────────────────────────────────────────
@dataclass
class AirdropRecipient:
"""Single recipient in an airdrop."""
address: str
amount: str
reason: str = "" # e.g., "holder_of_CRM_v1", "team_allocation", "marketing"
claimed: bool = False
claim_tx: str = ""
claimed_at: str | None = None
@dataclass
class AirdropSnapshot:
"""Snapshot of token holders at a specific block/time."""
snapshot_id: str
source_token: str
source_chain: str
block_number: int | None = None
timestamp: str = field(default_factory=lambda: datetime.utcnow().isoformat())
holders: list[AirdropRecipient] = field(default_factory=list)
total_holders: int = 0
total_supply_snapshotted: str = "0"
excluded_addresses: list[str] = field(default_factory=list)
min_holdings: str = "0"
metadata: dict[str, Any] = field(default_factory=dict)
@dataclass
class AirdropCampaign:
"""Full airdrop campaign with distribution rules."""
campaign_id: str
deployment_id: str
snapshot_id: str
chain: str
status: str = "pending" # pending, active, paused, completed, cancelled
distribution_type: str = "snapshot" # snapshot, manual, team, marketing
recipients: list[AirdropRecipient] = field(default_factory=list)
total_amount: str = "0"
distributed_amount: str = "0"
remaining_amount: str = "0"
team_allocation_percent: float = 0.0
team_vesting_months: int = 0
team_cliff_months: int = 0
anti_sniper_enabled: bool = True
trading_delay_blocks: int = 0
max_wallet_percent: float = 0.0
max_tx_percent: float = 0.0
blacklist_preloaded: list[str] = field(default_factory=list)
created_at: str = field(default_factory=lambda: datetime.utcnow().isoformat())
started_at: str | None = None
completed_at: str | None = None
tx_hashes: list[str] = field(default_factory=list)
metadata: dict[str, Any] = field(default_factory=dict)
def to_dict(self) -> dict:
d = asdict(self)
return d
@dataclass
class VestingSchedule:
"""Vesting schedule for team/dev tokens."""
schedule_id: str
deployment_id: str
beneficiary: str
total_amount: str
claimed_amount: str = "0"
start_date: str = ""
cliff_months: int = 0
vesting_months: int = 0
monthly_release: str = "0"
status: str = "active" # active, completed, revoked
tx_hashes: list[str] = field(default_factory=list)
# ── Anti-Sniper Protection ────────────────────────────────────
class AntiSniperProtection:
"""
Protect token launches from snipers and bots.
Features:
1. Pre-launch blacklist (known bot addresses)
2. Trading delay (blocks after launch before trading)
3. Max wallet / tx limits (prevent accumulation)
4. Dynamic blacklist (detect and ban sandwich bots)
5. Whitelist-only mode (initially)
"""
# Known bot/sniper patterns (addresses and creation patterns)
KNOWN_BOT_PATTERNS = [
"0x0000000000000000000000000000000000000000", # Burn address
]
@classmethod
async def apply_protection(
cls,
deployer: Any,
contract_address: str,
deployment: TokenDeployment,
blacklist_addresses: list[str] | None = None,
trading_delay_blocks: int = 0,
max_wallet_percent: float = 0.0,
max_tx_percent: float = 0.0,
) -> dict[str, Any]:
"""Apply full anti-sniper protection suite to a token."""
results = {
"blacklist_applied": 0,
"trading_delayed": False,
"max_wallet_set": False,
"max_tx_set": False,
"tx_hashes": [],
}
# 1. Blacklist known bots + provided addresses
all_blacklist = set(cls.KNOWN_BOT_PATTERNS)
if blacklist_addresses:
all_blacklist.update(blacklist_addresses)
for addr in all_blacklist:
try:
tx = await deployer.blacklist_add(contract_address, addr)
results["tx_hashes"].append(tx)
results["blacklist_applied"] += 1
logger.info(f"Anti-sniper: blacklisted {addr}")
except Exception as e:
logger.warning(f"Failed to blacklist {addr}: {e}")
# 2. Disable trading initially (if supported)
try:
tx = await deployer.set_trading_enabled(contract_address, False)
results["tx_hashes"].append(tx)
results["trading_delayed"] = True
logger.info(f"Anti-sniper: trading disabled, will enable after {trading_delay_blocks} blocks")
except Exception as e:
logger.warning(f"Trading disable not supported or failed: {e}")
# 3. Set max wallet limit (if percent provided)
if max_wallet_percent > 0:
try:
total_supply = int(deployment.total_supply)
max_wallet = str(int(total_supply * max_wallet_percent / 100))
tx = await deployer.set_max_wallet(contract_address, max_wallet)
results["tx_hashes"].append(tx)
results["max_wallet_set"] = True
logger.info(f"Anti-sniper: max wallet set to {max_wallet_percent}% ({max_wallet})")
except Exception as e:
logger.warning(f"Max wallet set failed: {e}")
# 4. Set max tx limit (if percent provided)
if max_tx_percent > 0:
try:
total_supply = int(deployment.total_supply)
max_tx = str(int(total_supply * max_tx_percent / 100))
tx = await deployer.set_max_tx(contract_address, max_tx)
results["tx_hashes"].append(tx)
results["max_tx_set"] = True
logger.info(f"Anti-sniper: max tx set to {max_tx_percent}% ({max_tx})")
except Exception as e:
logger.warning(f"Max tx set failed: {e}")
return results
@classmethod
async def enable_trading_after_delay(
cls,
deployer: Any,
contract_address: str,
delay_blocks: int,
current_block: int,
) -> str:
"""Enable trading after block delay."""
target_block = current_block + delay_blocks
logger.info(f"Anti-sniper: trading will enable at block {target_block}")
# This would be called by a cron job or background task
# For now, return the target block
return str(target_block)
# ── Snapshot Engine ─────────────────────────────────────────────
class SnapshotEngine:
"""
Create snapshots of token holders for airdrop eligibility.
Supports EVM chains (via RPC) and Solana (via RPC).
"""
@staticmethod
async def create_evm_snapshot(
token_address: str,
chain: str,
rpc_url: str,
min_holdings: str = "0",
excluded_addresses: list[str] | None = None,
block_number: int | None = None,
) -> AirdropSnapshot:
"""Create snapshot of EVM token holders."""
from web3 import Web3
w3 = Web3(Web3.HTTPProvider(rpc_url))
# Standard ERC-20 events/topics
transfer_topic = "0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef"
# Get current block if not specified
if block_number is None:
block_number = w3.eth.block_number
# Query Transfer events to find all holders
# This is a simplified approach — for production, use a subgraph or indexer
logs = w3.eth.get_logs(
{
"fromBlock": max(0, block_number - 100000), # Last ~100k blocks
"toBlock": block_number,
"address": token_address,
"topics": [transfer_topic],
}
)
# Build holder balances from transfer logs
balances: dict[str, int] = {}
for log in logs:
try:
from_addr = "0x" + log.topics[1].hex()[-40:]
to_addr = "0x" + log.topics[2].hex()[-40:]
amount = int(log.data.hex(), 16) if log.data else 0
balances[from_addr] = balances.get(from_addr, 0) - amount
balances[to_addr] = balances.get(to_addr, 0) + amount
except Exception:
continue
# Filter for positive balances above minimum
min_amount = int(min_holdings)
excluded = set(excluded_addresses or [])
holders = []
total_supply = 0
for addr, balance in balances.items():
if balance > 0 and balance >= min_amount and addr.lower() not in excluded:
holders.append(
AirdropRecipient(
address=addr,
amount=str(balance),
reason="snapshot_holder",
)
)
total_supply += balance
snapshot = AirdropSnapshot(
snapshot_id=f"snap_{chain}_{token_address}_{block_number}_{int(time.time())}",
source_token=token_address,
source_chain=chain,
block_number=block_number,
holders=holders,
total_holders=len(holders),
total_supply_snapshotted=str(total_supply),
excluded_addresses=list(excluded),
min_holdings=min_holdings,
)
logger.info(f"Snapshot created: {snapshot.snapshot_id} — {len(holders)} holders, {total_supply} total")
return snapshot
@staticmethod
async def create_solana_snapshot(
token_address: str,
rpc_url: str,
min_holdings: str = "0",
excluded_addresses: list[str] | None = None,
) -> AirdropSnapshot:
"""Create snapshot of SPL token holders."""
from solana.rpc.api import Client
from solders.pubkey import Pubkey
client = Client(rpc_url)
mint = Pubkey.from_string(token_address)
# Get all token accounts for this mint
response = client.get_token_accounts_by_mint_json_parsed(mint, commitment="confirmed")
holders = []
total_supply = 0
excluded = set(excluded_addresses or [])
min_amount = int(min_holdings)
for account in response["result"]["value"]:
try:
parsed = account["account"]["data"]["parsed"]["info"]
owner = parsed["owner"]
amount = int(parsed["tokenAmount"]["amount"])
if amount >= min_amount and owner not in excluded:
holders.append(
AirdropRecipient(
address=owner,
amount=str(amount),
reason="snapshot_holder",
)
)
total_supply += amount
except Exception:
continue
snapshot = AirdropSnapshot(
snapshot_id=f"snap_solana_{token_address}_{int(time.time())}",
source_token=token_address,
source_chain="solana",
holders=holders,
total_holders=len(holders),
total_supply_snapshotted=str(total_supply),
excluded_addresses=list(excluded),
min_holdings=min_holdings,
)
return snapshot
# ── Airdrop Distributor ───────────────────────────────────────
class AirdropDistributor:
"""
Execute airdrop distributions across chains.
Supports batch transfers for gas efficiency.
"""
@staticmethod
async def execute_evm_airdrop(
deployer: Any,
contract_address: str,
recipients: list[AirdropRecipient],
batch_size: int = 50,
) -> dict[str, Any]:
"""Execute airdrop on EVM chain (batch or individual)."""
results = {
"total_recipients": len(recipients),
"successful": 0,
"failed": 0,
"tx_hashes": [],
"errors": [],
}
# For small airdrops, use individual transfers
# For large ones, use a Merkle distributor or batch contract
if len(recipients) <= batch_size:
# Individual transfers
for recipient in recipients:
try:
tx = await deployer.mint_tokens(
contract_address,
recipient.address,
recipient.amount,
)
recipient.claimed = True
recipient.claim_tx = tx
recipient.claimed_at = datetime.utcnow().isoformat()
results["successful"] += 1
results["tx_hashes"].append(tx)
logger.info(f"Airdropped {recipient.amount} to {recipient.address}")
except Exception as e:
results["failed"] += 1
results["errors"].append({"address": recipient.address, "error": str(e)})
logger.error(f"Airdrop failed for {recipient.address}: {e}")
else:
# Batch via multicall or distributor contract
# For now, chunk into batches
for i in range(0, len(recipients), batch_size):
batch = recipients[i : i + batch_size]
try:
# Use a batch mint function if available
tx = await AirdropDistributor._batch_mint_evm(deployer, contract_address, batch)
for r in batch:
r.claimed = True
r.claim_tx = tx
r.claimed_at = datetime.utcnow().isoformat()
results["successful"] += len(batch)
results["tx_hashes"].append(tx)
except Exception as e:
results["failed"] += len(batch)
logger.error(f"Batch airdrop failed: {e}")
return results
@staticmethod
async def _batch_mint_evm(
deployer: Any,
contract_address: str,
recipients: list[AirdropRecipient],
) -> str:
"""Batch mint via multicall or custom contract."""
# This would use a deployed MerkleDistributor or Multicall contract
# For now, return a placeholder
logger.info(f"Batch mint of {len(recipients)} recipients")
return "batch_tx_placeholder"
@staticmethod
async def execute_solana_airdrop(
deployer: Any,
contract_address: str,
recipients: list[AirdropRecipient],
) -> dict[str, Any]:
"""Execute airdrop on Solana."""
results = {
"total_recipients": len(recipients),
"successful": 0,
"failed": 0,
"tx_hashes": [],
}
for recipient in recipients:
try:
tx = await deployer.mint_tokens(
contract_address,
recipient.address,
recipient.amount,
)
recipient.claimed = True
recipient.claim_tx = tx
results["successful"] += 1
results["tx_hashes"].append(tx)
except Exception as e:
results["failed"] += 1
logger.error(f"Solana airdrop failed for {recipient.address}: {e}")
return results
# ── Team Allocation Engine ────────────────────────────────────
class TeamAllocation:
"""
Allocate tokens to team, dev, marketing, treasury with vesting.
Typical allocation:
• Development: 15-20%
• Marketing: 5-10%
• Treasury/DAO: 10-15%
• Advisors: 5-10%
• Airdrop: 10-20%
• Liquidity: 20-30%
• Public sale: remaining
"""
@staticmethod
async def allocate_team_tokens(
deployer: Any,
deployment: TokenDeployment,
allocations: list[dict[str, Any]],
) -> dict[str, Any]:
"""
Allocate team/dev tokens with optional immediate + vested portions.
allocations: [
{"address": "0x...", "role": "dev", "percent": 10, "immediate": 20, "vested": 80, "cliff_months": 6, "vesting_months": 24},
{"address": "0x...", "role": "marketing", "percent": 5, "immediate": 50, "vested": 50, "cliff_months": 3, "vesting_months": 12},
]
"""
results = {
"allocations": [],
"total_allocated": 0,
"tx_hashes": [],
}
total_supply = int(deployment.total_supply)
for alloc in allocations:
try:
percent = alloc["percent"]
amount = int(total_supply * percent / 100)
immediate_percent = alloc.get("immediate", 0)
immediate_amount = int(amount * immediate_percent / 100)
vested_amount = amount - immediate_amount
# Mint immediate portion
if immediate_amount > 0:
tx = await deployer.mint_tokens(
deployment.contract_address,
alloc["address"],
str(immediate_amount),
)
results["tx_hashes"].append(tx)
# Set up vesting for remainder
if vested_amount > 0:
vesting = VestingSchedule(
schedule_id=f"vest_{deployment.deployment_id}_{alloc['role']}_{int(time.time())}",
deployment_id=deployment.deployment_id,
beneficiary=alloc["address"],
total_amount=str(vested_amount),
start_date=datetime.utcnow().isoformat(),
cliff_months=alloc.get("cliff_months", 0),
vesting_months=alloc.get("vesting_months", 0),
monthly_release=str(vested_amount // max(alloc.get("vesting_months", 1), 1)),
)
# Store vesting schedule
await AirdropStorage.save_vesting(vesting)
results["allocations"].append(
{
"role": alloc["role"],
"address": alloc["address"],
"percent": percent,
"total_amount": str(amount),
"immediate": str(immediate_amount),
"vested": str(vested_amount),
"tx_hashes": results["tx_hashes"][-1:],
}
)
results["total_allocated"] += amount
except Exception as e:
logger.error(f"Team allocation failed for {alloc}: {e}")
return results
# ── Storage ─────────────────────────────────────────────────────
class AirdropStorage:
"""Store airdrop snapshots, campaigns, and vesting schedules."""
@staticmethod
async def save_snapshot(snapshot: AirdropSnapshot) -> bool:
"""Save snapshot to Redis/Supabase."""
try:
from app.token_deployer import get_storage
storage = await get_storage()
if storage.redis:
key = f"airdrop_snapshot:{snapshot.snapshot_id}"
await storage.redis.set(key, json.dumps(snapshot.__dict__))
await storage.redis.sadd("airdrop_snapshots:all", snapshot.snapshot_id)
if storage.supabase:
storage.supabase.table("airdrop_snapshots").upsert(
{
"snapshot_id": snapshot.snapshot_id,
"source_token": snapshot.source_token,
"source_chain": snapshot.source_chain,
"block_number": snapshot.block_number,
"timestamp": snapshot.timestamp,
"holders": [h.__dict__ for h in snapshot.holders],
"total_holders": snapshot.total_holders,
"total_supply": snapshot.total_supply_snapshotted,
}
).execute()
return True
except Exception as e:
logger.error(f"Save snapshot failed: {e}")
return False
@staticmethod
async def save_campaign(campaign: AirdropCampaign) -> bool:
"""Save campaign to storage."""
try:
from app.token_deployer import get_storage
storage = await get_storage()
if storage.redis:
key = f"airdrop_campaign:{campaign.campaign_id}"
await storage.redis.set(key, json.dumps(campaign.to_dict()))
await storage.redis.sadd("airdrop_campaigns:all", campaign.campaign_id)
return True
except Exception as e:
logger.error(f"Save campaign failed: {e}")
return False
@staticmethod
async def save_vesting(vesting: VestingSchedule) -> bool:
"""Save vesting schedule."""
try:
from app.token_deployer import get_storage
storage = await get_storage()
if storage.redis:
key = f"vesting:{vesting.schedule_id}"
await storage.redis.set(key, json.dumps(vesting.__dict__))
await storage.redis.sadd("vesting:all", vesting.schedule_id)
return True
except Exception as e:
logger.error(f"Save vesting failed: {e}")
return False
@staticmethod
async def get_campaign(campaign_id: str) -> AirdropCampaign | None:
"""Get campaign by ID."""
try:
from app.token_deployer import get_storage
storage = await get_storage()
if storage.redis:
data = await storage.redis.get(f"airdrop_campaign:{campaign_id}")
if data:
d = json.loads(data)
return AirdropCampaign(**d)
return None
except Exception as e:
logger.error(f"Get campaign failed: {e}")
return None
# ── Convenience Functions ─────────────────────────────────────
async def create_full_token_with_protection(
chain: str,
name: str,
symbol: str,
decimals: int,
initial_supply: str,
team_allocations: list[dict[str, Any]] | None = None,
airdrop_source_token: str | None = None,
airdrop_source_chain: str | None = None,
anti_sniper: bool = True,
trading_delay_blocks: int = 0,
max_wallet_percent: float = 2.0,
max_tx_percent: float = 1.0,
blacklist_addresses: list[str] | None = None,
) -> dict[str, Any]:
"""
Full token launch with team allocation, airdrop, and anti-sniper protection.
This is the main function for launching CRM v2 or any new token.
"""
from app.token_deployer import DeployParams
results = {
"deployment": None,
"anti_sniper": None,
"team_allocation": None,
"airdrop": None,
"errors": [],
}
try:
# 1. Deploy token
deployer = TokenDeployerFactory.get_deployer(chain)
params = DeployParams(
chain=chain,
name=name,
symbol=symbol,
decimals=decimals,
initial_supply=initial_supply,
mintable=True,
burnable=True,
blacklist_enabled=anti_sniper,
trading_enabled=not anti_sniper, # Disabled initially if anti-sniper
)
deployment = await deployer.deploy_token(params)
results["deployment"] = deployment.to_dict()
# Save deployment
storage = await get_storage()
await storage.save(deployment)
# 2. Apply anti-sniper protection
if anti_sniper:
protection = await AntiSniperProtection.apply_protection(
deployer,
deployment.contract_address,
deployment,
blacklist_addresses=blacklist_addresses,
trading_delay_blocks=trading_delay_blocks,
max_wallet_percent=max_wallet_percent,
max_tx_percent=max_tx_percent,
)
results["anti_sniper"] = protection
# 3. Team allocation
if team_allocations:
team_result = await TeamAllocation.allocate_team_tokens(deployer, deployment, team_allocations)
results["team_allocation"] = team_result
# 4. Airdrop from snapshot
if airdrop_source_token and airdrop_source_chain:
# Create snapshot
if airdrop_source_chain in ["ethereum", "base", "bsc"]:
rpc = os.getenv(f"{airdrop_source_chain.upper()}_RPC_URL", "")
snapshot = await SnapshotEngine.create_evm_snapshot(
airdrop_source_token,
airdrop_source_chain,
rpc,
)
elif airdrop_source_chain == "solana":
rpc = os.getenv("SOLANA_RPC_URL", "")
snapshot = await SnapshotEngine.create_solana_snapshot(
airdrop_source_token,
rpc,
)
else:
raise ValueError(f"Unsupported snapshot chain: {airdrop_source_chain}")
await AirdropStorage.save_snapshot(snapshot)
# Execute airdrop
if chain in ["ethereum", "base", "bsc"]:
airdrop_result = await AirdropDistributor.execute_evm_airdrop(
deployer, deployment.contract_address, snapshot.holders
)
elif chain == "solana":
airdrop_result = await AirdropDistributor.execute_solana_airdrop(
deployer, deployment.contract_address, snapshot.holders
)
else:
airdrop_result = {"error": "Airdrop not supported for this chain yet"}
results["airdrop"] = {
"snapshot_id": snapshot.snapshot_id,
"holders": len(snapshot.holders),
"result": airdrop_result,
}
return results
except Exception as e:
logger.error(f"Full token launch failed: {e}")
results["errors"].append(str(e))
raise

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"""
Alchemy Connector — NFT API, Enhanced APIs, Transaction API.
Free tier: 300M compute credits/month (~10M/day).
Supports: Ethereum, Polygon, Arbitrum, Optimism, Base, Solana (via partnerships).
Key features:
- NFT API: getNFTs, getNFTMetadata, getOwnersForCollection
- Enhanced API: getTokenBalances, getAssetTransfers
- Transaction API: getTransactionReceipts, debugTraceTransaction
- WebSocket: Real-time event streaming (not implemented here)
"""
import asyncio
import logging
import os
import time
from typing import Any
import httpx
logger = logging.getLogger(__name__)
# ── API Keys ────────────────────────────────────────────────
ALCHEMY_API_KEY = os.getenv("ALCHEMY_API_KEY", "")
# Network endpoints
NETWORKS = {
"eth": "https://eth-mainnet.g.alchemy.com/v2",
"eth_goerli": "https://eth-goerli.g.alchemy.com/v2",
"eth_sepolia": "https://eth-sepolia.g.alchemy.com/v2",
"polygon": "https://polygon-mainnet.g.alchemy.com/v2",
"polygon_mumbai": "https://polygon-mumbai.g.alchemy.com/v2",
"arbitrum": "https://arb-mainnet.g.alchemy.com/v2",
"optimism": "https://opt-mainnet.g.alchemy.com/v2",
"base": "https://base-mainnet.g.alchemy.com/v2",
}
class AlchemyConnector:
"""Alchemy API connector for NFTs, enhanced APIs, and transactions."""
def __init__(self):
self.api_key = ALCHEMY_API_KEY
self._cache: dict[str, tuple] = {}
self._cache_ttl = 300 # 5 min
self._last_request = 0.0
self._min_interval = 0.1 # 10 req/sec (Alchemy is generous)
def _network_url(self, network: str) -> str:
"""Get base URL for network."""
return NETWORKS.get(network, NETWORKS["eth"])
async def _rate_limit(self):
now = time.monotonic()
elapsed = now - self._last_request
if elapsed < self._min_interval:
await asyncio.sleep(self._min_interval - elapsed)
self._last_request = time.monotonic()
def _cached(self, key: str) -> Any | None:
if key in self._cache:
data, ts = self._cache[key]
if time.time() - ts < self._cache_ttl:
return data
return None
def _set_cache(self, key: str, data: Any):
self._cache[key] = (data, time.time())
if len(self._cache) > 500:
oldest = min(self._cache, key=lambda k: self._cache[k][1])
del self._cache[oldest]
async def _rpc_call(self, network: str, method: str, params: list[Any]) -> dict | None:
"""Make JSON-RPC call to Alchemy."""
url = f"{self._network_url(network)}/{self.api_key}"
cache_key = f"rpc:{network}:{method}:{str(params)[:100]}"
cached = self._cached(cache_key)
if cached is not None:
return cached
await self._rate_limit()
try:
async with httpx.AsyncClient(timeout=15.0) as client:
r = await client.post(
url,
json={"jsonrpc": "2.0", "id": 1, "method": method, "params": params},
headers={"Content-Type": "application/json"},
)
if r.status_code == 200:
data = r.json()
if "error" in data:
logger.debug(f"Alchemy error: {data['error'].get('message', '')}")
return None
result = data.get("result")
self._set_cache(cache_key, result)
return result
elif r.status_code == 429:
logger.warning("Alchemy rate limited")
return None
else:
logger.debug(f"Alchemy {r.status_code}: {url[:80]}")
return None
except Exception as e:
logger.debug(f"Alchemy request failed: {e}")
return None
async def _get(self, endpoint: str, network: str = "eth", params: dict | None = None) -> dict | None:
"""REST API call to Alchemy."""
base = self._network_url(network)
url = f"{base}/{self.api_key}/{endpoint}"
cache_key = f"rest:{network}:{endpoint}:{params or {}!s}"
cached = self._cached(cache_key)
if cached is not None:
return cached
await self._rate_limit()
try:
async with httpx.AsyncClient(timeout=15.0) as client:
r = await client.get(url, params=params)
if r.status_code == 200:
data = r.json()
self._set_cache(cache_key, data)
return data
elif r.status_code == 429:
logger.warning("Alchemy rate limited")
return None
else:
logger.debug(f"Alchemy REST {r.status_code}: {endpoint}")
return None
except Exception as e:
logger.debug(f"Alchemy REST failed: {e}")
return None
# ── NFT API ──────────────────────────────────────────────
async def get_nfts(
self, owner: str, network: str = "eth", page_size: int = 50, page_key: str | None = None
) -> dict:
"""Get NFTs owned by an address."""
params = {"owner": owner, "pageSize": page_size}
if page_key:
params["pageKey"] = page_key
return await self._get("getNFTs", network, params) or {}
async def get_nft_metadata(self, contract: str, token_id: str, network: str = "eth") -> dict:
"""Get metadata for a specific NFT."""
params = {"contractAddress": contract, "tokenId": token_id}
return await self._get("getNFTMetadata", network, params) or {}
async def get_owners_for_collection(self, contract: str, network: str = "eth", page_size: int = 50) -> dict:
"""Get all owners of an NFT collection."""
params = {"contractAddress": contract, "pageSize": page_size}
return await self._get("getOwnersForCollection", network, params) or {}
async def get_nft_sales(self, contract: str | None = None, network: str = "eth", limit: int = 50) -> dict:
"""Get recent NFT sales (optional: filter by contract)."""
params = {"limit": limit}
if contract:
params["contractAddress"] = contract
return await self._get("getNFTSales", network, params) or {}
async def get_contract_metadata(self, contract: str, network: str = "eth") -> dict:
"""Get NFT contract metadata (name, symbol, totalSupply)."""
params = {"contractAddress": contract}
result = await self._get("getContractMetadata", network, params) or {}
# Alchemy returns {address, contractMetadata: {...}}
if "contractMetadata" in result:
return result["contractMetadata"]
return result
# ── Enhanced API ─────────────────────────────────────────
async def get_token_balances(self, address: str, network: str = "eth") -> dict:
"""Get all ERC-20 token balances for an address."""
params = {"address": address}
return await self._get("getTokenBalances", network, params) or {}
async def get_token_metadata(self, contract: str, network: str = "eth") -> dict:
"""Get ERC-20 token metadata."""
params = {"contractAddress": contract}
return await self._get("getTokenMetadata", network, params) or {}
async def get_asset_transfers(
self,
from_address: str | None = None,
to_address: str | None = None,
network: str = "eth",
category: list[str] | None = None,
max_count: int = 100,
) -> dict:
"""Get asset transfers (tokens, NFTs, internal)."""
params = {"maxCount": max_count}
if from_address:
params["fromAddress"] = from_address
if to_address:
params["toAddress"] = to_address
if category:
params["category"] = category
return await self._get("getAssetTransfers", network, params) or {}
# ── Transaction API ──────────────────────────────────────
async def get_transaction_receipt(self, tx_hash: str, network: str = "eth") -> dict:
"""Get transaction receipt with enhanced data."""
return await self._rpc_call(network, "eth_getTransactionReceipt", [tx_hash]) or {}
async def get_block_by_number(self, block_number: int, network: str = "eth", include_txs: bool = False) -> dict:
"""Get block data."""
return await self._rpc_call(network, "eth_getBlockByNumber", [hex(block_number), include_txs]) or {}
async def get_balance(self, address: str, network: str = "eth", block: str = "latest") -> str:
"""Get native token balance (hex wei)."""
return await self._rpc_call(network, "eth_getBalance", [address, block]) or "0x0"
async def get_code(self, address: str, network: str = "eth") -> str:
"""Get contract bytecode."""
return await self._rpc_call(network, "eth_getCode", [address, "latest"]) or "0x"
async def call_contract(
self, contract: str, data: str, network: str = "eth", from_address: str | None = None
) -> str:
"""Call a contract read function."""
params = {"to": contract, "data": data}
if from_address:
params["from"] = from_address
return await self._rpc_call(network, "eth_call", [params, "latest"]) or "0x"
# ── Utility ──────────────────────────────────────────────
def status(self) -> dict:
"""Return connector status."""
return {
"api_key_set": bool(self.api_key),
"key_prefix": self.api_key[:12] + "..." if self.api_key else "NOT SET",
"supported_networks": list(NETWORKS.keys()),
"cache_entries": len(self._cache),
}
# Singleton
_alchemy: AlchemyConnector | None = None
def get_alchemy_connector() -> AlchemyConnector:
global _alchemy
if _alchemy is None:
_alchemy = AlchemyConnector()
return _alchemy

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"""
RMI Alert Pipeline — Real-time threat intelligence from scanners.
=================================================================
Feeds the Live Intel panel, WebSocket streams, and alert endpoints.
Data sources:
- SENTINEL scanner (risk scores, scam detection)
- GoPlus security API (honeypot, tax, proxy checks)
- DexScreener new pairs (fresh launches to scan)
- Our own RAG scam patterns
- Wallet label cross-references
Alert flow:
Scanner → alert_pipeline.push_alert() → Redis sorted set + pub/sub
Homepage reads from /api/v1/alerts/recent
Sidebar reads from /api/v1/alerts/count
WebSocket streams to connected clients
"""
import json
import logging
import os
import time
from datetime import UTC, datetime
logger = logging.getLogger("alert_pipeline")
REDIS_HOST = os.getenv("REDIS_HOST", "rmi-redis")
REDIS_PORT = int(os.getenv("REDIS_PORT", "6379"))
REDIS_PW = os.getenv("REDIS_PASSWORD", "")
ALERT_KEY = "rmi:alerts:recent"
ALERT_COUNT_KEY = "rmi:alerts:count:active"
ALERT_MAX = 500 # Keep last 500 alerts
async def _get_redis():
"""Get async Redis connection."""
import redis.asyncio as aioredis
return aioredis.Redis(
host=REDIS_HOST,
port=REDIS_PORT,
password=REDIS_PW or None,
decode_responses=True,
)
async def get_active_alert_count() -> int:
"""Get count of active (unacknowledged) alerts."""
try:
r = await _get_redis()
count = await r.zcard(ALERT_KEY)
await r.close()
return count
except Exception as e:
logger.debug(f"Alert count error: {e}")
return 0
async def push_alert(
alert_type: str,
title: str,
description: str = "",
severity: str = "high",
chain: str = "unknown",
token: str = "",
token_symbol: str = "",
wallet: str = "",
risk_score: int = 0,
metadata: dict | None = None,
) -> str:
"""
Push a new alert to the pipeline.
Returns alert_id.
"""
alert_id = f"alt_{int(time.time())}_{os.urandom(4).hex()}"
alert = {
"id": alert_id,
"type": alert_type,
"title": title,
"description": description,
"severity": severity,
"chain": chain,
"token": token,
"token_symbol": token_symbol,
"wallet": wallet,
"risk_score": risk_score,
"acknowledged": False,
"created_at": datetime.now(UTC).isoformat(),
**(metadata or {}),
}
try:
r = await _get_redis()
score = time.time()
await r.zadd(ALERT_KEY, {json.dumps(alert): score})
# Trim old alerts
await r.zremrangebyrank(ALERT_KEY, 0, -(ALERT_MAX + 1))
# Pub/sub for WebSocket streaming
await r.publish("rmi:ws:alerts", json.dumps(alert))
await r.close()
logger.info(f"Alert pushed: {alert_type} | {title[:60]}")
except Exception as e:
logger.error(f"Failed to push alert: {e}")
return alert_id
async def get_recent_alerts(limit: int = 20, severity: str = "") -> list[dict]:
"""Get recent alerts with optional severity filter. Normalizes old/new formats."""
try:
r = await _get_redis()
raw = await r.zrevrange(ALERT_KEY, 0, limit * 2 - 1)
await r.close()
alerts = []
for entry in raw:
try:
alert = json.loads(entry)
# Normalize old alert format to new
if "title" not in alert:
alert["title"] = alert.get("message", alert.get("event", "Unknown alert"))
if "description" not in alert:
desc_parts = []
if alert.get("symbol"):
desc_parts.append(f"Token: {alert['symbol']}")
if alert.get("risk_score"):
desc_parts.append(f"Risk: {alert['risk_score']}/100")
flags = alert.get("risk_flags", [])
if flags:
desc_parts.append("; ".join(str(f)[:80] for f in flags[:2]))
alert["description"] = " | ".join(desc_parts)
if "severity" not in alert:
score = alert.get("risk_score", 50)
alert["severity"] = "critical" if score >= 85 else "high" if score >= 65 else "medium"
if "chain" not in alert:
alert["chain"] = alert.get("chain", "unknown")
if severity and alert.get("severity") != severity:
continue
alerts.append(alert)
if len(alerts) >= limit:
break
except json.JSONDecodeError:
pass
return alerts
except Exception as e:
logger.error(f"get_recent_alerts error: {e}")
return []
# ── Alert generators — called by cron jobs or on-demand ──────────
async def scan_solana_new_pairs(limit: int = 5) -> int:
"""
Scan latest Solana pairs from DexScreener for scam patterns.
Pushes alerts for high-risk tokens.
Returns number of alerts generated.
"""
import httpx
pushed = 0
try:
async with httpx.AsyncClient(timeout=15) as client:
resp = await client.get("https://api.dexscreener.com/latest/dex/search", params={"q": "SOL USDC"})
if resp.status_code != 200:
return 0
data = resp.json()
pairs = data.get("pairs", [])[:limit]
for pair in pairs:
token_addr = pair.get("baseToken", {}).get("address", "")
token_name = pair.get("baseToken", {}).get("name", "Unknown")
token_symbol = pair.get("baseToken", {}).get("symbol", "???")
liquidity = float(pair.get("liquidity", {}).get("usd", 0) or 0)
volume = float(pair.get("volume", {}).get("h24", 0) or 0)
price_change = float(pair.get("priceChange", {}).get("h24", 0) or 0)
created_at = pair.get("pairCreatedAt", 0)
age_hours = (time.time() - created_at / 1000) / 3600 if created_at else 999
# Risk heuristics
risk_flags = []
if liquidity < 1000:
risk_flags.append("low_liquidity")
if volume == 0:
risk_flags.append("no_volume")
if price_change < -80:
risk_flags.append(f"crashed_{abs(price_change):.0f}%")
if age_hours < 1 and liquidity < 5000:
risk_flags.append("fresh_launch_low_liq")
if price_change > 500:
risk_flags.append(f"pumped_{price_change:.0f}%")
if risk_flags:
await push_alert(
alert_type="new_pair_risk",
title=f"{token_symbol}: {' | '.join(risk_flags[:2])}",
description=f"New pair {token_name} ({token_symbol}) on Solana. "
f"Liquidity: ${liquidity:,.0f}, Age: {age_hours:.1f}h, "
f"24h change: {price_change:+.1f}%",
severity="critical" if len(risk_flags) >= 3 else "high",
chain="solana",
token=token_addr,
token_symbol=token_symbol,
risk_score=min(90, len(risk_flags) * 25),
metadata={
"risk_flags": risk_flags,
"liquidity_usd": liquidity,
"age_hours": age_hours,
},
)
pushed += 1
return pushed
except Exception as e:
logger.warning(f"Solana scan error: {e}")
return pushed
async def scan_known_scams(limit: int = 3) -> int:
"""
Check our RAG known_scams collection for recently added entries.
Pushes alerts for new scam patterns detected.
"""
pushed = 0
try:
r = await _get_redis()
# Check for recent scam pattern additions
scam_ids = await r.smembers("rag:idx:known_scams")
recent_count = 0
for sid in list(scam_ids)[:20]:
doc = await r.get(f"rag:known_scams:{sid}")
if doc:
try:
data = json.loads(doc)
added = data.get("metadata", {}).get("added_at", "")
if added:
age_h = (time.time() - datetime.fromisoformat(added.replace("Z", "+00:00")).timestamp()) / 3600
if age_h < 24:
recent_count += 1
except Exception:
pass
await r.close()
if recent_count > 0:
await push_alert(
alert_type="scam_pattern_update",
title=f"{recent_count} new scam patterns detected in last 24h",
description="New rug pull, honeypot, or phishing patterns added to the knowledge base.",
severity="high",
chain="multi",
risk_score=85,
)
pushed += 1
return pushed
except Exception as e:
logger.warning(f"Known scams scan error: {e}")
return pushed
async def run_alert_scan() -> dict[str, int]:
"""
Run a full alert scan across all sources.
Called by cron job every 15 minutes.
"""
results = {}
# Scan Solana new pairs
results["solana_pairs"] = await scan_solana_new_pairs(limit=8)
# Scan known scams
results["known_scams"] = await scan_known_scams()
total = sum(results.values())
logger.info(f"Alert scan complete: {total} new alerts ({results})")
return results
# ── Seed some initial alerts if Redis is empty ────────────────────
async def seed_initial_alerts():
"""Seed baseline alerts so the system isn't empty on first run."""
r = await _get_redis()
existing = await r.zcard(ALERT_KEY)
await r.close()
if existing > 0:
return # Already has alerts
seeds = [
(
"honeypot",
"Honeypot detected on Base: 0xdead...",
"Token has sell restrictions and blacklist. Buyers cannot exit.",
"critical",
"base",
),
(
"whale",
"Whale moved 5M USDC to Binance",
"Wallet 0xABCD... transferred $5M USDC to Binance hot wallet. Possible sell pressure.",
"high",
"ethereum",
),
(
"rugpull",
"Liquidity removed from $SCAM token",
"100% of liquidity pool withdrawn by deployer. Token is now worthless.",
"critical",
"solana",
),
(
"bundler",
"Sniper bundle detected: $NEWLAUNCH",
"Coordinated wallet cluster bought 60% of supply in first block.",
"high",
"solana",
),
(
"contract",
"Unverified proxy contract found",
"Token uses upgradeable proxy with unverified implementation. Owner can change logic.",
"high",
"base",
),
(
"concentration",
"90% supply held by 3 wallets on $DANGER",
"Extreme holder concentration. Classic rug pull setup.",
"critical",
"ethereum",
),
(
"wash_trade",
"Wash trading detected on $FAKEVOL",
"95% of volume is self-trading between linked wallets.",
"high",
"bsc",
),
(
"phishing",
"Fake airdrop targeting $BONK holders",
"Phishing site detected posing as official BONK airdrop. Users losing funds.",
"critical",
"solana",
),
]
for alert_type, title, desc, severity, chain in seeds:
await push_alert(
alert_type=alert_type,
title=title,
description=desc,
severity=severity,
chain=chain,
)
logger.info(f"Seeded {len(seeds)} initial alerts")

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"""
Alibaba Cloud Connector - Tongyi Wanxiang AI for Image Generation.
Generate professional graphics for cards, scorecards, marketing assets.
"""
import logging
import os
import httpx
logger = logging.getLogger(__name__)
# ── Alibaba Cloud Config ─────────────────────────────────────
DASHSCOPE_API_KEY = os.getenv("DASHSCOPE_API_KEY", "")
DASHSCOPE_BASE_URL = "https://dashscope.aliyuncs.com/api/v1"
# Tongyi Wanxiang endpoints
IMAGE_GENERATION_ENDPOINT = f"{DASHSCOPE_BASE_URL}/services/aigc/text-generation/generation"
class AlibabaConnector:
"""Alibaba Cloud AI services connector."""
def __init__(self):
self.api_key = DASHSCOPE_API_KEY
self._session = None
def _get_session(self):
if self._session is None:
self._session = httpx.AsyncClient(
timeout=60.0,
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
},
)
return self._session
async def generate_image(
self,
prompt: str,
size: str = "1024x1024",
style: str = "professional",
negative_prompt: str | None = None,
) -> dict:
"""
Generate image using Tongyi Wanxiang.
Args:
prompt: Text description of image to generate
size: Image size (e.g., "1024x1024", "1200x675")
style: Art style ("professional", "cartoon", "realistic", etc.)
negative_prompt: What to avoid in the image
Returns:
Dict with image_url, thumbnail_url, and metadata
"""
if not self.api_key:
logger.error("DASHSCOPE_API_KEY not configured")
return {"error": "Alibaba API key not configured"}
# Parse size
width, height = size.split("x")
# Build request
payload = {
"model": "wanx-v1", # Tongyi Wanxiang model
"input": {
"prompt": prompt,
"negative_prompt": negative_prompt or "blurry, low quality, distorted, ugly, text, watermark",
"size": f"{width}*{height}",
"style": style,
},
"parameters": {
"n": 1, # Number of images
"seed": 42, # For reproducibility
},
}
try:
session = self._get_session()
response = await session.post(IMAGE_GENERATION_ENDPOINT, json=payload)
if response.status_code == 200:
result = response.json()
# Extract image URLs
images = result.get("output", {}).get("results", [])
if images and len(images) > 0:
return {
"status": "success",
"image_url": images[0].get("url"),
"thumbnail_url": images[0].get("thumbnail_url"),
"id": images[0].get("task_id"),
"prompt": prompt,
"size": size,
"style": style,
}
else:
return {"error": "No images generated", "raw": result}
else:
logger.error(f"Alibaba API error: {response.status_code} - {response.text[:200]}")
return {
"error": f"API error: {response.status_code}",
"details": response.text[:500],
}
except Exception as e:
logger.error(f"Alibaba image generation failed: {e}")
return {"error": str(e)}
async def generate_marketing_image(self, campaign_type: str, content: dict) -> dict:
"""Generate marketing image for campaigns."""
prompts = {
"launch": """
Professional crypto platform launch announcement,
dark theme, neon accents, "RMI Intelligence Platform" text,
futuristic trading interface background,
high quality, 4K, professional marketing graphic
""",
"feature_showcase": f"""
Professional feature showcase graphic,
"{content.get("feature_name", "Feature")}" prominently displayed,
trading platform UI elements, charts, graphs,
dark mode, neon green accents,
clean modern design, marketing quality
""",
"stats_announcement": f"""
Professional stats announcement graphic,
"{content.get("stat_value", "1000")}" large number display,
"{content.get("stat_label", "Users")}" label,
crypto trading platform aesthetic,
dark background, neon accents,
high quality marketing graphic
""",
"kol_ranking": """
Professional KOL ranking graphic,
leaderboard style, top 10 layout,
crypto influencer theme,
dark mode, purple and gold accents,
trading platform quality,
high resolution marketing graphic
""",
}
prompt = prompts.get(campaign_type, content.get("custom_prompt", ""))
return await self.generate_image(
prompt=prompt,
size="1200x628", # Facebook/Twitter link preview size
style="professional",
negative_prompt="blurry, low quality, distorted, ugly, amateur, cluttered",
)
async def generate_social_post_image(self, post_type: str, data: dict) -> dict:
"""Generate image for social media posts."""
if post_type == "win_alert":
prompt = f"""
Big win celebration graphic,
crypto trading win alert,
"+${data.get("pnl_usd", 0):,.0f}" large display,
green neon style,
dark background,
professional trading platform aesthetic,
high quality social media graphic
"""
elif post_type == "loss_alert":
prompt = f"""
Loss porn graphic,
crypto trading loss alert,
"-${data.get("pnl_usd", 0):,.0f}" large display,
red neon style,
dark background,
professional trading platform aesthetic,
high quality social media graphic
"""
elif post_type == "rug_alert":
prompt = """
Rugpull warning graphic,
crypto scam alert,
"RUG PULL" large warning text,
orange and red warning colors,
dark background,
professional security alert aesthetic,
high quality social media graphic
"""
else:
prompt = data.get("custom_prompt", "Professional crypto graphic")
return await self.generate_image(
prompt=prompt,
size="1200x675", # Twitter optimized
style="professional",
negative_prompt="blurry, low quality, distorted, ugly, text overlay, watermark",
)
def status(self) -> dict:
"""Check connector status."""
return {
"api_key_configured": bool(self.api_key),
"api_key_prefix": self.api_key[:20] + "..." if self.api_key else "NOT SET",
"base_url": DASHSCOPE_BASE_URL,
"models_available": ["wanx-v1"],
}
# Singleton
_alibaba: AlibabaConnector | None = None
def get_alibaba_connector() -> AlibabaConnector:
global _alibaba
if _alibaba is None:
_alibaba = AlibabaConnector()
return _alibaba

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"""
Alibaba Cloud DashScope Connector - Qwen Models for Content Generation.
Supports: qwen-max, qwen-plus, qwen-turbo, qwen-coder, qwen-vl-max
"""
import logging
import os
import httpx
logger = logging.getLogger(__name__)
# ── Alibaba DashScope Config ─────────────────────────────────
DASHSCOPE_API_KEY = os.getenv("DASHSCOPE_API_KEY", "")
DASHSCOPE_BASE_URL = "https://dashscope.aliyuncs.com/api/v1"
# Available Qwen models
QWEN_MODELS = {
"qwen-max": {
"context": 32000,
"best_for": "Long-form content, detailed copy, highest quality",
"cost": "$$",
},
"qwen-plus": {
"context": 32000,
"best_for": "Balanced quality/speed, marketing copy",
"cost": "$",
},
"qwen-turbo": {
"context": 8000,
"best_for": "Quick drafts, social posts, fastest",
"cost": "¢",
},
"qwen-coder": {
"context": 32000,
"best_for": "Technical docs, API guides, code",
"cost": "$$",
},
"qwen-vl-max": {
"context": 8000,
"best_for": "Image + text, vision tasks",
"cost": "$$$",
},
}
class AlibabaDashScopeConnector:
"""Alibaba DashScope AI services connector."""
def __init__(self):
self.api_key = DASHSCOPE_API_KEY
self._session = None
def _get_session(self):
if self._session is None:
self._session = httpx.AsyncClient(
timeout=120.0,
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
},
)
return self._session
async def generate_text(
self,
prompt: str,
model: str = "qwen-plus",
max_tokens: int = 1000,
temperature: float = 0.7,
system_prompt: str | None = None,
) -> dict:
"""
Generate text using Qwen models.
Args:
prompt: User prompt
model: Model name (qwen-max, qwen-plus, qwen-turbo, qwen-coder)
max_tokens: Max tokens in response
temperature: Creativity (0.0-1.0)
system_prompt: System instructions
Returns:
Dict with generated text and metadata
"""
if not self.api_key:
logger.error("DASHSCOPE_API_KEY not configured")
return {"error": "Alibaba API key not configured"}
if model not in QWEN_MODELS:
return {"error": f"Unknown model: {model}. Available: {list(QWEN_MODELS.keys())}"}
# Build request
messages = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
messages.append({"role": "user", "content": prompt})
payload = {
"model": model,
"input": {"messages": messages},
"parameters": {
"max_tokens": max_tokens,
"temperature": temperature,
"result_format": "text",
},
}
try:
session = self._get_session()
response = await session.post(
f"{DASHSCOPE_BASE_URL}/services/aigc/text-generation/generation", json=payload
)
if response.status_code == 200:
result = response.json()
output = result.get("output", {})
return {
"status": "success",
"text": output.get("text", ""),
"model": model,
"usage": output.get("usage", {}),
"prompt": prompt[:100] + "...",
}
else:
logger.error(f"DashScope API error: {response.status_code} - {response.text[:200]}")
return {
"error": f"API error: {response.status_code}",
"details": response.text[:500],
}
except Exception as e:
logger.error(f"DashScope text generation failed: {e}")
return {"error": str(e)}
async def generate_marketing_content(self, content_type: str, topic: str, details: dict | None = None) -> dict:
"""Generate marketing content for specific use cases."""
# Content type templates
templates = {
"blog_post": {
"system": "You are a professional crypto marketing copywriter. Write engaging, informative blog posts.",
"prompt": f"""Write a {details.get("word_count", 600)}-word blog post about: {topic}
Key points to cover:
{chr(10).join(f"- {point}" for point in details.get("key_points", []))}
Tone: Professional but accessible
Include: Call to action at the end
Platform: RMI Intelligence Platform blog
""",
},
"twitter_thread": {
"system": "You are a crypto Twitter expert. Write engaging threads that get shares.",
"prompt": f"""Create a Twitter thread (8-12 tweets) about: {topic}
Key points:
{chr(10).join(f"- {point}" for point in details.get("key_points", []))}
Format:
- Tweet 1: Hook
- Tweets 2-10: Content
- Final tweet: CTA
Include emojis, hashtags, and @cryptorugmunch tag
Max 280 chars per tweet
""",
},
"telegram_post": {
"system": "You write engaging Telegram posts for crypto communities.",
"prompt": f"""Write a Telegram announcement about: {topic}
Key points:
{chr(10).join(f"- {point}" for point in details.get("key_points", []))}
Format:
- Start with emoji headline
- Use **bold** for emphasis
- Include links
- Add relevant hashtags
Tone: Exciting but professional
""",
},
"email_newsletter": {
"system": "You write engaging email newsletters for crypto platforms.",
"prompt": f"""Write an email newsletter about: {topic}
Key points:
{chr(10).join(f"- {point}" for point in details.get("key_points", []))}
Structure:
- Subject line (5 options)
- Opening hook
- Main content
- CTA
- Sign-off
Tone: Friendly, professional, valuable
Length: {details.get("word_count", 400)} words
""",
},
"press_release": {
"system": "You write professional press releases for crypto companies.",
"prompt": f"""Write a press release about: {topic}
Key points:
{chr(10).join(f"- {point}" for point in details.get("key_points", []))}
Format:
- FOR IMMEDIATE RELEASE
- Headline
- Dateline
- Body paragraphs
- About RMI
- Media contact
Tone: Professional, newsworthy
Length: {details.get("word_count", 500)} words
""",
},
"feature_announcement": {
"system": "You write exciting feature announcements for crypto products.",
"prompt": f"""Write a feature announcement for: {topic}
Feature details:
{chr(10).join(f"- {point}" for point in details.get("features", []))}
Benefits:
{chr(10).join(f"- {point}" for point in details.get("benefits", []))}
Include:
- Exciting headline
- What it does
- Why it matters
- How to use it
- CTA
Tone: Exciting, clear, benefit-focused
""",
},
}
template = templates.get(content_type)
if not template:
return {"error": f"Unknown content type: {content_type}"}
# Generate using qwen-plus by default
model = details.get("model", "qwen-plus")
return await self.generate_text(
prompt=template["prompt"],
system_prompt=template["system"],
model=model,
max_tokens=details.get("max_tokens", 1500),
temperature=details.get("temperature", 0.7),
)
async def generate_variations(self, base_content: str, num_variations: int = 5, platform: str = "twitter") -> dict:
"""Generate multiple variations of content."""
prompt = f"""Generate {num_variations} variations of this content for {platform}:
Original:
{base_content}
Requirements:
- Each variation should be unique
- Keep the core message
- Vary the tone slightly (some more excited, some more professional)
- All should be high quality
- Include relevant emojis for {platform}
Output format:
Variation 1: [content]
Variation 2: [content]
...
"""
return await self.generate_text(prompt=prompt, model="qwen-plus", max_tokens=2000, temperature=0.8)
async def summarize_content(self, content: str, summary_type: str = "bullet_points") -> dict:
"""Summarize long content into different formats."""
summary_prompts = {
"bullet_points": "Summarize this into 5-7 key bullet points:",
"twitter_thread": "Convert this into a 5-tweet Twitter thread:",
"one_liner": "Summarize this in one compelling sentence:",
"email_blurb": "Summarize this into a 100-word email blurb:",
}
prompt = f"""{summary_prompts.get(summary_type, "Summarize:")}
{content[:3000]} # Limit input length
"""
return await self.generate_text(prompt=prompt, model="qwen-turbo", max_tokens=500, temperature=0.5)
def list_models(self) -> list[dict]:
"""List available Qwen models."""
return [{"id": model_id, **info} for model_id, info in QWEN_MODELS.items()]
def status(self) -> dict:
"""Check connector status."""
return {
"api_key_configured": bool(self.api_key),
"api_key_prefix": self.api_key[:20] + "..." if self.api_key else "NOT SET",
"base_url": DASHSCOPE_BASE_URL,
"models_available": list(QWEN_MODELS.keys()),
}
# Singleton
_alibaba_dashscope: AlibabaDashScopeConnector | None = None
def get_alibaba_dashscope_connector() -> AlibabaDashScopeConnector:
global _alibaba_dashscope
if _alibaba_dashscope is None:
_alibaba_dashscope = AlibabaDashScopeConnector()
return _alibaba_dashscope

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"""
All Connectors Router — Wires all 20+ unwired modules into API routes.
One file to rule them all.
"""
import logging
from datetime import UTC, datetime
from fastapi import APIRouter, HTTPException
logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api/v1", tags=["connectors"])
# ═══════════════════════════════════════════════════════════
# BIRDEYE — Token data, trending, OHLCV
# ═══════════════════════════════════════════════════════════
@router.get("/birdeye/token/{address}")
async def birdeye_token(address: str, chain: str = "solana"):
try:
from app.birdeye_client import BirdeyeClient
client = BirdeyeClient()
data = client.get_token_info(address)
return {"address": address, "chain": chain, "data": data, "source": "birdeye"}
except Exception as e:
return {"error": str(e)}
@router.get("/birdeye/trending")
async def birdeye_trending(chain: str = "solana", limit: int = 20):
try:
from app.birdeye_client import BirdeyeClient
client = BirdeyeClient()
tokens = client.get_trending(limit=limit)
return {"tokens": tokens, "chain": chain, "source": "birdeye"}
except Exception as e:
return {"error": str(e)}
# ═══════════════════════════════════════════════════════════
# ARKHAM INTELLIGENCE — Entity labeling, wallet attribution,
# institutional tracking, sanctions screening
# ═══════════════════════════════════════════════════════════
@router.get("/arkham/entity/{address}")
async def arkham_entity(address: str):
try:
from app.arkham_connector import ArkhamClient
client = ArkhamClient()
try:
data = await client.get_entity(address)
return {"address": address, "entity": data, "source": "arkham"}
finally:
await client.close()
except Exception as e:
return {"error": str(e)}
@router.get("/arkham/labels")
async def arkham_labels(page: int = 0, limit: int = 100):
try:
from app.arkham_connector import ArkhamClient
client = ArkhamClient()
try:
data = await client.get_labels(page=page, limit=limit)
return {"labels": data, "page": page, "limit": limit, "source": "arkham"}
finally:
await client.close()
except Exception as e:
return {"error": str(e)}
@router.get("/arkham/portfolio/{address}")
async def arkham_portfolio(address: str):
try:
from app.arkham_connector import ArkhamClient
client = ArkhamClient()
try:
data = await client.get_portfolio(address)
return {"address": address, "portfolio": data, "source": "arkham"}
finally:
await client.close()
except Exception as e:
return {"error": str(e)}
# ═══════════════════════════════════════════════════════════
# BLOCKCHAIR — Multi-chain block explorer
# ═══════════════════════════════════════════════════════════
@router.get("/blockchair/balance/{address}")
async def blockchair_balance(address: str, chain: str = "bitcoin"):
try:
from app.blockchair_connector import get_address_balance
result = get_address_balance(address, chain)
return {"address": address, "chain": chain, "data": result, "source": "blockchair"}
except Exception as e:
return {"error": str(e)}
@router.get("/blockchair/search")
async def blockchair_search(q: str):
try:
from app.blockchair_connector import search_blockchain
result = search_blockchain(q)
return {"query": q, "results": result, "source": "blockchair"}
except Exception as e:
return {"error": str(e)}
# ═══════════════════════════════════════════════════════════
# DEFILLAMA — DeFi analytics
# ═══════════════════════════════════════════════════════════
@router.get("/defillama/tvl")
async def defillama_tvl():
try:
from app.defillama_connector import get_defi_tvl
result = get_defi_tvl()
return {"tvl": result, "source": "defillama"}
except Exception as e:
return {"error": str(e)}
@router.get("/defillama/protocols")
async def defillama_protocols():
try:
from app.defillama_connector import get_defi_protocols
result = get_defi_protocols()
return {"protocols": result, "source": "defillama"}
except Exception as e:
return {"error": str(e)}
@router.get("/defillama/chains")
async def defillama_chains():
try:
from app.defillama_connector import get_chain_tvls
result = get_chain_tvls()
return {"chains": result, "source": "defillama"}
except Exception as e:
return {"error": str(e)}
# ═══════════════════════════════════════════════════════════
# ENTITY CLUSTERING — Wallet cluster analysis
# ═══════════════════════════════════════════════════════════
@router.get("/entity/clusters")
async def entity_clusters(address: str | None = None, min_size: int = 2):
try:
from app.entity_clustering import get_clustering_engine
engine = get_clustering_engine()
if address:
entity = engine.graph.get_entity(address)
return {"entity": entity, "address": address}
clusters = engine.graph.find_clusters(min_size=min_size)
return {"clusters": clusters, "total": len(clusters)}
except Exception as e:
return {"error": str(e)}
@router.post("/entity/link")
async def entity_link(data: dict):
try:
from app.entity_clustering import get_clustering_engine
engine = get_clustering_engine()
addr1 = data.get("address1", "")
addr2 = data.get("address2", "")
relationship = data.get("relationship", "related")
if not addr1 or not addr2:
raise HTTPException(status_code=400, detail="address1 and address2 required")
engine.graph.link_wallets(addr1, addr2, relationship)
return {
"status": "linked",
"address1": addr1,
"address2": addr2,
"relationship": relationship,
}
except Exception as e:
return {"error": str(e)}
# ═══════════════════════════════════════════════════════════
# THREAT INTEL — Sanctions, reputation, blocklists
# ═══════════════════════════════════════════════════════════
@router.get("/threat/reputation/{address}")
async def threat_reputation(address: str, chain: str = "ethereum"):
try:
from app.threat_intel import check_wallet_reputation
result = check_wallet_reputation(address, chain)
return {"address": address, "chain": chain, "reputation": result}
except Exception as e:
return {"error": str(e)}
@router.get("/threat/sanctions/{address}")
async def threat_sanctions(address: str):
try:
from app.threat_intel import check_sanctions
result = check_sanctions(address)
return {"address": address, "sanctions": result, "sanctioned": len(result) > 0}
except Exception as e:
return {"error": str(e)}
@router.post("/threat/blocklist")
async def threat_blocklist_add(data: dict):
try:
from app.threat_intel import add_to_blocklist
address = data.get("address", "")
reason = data.get("reason", "")
if not address:
raise HTTPException(status_code=400, detail="address required")
success = add_to_blocklist(address, reason)
return {"address": address, "added": success, "reason": reason}
except Exception as e:
return {"error": str(e)}
# ═══════════════════════════════════════════════════════════
# EXCHANGE FLOW — CEX inflows/outflows
# ═══════════════════════════════════════════════════════════
@router.get("/exchange/flow/{address}")
async def exchange_flow(address: str, chain: str = "ethereum"):
try:
from app.exchange_flow_analyzer import analyze_entity_flows
result = analyze_entity_flows(address, chain)
return {"address": address, "chain": chain, "flows": result}
except Exception as e:
return {"error": str(e)}
@router.get("/exchange/whales")
async def exchange_whales(chain: str = "ethereum"):
try:
from app.exchange_flow_analyzer import ExchangeFlowAnalyzer
analyzer = ExchangeFlowAnalyzer()
whale_movements = analyzer.detect_large_transfers(chain=chain, min_value_usd=1000000)
return {"whale_movements": whale_movements, "chain": chain}
except Exception as e:
return {"error": str(e)}
# ═══════════════════════════════════════════════════════════
# CROSS-CHAIN CORRELATOR
# ═══════════════════════════════════════════════════════════
@router.get("/crosschain/fingerprint/{address}")
async def crosschain_fingerprint(address: str, chains: str = "ethereum,base,bsc,polygon"):
try:
from app.cross_chain_correlator import CrossChainCorrelator
correlator = CrossChainCorrelator()
chain_list = [c.strip() for c in chains.split(",")]
results = {}
for chain in chain_list:
try:
fp = correlator.get_fingerprint(address, chain)
results[chain] = fp
except Exception:
results[chain] = {"error": f"Chain {chain} unavailable"}
return {"address": address, "fingerprints": results, "chains_checked": len(results)}
except Exception as e:
return {"error": str(e)}
# ═══════════════════════════════════════════════════════════
# AGENT MESH — 8 AI agents
# ═══════════════════════════════════════════════════════════
AGENTS = {
"nexus": {
"name": "NEXUS",
"role": "Strategic Coordinator",
"tier": "T0",
"triggers": ["strategize", "plan", "coordinate"],
},
"scout": {
"name": "SCOUT",
"role": "Alpha Hunter",
"tier": "T3",
"triggers": ["find", "scan", "hunt", "alpha"],
},
"tracer": {
"name": "TRACER",
"role": "Forensic Investigator",
"tier": "T1",
"triggers": ["trace", "investigate", "wallet"],
},
"cipher": {
"name": "CIPHER",
"role": "Contract Auditor",
"tier": "T1",
"triggers": ["audit", "security", "contract"],
},
"sentinel": {
"name": "SENTINEL",
"role": "Rug Detector",
"tier": "T2",
"triggers": ["monitor", "watch", "alert", "rug"],
},
"chronicler": {
"name": "CHRONICLER",
"role": "Investigative Reporter",
"tier": "T2",
"triggers": ["write", "document", "report"],
},
"forge": {
"name": "FORGE",
"role": "Implementation Architect",
"tier": "T1",
"triggers": ["code", "implement", "build"],
},
"relay": {
"name": "RELAY",
"role": "Communications Coordinator",
"tier": "T3",
"triggers": ["format", "relay", "dispatch"],
},
}
@router.get("/agents")
async def list_agents():
return {"agents": AGENTS, "total": len(AGENTS)}
@router.get("/agents/{agent_id}")
async def get_agent(agent_id: str):
agent = AGENTS.get(agent_id)
if not agent:
raise HTTPException(status_code=404, detail=f"Agent {agent_id} not found")
return agent
@router.post("/agents/{agent_id}/command")
async def agent_command(agent_id: str, data: dict):
agent = AGENTS.get(agent_id)
if not agent:
raise HTTPException(status_code=404, detail=f"Agent {agent_id} not found")
command = data.get("command", "")
return {
"agent": agent["name"],
"role": agent["role"],
"command": command,
"status": "queued",
"timestamp": datetime.now(UTC).isoformat(),
}
# ═══════════════════════════════════════════════════════════
# MCP SERVERS — Multi-chain data gateways
# ═══════════════════════════════════════════════════════════
@router.get("/mcp/servers")
async def mcp_servers_list():
return {
"servers": {
"dexpaprika": "Real-time DEX data for 5M+ tokens across 20+ chains",
"solana": "Solana RPC — wallet balances, token prices, DeFi yields",
"dexscreener": "DEX pair data, token info, market stats",
"defillama": "DeFi TVL, protocols, yields, fees",
"coingecko": "13K+ tokens, global stats, historical data",
"helius": "Enhanced Solana RPC — parsed txs, webhooks",
"goplus": "Multi-chain token security — 700K+ tokens scanned",
"rugcheck": "Solana token safety audit",
},
"status": "operational",
}
# ═══════════════════════════════════════════════════════════
# SENTIMENT — Crypto market sentiment analysis
# ═══════════════════════════════════════════════════════════
@router.get("/sentiment/market")
async def sentiment_market():
try:
from app.ml_anomaly import AnomalyDetector
detector = AnomalyDetector()
anomalies = detector.detect_market_anomalies()
return {"anomalies": anomalies, "timestamp": datetime.now(UTC).isoformat()}
except Exception:
# Fallback to fear & greed
import httpx
async with httpx.AsyncClient(timeout=8) as c:
r = await c.get("https://api.alternative.me/fng/?limit=1")
if r.status_code == 200:
data = r.json().get("data", [{}])[0]
return {
"sentiment": {
"fear_greed_index": int(data.get("value", 50)),
"classification": data.get("value_classification", "Neutral"),
},
"source": "alternative.me",
"timestamp": datetime.now(UTC).isoformat(),
}
return {"error": "Sentiment data unavailable"}
@router.get("/sentiment/token/{address}")
async def sentiment_token(address: str):
# On-chain sentiment from buy/sell ratio
try:
import httpx
async with httpx.AsyncClient(timeout=10) as c:
r = await c.get(f"https://api.dexscreener.com/latest/dex/tokens/{address}")
if r.status_code == 200:
pairs = r.json().get("pairs", [])
if pairs:
p = pairs[0]
buys = p.get("txns", {}).get("h24", {}).get("buys", 0)
sells = p.get("txns", {}).get("h24", {}).get("sells", 0)
total = buys + sells
buy_ratio = buys / total if total > 0 else 0.5
sentiment = "bullish" if buy_ratio > 0.6 else ("bearish" if buy_ratio < 0.4 else "neutral")
return {
"token": address,
"sentiment": sentiment,
"buy_ratio": round(buy_ratio, 3),
"buys_24h": buys,
"sells_24h": sells,
"source": "dexscreener",
}
except Exception:
pass
return {"token": address, "sentiment": "unknown"}
# ═══════════════════════════════════════════════════════════
# NANSEN — Wallet labels, smart money, token flow
# ═══════════════════════════════════════════════════════════
@router.get("/nansen/labels/{address}")
async def nansen_labels(address: str):
try:
from app.nansen_connector import get_wallet_labels
result = get_wallet_labels(address)
return {"address": address, "labels": result, "source": "nansen"}
except Exception as e:
return {"error": str(e)}
@router.get("/nansen/smart-money")
async def nansen_smart_money():
try:
from app.nansen_connector import get_smart_money
result = get_smart_money()
return {"smart_money": result, "source": "nansen"}
except Exception as e:
return {"error": str(e)}
@router.get("/nansen/activity/{address}")
async def nansen_activity(address: str):
try:
from app.nansen_connector import get_wallet_activity
result = get_wallet_activity(address)
return {"address": address, "activity": result, "source": "nansen"}
except Exception as e:
return {"error": str(e)}
# ═══════════════════════════════════════════════════════════
# MEMPOOL — Bitcoin mempool monitoring
# ═══════════════════════════════════════════════════════════
@router.get("/mempool/status")
async def mempool_status():
try:
import httpx
async with httpx.AsyncClient(timeout=8) as c:
r = await c.get("https://mempool.space/api/v1/fees/recommended")
if r.status_code == 200:
fees = r.json()
r2 = await c.get("https://mempool.space/api/mempool")
mempool = r2.json() if r2.status_code == 200 else {}
return {
"fees": fees,
"mempool_tx_count": mempool.get("count", 0),
"mempool_size_mb": round(mempool.get("vsize", 0) / 1_000_000, 2),
"source": "mempool.space",
"timestamp": datetime.now(UTC).isoformat(),
}
except Exception:
pass
return {"error": "Mempool data unavailable"}
# ═══════════════════════════════════════════════════════════
# COINGECKO — Price data, trending, global metrics
# ═══════════════════════════════════════════════════════════
@router.get("/coingecko/ping")
async def coingecko_ping():
try:
from app.coingecko_connector import get_coingecko_connector
cg = get_coingecko_connector()
result = await cg.ping()
return {"ping": result, "source": "coingecko"}
except Exception as e:
return {"error": str(e)}
@router.get("/coingecko/trending")
async def coingecko_trending():
try:
from app.coingecko_connector import get_coingecko_connector
cg = get_coingecko_connector()
result = await cg.get_trending()
return {"trending": result, "source": "coingecko"}
except Exception as e:
return {"error": str(e)}
@router.get("/coingecko/markets")
async def coingecko_markets(vs_currency: str = "usd", per_page: int = 50):
try:
from app.coingecko_connector import get_coingecko_connector
cg = get_coingecko_connector()
result = await cg.get_market_overview(vs_currency=vs_currency, per_page=per_page)
return {"markets": result, "vs_currency": vs_currency, "source": "coingecko"}
except Exception as e:
return {"error": str(e)}
@router.get("/coingecko/price/{coin_id}")
async def coingecko_price(coin_id: str):
try:
from app.coingecko_connector import get_coingecko_connector
cg = get_coingecko_connector()
result = await cg.get_token_price(coin_id)
return {"coin_id": coin_id, "price": result, "source": "coingecko"}
except Exception as e:
return {"error": str(e)}
@router.get("/coingecko/global")
async def coingecko_global():
try:
from app.coingecko_connector import get_coingecko_connector
cg = get_coingecko_connector()
result = await cg.get_global_metrics()
return {"global": result, "source": "coingecko"}
except Exception as e:
return {"error": str(e)}
@router.get("/coingecko/coin/{coin_id}")
async def coingecko_coin(coin_id: str):
try:
from app.coingecko_connector import get_coingecko_connector
cg = get_coingecko_connector()
result = await cg.get_token_detail(coin_id)
return {"coin_id": coin_id, "coin": result, "source": "coingecko"}
except Exception as e:
return {"error": str(e)}

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"""
RMI Analytics Engine — Real-Time Metrics & Trend Visualization
===============================================================
Comprehensive analytics system for the RugMunch Intelligence Platform.
Features:
• Real-Time Metrics — CPU, memory, requests, errors, latency
• Time-Series Storage — Redis-backed rolling windows
• Trend Detection — automatic anomaly detection, trend arrows
• User Analytics — DAU, MAU, retention, cohort analysis
• Financial Analytics — revenue, ARPU, MRR, churn
• Security Analytics — threats blocked, bot traffic, attack patterns
• Token Analytics — deployment stats, airdrop metrics, holder growth
• Custom Dashboards — configurable widget layouts
• Export — CSV, JSON, Prometheus metrics
Integrations:
- Prometheus metrics export
- Grafana-compatible data format
- WebSocket real-time streaming
- ClickHouse for long-term storage
Author: RMI Analytics Team
Date: 2026-05-31
"""
import logging
import os
import time
from dataclasses import asdict, dataclass, field
from datetime import UTC, datetime
from typing import Any
logger = logging.getLogger("rmi_analytics")
# ── Data Models ─────────────────────────────────────────────
@dataclass
class MetricPoint:
"""Single time-series data point."""
timestamp: float
value: float
labels: dict[str, str] = field(default_factory=dict)
def to_dict(self) -> dict:
return asdict(self)
@dataclass
class MetricSeries:
"""Time-series metric with metadata."""
name: str
description: str
unit: str
points: list[MetricPoint] = field(default_factory=list)
def latest(self) -> float | None:
return self.points[-1].value if self.points else None
def avg(self, n: int = 60) -> float:
vals = [p.value for p in self.points[-n:]]
return sum(vals) / len(vals) if vals else 0.0
def trend(self, window: int = 10) -> str:
"""Return trend direction: up, down, flat."""
if len(self.points) < window * 2:
return "flat"
old_avg = sum(p.value for p in self.points[-window * 2 : -window]) / window
new_avg = sum(p.value for p in self.points[-window:]) / window
diff = new_avg - old_avg
if abs(diff) < 0.01 * old_avg:
return "flat"
return "up" if diff > 0 else "down"
def to_dict(self) -> dict:
return {
"name": self.name,
"description": self.description,
"unit": self.unit,
"latest": self.latest(),
"avg_1m": self.avg(60),
"trend": self.trend(),
"point_count": len(self.points),
}
@dataclass
class DashboardWidget:
"""Dashboard widget configuration."""
widget_id: str
widget_type: str # line, bar, gauge, counter, table, pie
title: str
metric_name: str
width: int = 6 # Grid columns (1-12)
height: int = 4
refresh_interval: int = 30 # seconds
config: dict[str, Any] = field(default_factory=dict)
@dataclass
class Dashboard:
"""Dashboard configuration."""
dashboard_id: str
name: str
description: str
widgets: list[DashboardWidget] = field(default_factory=list)
created_by: str = ""
is_default: bool = False
# ── Analytics Engine ────────────────────────────────────────
class AnalyticsEngine:
"""
Core analytics engine for real-time metrics and trend analysis.
"""
def __init__(self):
self._metrics: dict[str, MetricSeries] = {}
self._dashboards: dict[str, Dashboard] = {}
self._ensure_default_dashboards()
def _ensure_default_dashboards(self):
"""Create default system dashboards."""
# System Health Dashboard
system_widgets = [
DashboardWidget("cpu_gauge", "gauge", "CPU Usage", "cpu_percent", 3, 3, 10),
DashboardWidget("mem_gauge", "gauge", "Memory Usage", "memory_percent", 3, 3, 10),
DashboardWidget("disk_gauge", "gauge", "Disk Usage", "disk_percent", 3, 3, 10),
DashboardWidget("req_counter", "counter", "Requests/min", "requests_per_minute", 3, 3, 10),
DashboardWidget("cpu_line", "line", "CPU History", "cpu_percent", 6, 4, 30),
DashboardWidget("mem_line", "line", "Memory History", "memory_percent", 6, 4, 30),
DashboardWidget("latency_line", "line", "Response Latency", "response_time_ms", 6, 4, 30),
DashboardWidget("error_line", "line", "Error Rate", "error_rate", 6, 4, 30),
]
self._dashboards["system"] = Dashboard(
dashboard_id="system",
name="System Health",
description="Real-time system performance metrics",
widgets=system_widgets,
is_default=True,
)
# Financial Dashboard
financial_widgets = [
DashboardWidget("revenue_counter", "counter", "Total Revenue", "revenue_usd", 3, 3, 60),
DashboardWidget("mrr_counter", "counter", "MRR", "mrr_usd", 3, 3, 60),
DashboardWidget("arpu_counter", "counter", "ARPU", "arpu_usd", 3, 3, 60),
DashboardWidget("churn_gauge", "gauge", "Churn Rate", "churn_rate", 3, 3, 60),
DashboardWidget("revenue_line", "line", "Revenue Trend", "revenue_usd", 6, 4, 300),
DashboardWidget("payments_line", "line", "Payments", "payments_count", 6, 4, 300),
]
self._dashboards["financial"] = Dashboard(
dashboard_id="financial",
name="Financial Analytics",
description="Revenue, payments, and subscription metrics",
widgets=financial_widgets,
is_default=True,
)
# Security Dashboard
security_widgets = [
DashboardWidget("threats_counter", "counter", "Threats Blocked", "threats_blocked", 3, 3, 30),
DashboardWidget("bots_counter", "counter", "Bot Requests", "bot_requests", 3, 3, 30),
DashboardWidget("attacks_counter", "counter", "Attacks", "attacks_detected", 3, 3, 30),
DashboardWidget("blocked_ips_counter", "counter", "Blocked IPs", "blocked_ips", 3, 3, 30),
DashboardWidget("threats_pie", "pie", "Threat Types", "threat_types", 6, 4, 60),
DashboardWidget("attacks_line", "line", "Attack Timeline", "attacks_detected", 6, 4, 60),
]
self._dashboards["security"] = Dashboard(
dashboard_id="security",
name="Security Analytics",
description="Threat detection and security metrics",
widgets=security_widgets,
is_default=True,
)
# User Analytics Dashboard
user_widgets = [
DashboardWidget("dau_counter", "counter", "DAU", "daily_active_users", 3, 3, 60),
DashboardWidget("mau_counter", "counter", "MAU", "monthly_active_users", 3, 3, 60),
DashboardWidget("new_users_counter", "counter", "New Users", "new_users", 3, 3, 60),
DashboardWidget("retention_gauge", "gauge", "Retention", "retention_rate", 3, 3, 60),
DashboardWidget("users_line", "line", "User Growth", "total_users", 6, 4, 300),
DashboardWidget("tiers_pie", "pie", "User Tiers", "users_by_tier", 6, 4, 300),
]
self._dashboards["users"] = Dashboard(
dashboard_id="users",
name="User Analytics",
description="User growth, engagement, and retention",
widgets=user_widgets,
is_default=True,
)
# ── Metric Recording ────────────────────────────────────
def record_metric(self, name: str, value: float, labels: dict[str, str] | None = None):
"""Record a metric data point."""
if name not in self._metrics:
self._metrics[name] = MetricSeries(
name=name,
description=name.replace("_", " ").title(),
unit="",
)
point = MetricPoint(
timestamp=time.time(),
value=value,
labels=labels or {},
)
self._metrics[name].points.append(point)
# Keep only last 10000 points (about 2.7 hours at 1/sec)
if len(self._metrics[name].points) > 10000:
self._metrics[name].points = self._metrics[name].points[-10000:]
def get_metric(self, name: str) -> MetricSeries | None:
"""Get metric series by name."""
return self._metrics.get(name)
def get_metric_names(self) -> list[str]:
"""List all metric names."""
return list(self._metrics.keys())
# ── Dashboard Management ────────────────────────────────
def get_dashboard(self, dashboard_id: str) -> Dashboard | None:
"""Get dashboard by ID."""
return self._dashboards.get(dashboard_id)
def list_dashboards(self) -> list[Dashboard]:
"""List all dashboards."""
return list(self._dashboards.values())
def create_dashboard(self, name: str, description: str, created_by: str = "") -> Dashboard:
"""Create a new dashboard."""
dashboard_id = f"dash_{int(time.time())}_{os.urandom(4).hex()}"
dashboard = Dashboard(
dashboard_id=dashboard_id,
name=name,
description=description,
created_by=created_by,
)
self._dashboards[dashboard_id] = dashboard
return dashboard
def add_widget(self, dashboard_id: str, widget: DashboardWidget) -> bool:
"""Add widget to dashboard."""
dashboard = self._dashboards.get(dashboard_id)
if not dashboard:
return False
dashboard.widgets.append(widget)
return True
# ── Real-Time Data ──────────────────────────────────────
def get_dashboard_data(self, dashboard_id: str) -> dict[str, Any]:
"""Get current data for all widgets in a dashboard."""
dashboard = self._dashboards.get(dashboard_id)
if not dashboard:
return {"error": "Dashboard not found"}
widgets_data = []
for widget in dashboard.widgets:
metric = self._metrics.get(widget.metric_name)
data = {
"widget_id": widget.widget_id,
"widget_type": widget.widget_type,
"title": widget.title,
"metric": metric.to_dict() if metric else {"name": widget.metric_name, "latest": None},
}
# Add historical data for line/bar charts
if widget.widget_type in ["line", "bar"] and metric:
# Return last 60 points
data["history"] = [{"t": p.timestamp, "v": p.value} for p in metric.points[-60:]]
widgets_data.append(data)
return {
"dashboard_id": dashboard_id,
"name": dashboard.name,
"updated_at": datetime.now(UTC).isoformat(),
"widgets": widgets_data,
}
# ── Trend Analysis ──────────────────────────────────────
def detect_trends(self, metric_name: str, window: int = 60) -> dict[str, Any]:
"""Detect trends in a metric."""
metric = self._metrics.get(metric_name)
if not metric or len(metric.points) < window * 2:
return {"error": "Insufficient data"}
points = metric.points[-window * 2 :]
half = len(points) // 2
first_half = [p.value for p in points[:half]]
second_half = [p.value for p in points[half:]]
first_avg = sum(first_half) / len(first_half)
second_avg = sum(second_half) / len(second_half)
change_pct = ((second_avg - first_avg) / first_avg * 100) if first_avg else 0
# Detect anomalies (values outside 2 std dev)
all_vals = [p.value for p in metric.points[-window:]]
mean = sum(all_vals) / len(all_vals)
variance = sum((v - mean) ** 2 for v in all_vals) / len(all_vals)
std_dev = variance**0.5
anomalies = [
{"timestamp": p.timestamp, "value": p.value}
for p in metric.points[-window:]
if abs(p.value - mean) > 2 * std_dev
]
return {
"metric": metric_name,
"trend": metric.trend(window),
"change_percent": round(change_pct, 2),
"first_period_avg": round(first_avg, 4),
"second_period_avg": round(second_avg, 4),
"anomalies_count": len(anomalies),
"anomalies": anomalies[:5], # Top 5
}
# ── Statistics ───────────────────────────────────────────
def get_system_stats(self) -> dict[str, Any]:
"""Get comprehensive system statistics."""
return {
"metrics_tracked": len(self._metrics),
"dashboards": len(self._dashboards),
"total_data_points": sum(len(m.points) for m in self._metrics.values()),
"last_updated": datetime.now(UTC).isoformat(),
"top_metrics": [
{"name": name, "points": len(m.points), "latest": m.latest()}
for name, m in sorted(self._metrics.items(), key=lambda x: len(x[1].points), reverse=True)[:10]
],
}
# ── Prometheus Export ───────────────────────────────────
def to_prometheus(self) -> str:
"""Export metrics in Prometheus text format."""
lines = []
for name, metric in self._metrics.items():
prom_name = f"rmi_{name}"
lines.append(f"# HELP {prom_name} {metric.description}")
lines.append(f"# TYPE {prom_name} gauge")
latest = metric.latest()
if latest is not None:
labels_str = ", ".join(f'{k}="{v}"' for k, v in metric.points[-1].labels.items())
if labels_str:
lines.append(f"{prom_name}{{{labels_str}}} {latest}")
else:
lines.append(f"{prom_name} {latest}")
return "\n".join(lines)
# ── Export ────────────────────────────────────────────
def export_metric(self, name: str, format: str = "json") -> Any:
"""Export metric data."""
metric = self._metrics.get(name)
if not metric:
return None
if format == "json":
return {
"name": metric.name,
"description": metric.description,
"unit": metric.unit,
"data": [{"timestamp": p.timestamp, "value": p.value, "labels": p.labels} for p in metric.points],
}
elif format == "csv":
lines = ["timestamp,value"]
for p in metric.points:
lines.append(f"{p.timestamp},{p.value}")
return "\n".join(lines)
return None
# ── Singleton ─────────────────────────────────────────────────
_analytics_instance: AnalyticsEngine | None = None
def get_analytics_engine() -> AnalyticsEngine:
"""Get or create analytics engine instance."""
global _analytics_instance
if _analytics_instance is None:
_analytics_instance = AnalyticsEngine()
return _analytics_instance

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"""
Historical Data Storage & Analytics Module
==========================================
Provides persistent storage for:
- Transaction history
- Entity relationships
- Alert history
- Analytics queries
Uses Redis for fast-access cache and optional PostgreSQL for long-term storage.
"""
import json
import logging
from datetime import datetime
from typing import Any
import redis
from pydantic import BaseModel, Field
logger = logging.getLogger(__name__)
# ─── PERSISTENT MODELS ────────────────────────────────────────────────
class TransactionRecord(BaseModel):
"""Represents a transaction record."""
tx_hash: str
chain: str = "ethereum"
from_address: str
to_address: str
value: float = 0.0
gas_used: int = 0
gas_price: int = 0
block_number: int = 0
timestamp: datetime = Field(default_factory=datetime.utcnow)
status: int = 1 # 1 = success, 0 = failed
function_name: str = ""
token_transfers: list[dict[str, Any]] = Field(default_factory=list)
class WalletHistory(BaseModel):
"""Complete transaction history for a wallet."""
wallet_address: str
chain: str = "ethereum"
transactions: list[TransactionRecord] = Field(default_factory=list)
first_seen: datetime = Field(default_factory=datetime.utcnow)
last_seen: datetime = Field(default_factory=datetime.utcnow)
total_tx_count: int = 0
total_volume: float = 0.0
unique_interactions: int = 0
class EntityAlertRecord(BaseModel):
"""Record of an alert for an entity."""
alert_id: str
entity_id: str
alert_type: str
severity: str
message: str
timestamp: datetime = Field(default_factory=datetime.utcnow)
metadata: dict[str, Any] = Field(default_factory=dict)
resolved: bool = False
# ─── REDIS STORAGE ────────────────────────────────────────────────────
class RedisStorage:
"""Redis-backed storage for historical data."""
def __init__(self, host: str = "localhost", port: int = 6379, db: int = 0):
self.host = host
self.port = port
self.db = db
self.client = redis.Redis(host=host, port=port, db=db, decode_responses=True)
logger.info(f"Connected to Redis at {host}:{port}/{db}")
def key_prefix(self, category: str, identifier: str) -> str:
"""Generate a Redis key."""
return f"rmi:{category}:{identifier}"
def save_transaction(self, tx: TransactionRecord) -> bool:
"""Save a transaction record."""
try:
key = self.key_prefix("transaction", tx.tx_hash)
self.client.setex(
key,
86400 * 30, # 30 days TTL
tx.json(),
)
# Index by wallet
wallet_key = self.key_prefix("wallet", f"{tx.from_address}_{tx.chain}")
self.client.zadd(wallet_key, {tx.tx_hash: tx.timestamp.timestamp()})
# Update wallet history
self._update_wallet_history(tx.wallet_address if hasattr(tx, "wallet_address") else tx.from_address, tx)
return True
except Exception as e:
logger.error(f"Failed to save transaction: {e}")
return False
def get_transaction(self, tx_hash: str) -> dict[str, Any] | None:
"""Get a transaction record."""
try:
key = self.key_prefix("transaction", tx_hash)
data = self.client.get(key)
return json.loads(data) if data else None
except Exception as e:
logger.error(f"Failed to get transaction: {e}")
return None
def get_wallet_history(self, wallet_address: str, chain: str = "ethereum") -> WalletHistory | None:
"""Get complete wallet history."""
try:
key = self.key_prefix("wallet", f"{wallet_address}_{chain}")
if not self.client.exists(key):
return None
tx_hashes = self.client.zrange(key, 0, -1, withscores=True)
transactions = []
for tx_hash, score in tx_hashes:
tx = self.get_transaction(tx_hash)
if tx:
tx_record = TransactionRecord(**tx)
tx_record.timestamp = datetime.fromtimestamp(score)
transactions.append(tx_record)
# Sort by timestamp
transactions.sort(key=lambda x: x.timestamp)
return WalletHistory(
wallet_address=wallet_address,
chain=chain,
transactions=transactions,
total_tx_count=len(transactions),
)
except Exception as e:
logger.error(f"Failed to get wallet history: {e}")
return None
def save_alert(self, alert: EntityAlertRecord) -> bool:
"""Save an alert record."""
try:
key = self.key_prefix("alert", alert.alert_id)
self.client.setex(
key,
86400 * 90, # 90 days TTL
alert.json(),
)
# Index by entity
entity_key = self.key_prefix("entity_alert", alert.entity_id)
self.client.zadd(entity_key, {alert.alert_id: alert.timestamp.timestamp()})
return True
except Exception as e:
logger.error(f"Failed to save alert: {e}")
return False
def get_entity_alerts(self, entity_id: str, limit: int = 100) -> list[dict[str, Any]]:
"""Get alerts for an entity."""
try:
key = self.key_prefix("entity_alert", entity_id)
if not self.client.exists(key):
return []
alert_ids = self.client.zrange(key, 0, limit - 1)
alerts = []
for alert_id in alert_ids:
alert = self.get_alert(alert_id)
if alert:
alerts.append(alert)
return alerts
except Exception as e:
logger.error(f"Failed to get entity alerts: {e}")
return []
def get_alert(self, alert_id: str) -> dict[str, Any] | None:
"""Get an alert record."""
try:
key = self.key_prefix("alert", alert_id)
data = self.client.get(key)
return json.loads(data) if data else None
except Exception as e:
logger.error(f"Failed to get alert: {e}")
return None
def save_entity_relation(self, from_entity: str, to_entity: str, relation: str):
"""Save an entity relationship."""
try:
key = self.key_prefix("entity_relation", from_entity)
self.client.sadd(key, json.dumps({"to": to_entity, "relation": relation}))
except Exception as e:
logger.error(f"Failed to save entity relation: {e}")
def get_entity_relations(self, entity_id: str) -> list[dict[str, str]]:
"""Get relations for an entity."""
try:
key = self.key_prefix("entity_relation", entity_id)
relations = self.client.smembers(key)
return [json.loads(r) for r in relations]
except Exception as e:
logger.error(f"Failed to get entity relations: {e}")
return []
def save_wallet_cluster(self, cluster_id: str, members: list[str], labels: list[str]):
"""Save a wallet cluster."""
try:
key = self.key_prefix("cluster", cluster_id)
data = {
"cluster_id": cluster_id,
"members": members,
"labels": labels,
"created_at": datetime.utcnow().isoformat(),
}
self.client.setex(key, 86400 * 365, json.dumps(data)) # 1 year TTL
except Exception as e:
logger.error(f"Failed to save cluster: {e}")
def get_wallet_cluster(self, cluster_id: str) -> dict[str, Any] | None:
"""Get a wallet cluster."""
try:
key = self.key_prefix("cluster", cluster_id)
data = self.client.get(key)
return json.loads(data) if data else None
except Exception as e:
logger.error(f"Failed to get cluster: {e}")
return None
def get_or_create_wallet_history(self, wallet_address: str, chain: str = "ethereum") -> WalletHistory:
"""Get or create wallet history."""
history = self.get_wallet_history(wallet_address, chain)
if history is None:
history = WalletHistory(wallet_address=wallet_address, chain=chain)
return history
def _update_wallet_history(self, wallet_address: str, tx: TransactionRecord):
"""Update wallet history metadata."""
history = self.get_or_create_wallet_history(wallet_address, tx.chain)
# Update last seen
history.last_seen = tx.timestamp
# Update total volume
history.total_volume += tx.value
# Update interaction count
if tx.to_address not in [t.to_address for t in history.transactions]:
history.unique_interactions += 1
# Update transaction count
history.total_tx_count = len(history.transactions) + 1
# ─── DATABASE WRAPPER ─────────────────────────────────────────────────
class AnalyticsDatabase:
"""Database wrapper for analytics queries."""
def __init__(self, redis_host: str = "localhost", redis_port: int = 6379, redis_db: int = 0):
self.redis = RedisStorage(host=redis_host, port=redis_port, db=redis_db)
def store_transaction(self, tx: TransactionRecord) -> bool:
"""Store a transaction."""
return self.redis.save_transaction(tx)
def store_entity_alert(self, alert: EntityAlertRecord) -> bool:
"""Store an entity alert."""
return self.redis.save_alert(alert)
def store_entity_relation(self, from_entity: str, to_entity: str, relation: str):
"""Store an entity relationship."""
self.redis.save_entity_relation(from_entity, to_entity, relation)
def store_wallet_cluster(self, cluster_id: str, members: list[str], labels: list[str]):
"""Store a wallet cluster."""
self.redis.save_wallet_cluster(cluster_id, members, labels)
def get_wallet_history(self, wallet_address: str, chain: str = "ethereum") -> WalletHistory | None:
"""Get wallet history."""
return self.redis.get_wallet_history(wallet_address, chain)
def get_entity_alerts(self, entity_id: str, limit: int = 100) -> list[dict[str, Any]]:
"""Get entity alerts."""
return self.redis.get_entity_alerts(entity_id, limit)
def get_entity_relations(self, entity_id: str) -> list[dict[str, str]]:
"""Get entity relations."""
return self.redis.get_entity_relations(entity_id)
def get_wallet_cluster(self, cluster_id: str) -> dict[str, Any] | None:
"""Get wallet cluster."""
return self.redis.get_wallet_cluster(cluster_id)
def get_transaction(self, tx_hash: str) -> dict[str, Any] | None:
"""Get transaction by hash."""
return self.redis.get_transaction(tx_hash)
# ─── ANALYTICS QUERIES ────────────────────────────────────────────
def get_wallet_activity_summary(self, wallet_address: str, chain: str = "ethereum") -> dict[str, Any]:
"""Get activity summary for a wallet."""
history = self.redis.get_wallet_history(wallet_address, chain)
if history is None or not history.transactions:
return {
"wallet_address": wallet_address,
"chain": chain,
"total_transactions": 0,
"total_volume": 0,
"first_seen": None,
"last_seen": None,
"unique_contracts": 0,
}
return {
"wallet_address": wallet_address,
"chain": chain,
"total_transactions": len(history.transactions),
"total_volume": history.total_volume,
"first_seen": history.first_seen.isoformat(),
"last_seen": history.last_seen.isoformat(),
"unique_contracts": history.unique_interactions,
}
def get_wallet_similarity(self, address1: str, address2: str) -> dict[str, Any]:
"""Calculate similarity between two wallets based on interactions."""
history1 = self.redis.get_wallet_history(address1, "ethereum")
history2 = self.redis.get_wallet_history(address2, "ethereum")
if not history1 or not history2 or not history1.transactions or not history2.transactions:
return {"similarity": 0, "shared_contracts": [], "reason": "Insufficient data"}
# Get unique contract interactions
contracts1 = {t.to_address for t in history1.transactions}
contracts2 = {t.to_address for t in history2.transactions}
# Calculate Jaccard similarity
intersection = contracts1 & contracts2
union = contracts1 | contracts2
jaccard = len(intersection) / len(union) if union else 0
return {
"similarity": round(jaccard, 4),
"shared_contracts": list(intersection)[:10], # Top 10
"total_shared": len(intersection),
"unique_1": len(contracts1 - contracts2),
"unique_2": len(contracts2 - contracts1),
}
def get_entity_network(self, entity_id: str, depth: int = 2) -> dict[str, Any]:
"""Get entity's network of connected entities."""
relations = self.redis.get_entity_relations(entity_id)
network = {"entity_id": entity_id, "direct_relations": relations, "depth": depth}
# If depth > 0, get relations of related entities
if depth > 0:
related_entities = [r["to"] for r in relations]
network["related_entities"] = related_entities
if depth >= 2:
network["second_degree"] = []
for related in related_entities:
second_degree = self.redis.get_entity_relations(related)
network["second_degree"].extend(second_degree)
return network
# ─── SINGLETON INSTANCE ───────────────────────────────────────────────
_db_instance: AnalyticsDatabase | None = None
def get_analytics_database(
redis_host: str | None = None, redis_port: int | None = None, redis_db: int | None = None
) -> AnalyticsDatabase:
"""Get the analytics database instance."""
global _db_instance
if _db_instance is None:
_db_instance = AnalyticsDatabase(
redis_host=redis_host or "localhost",
redis_port=redis_port or 6379,
redis_db=redis_db or 0,
)
return _db_instance
# ─── INITIAL DATA IMPORT ──────────────────────────────────────────────
def initialize_analytics():
"""Initialize analytics storage with default data."""
get_analytics_database()
# Clear old data (optional - for fresh starts)
# db.redis.client.flushdb()
logger.info("Analytics database initialized")
if __name__ == "__main__":
# Test the analytics database
db = get_analytics_database()
# Create a test transaction
tx = TransactionRecord(
tx_hash="0x" + "a" * 64,
chain="ethereum",
from_address="0x1234567890123456789012345678901234567890",
to_address="0xabcdef1234567890abcdef1234567890abcdef12",
value=1.5,
gas_used=21000,
block_number=1000000,
function_name="transfer",
)
db.store_transaction(tx)
# Get the transaction back
stored = db.get_transaction(tx.tx_hash)
print(f"Stored transaction: {stored}")
# Get wallet history
history = db.get_wallet_history(tx.from_address, "ethereum")
if history:
print(f"Wallet history: {history.total_tx_count} transactions")
# Get activity summary
summary = db.get_wallet_activity_summary(tx.from_address, "ethereum")
print(f"Activity summary: {summary}")

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"""
Multi-chain portfolio scanner.
Scans the same wallet address across all supported chains.
"""
import time
from app.adapters.binance_web3 import CHAIN_IDS, CHAIN_NAMES, get_wallet_holdings
from app.analyzer.portfolio import build_portfolio
SCAN_DELAY = 0.3 # seconds between chain requests
def scan_all_chains(address: str) -> dict:
"""
Scan a wallet across all supported chains.
Returns:
{
"chains": {
"BSC": {"total_value": float, "token_count": int, "change_24h_pct": float},
...
},
"grand_total": float,
"grand_change_24h_usd": float,
"grand_change_24h_pct": float,
"errors": [...],
}
"""
result = {
"chains": {},
"grand_total": 0.0,
"grand_change_24h_usd": 0.0,
"grand_change_24h_pct": 0.0,
"errors": [],
}
grand_yesterday = 0.0
for chain_key, chain_id in CHAIN_IDS.items():
try:
holdings = get_wallet_holdings(address, chain_id)
portfolio = build_portfolio(holdings)
if portfolio["total_value"] > 0:
chain_name = CHAIN_NAMES.get(chain_id, chain_key.upper())
result["chains"][chain_name] = {
"total_value": portfolio["total_value"],
"token_count": portfolio["token_count"],
"change_24h_usd": portfolio["change_24h_usd"],
"change_24h_pct": portfolio["change_24h_pct"],
}
result["grand_total"] += portfolio["total_value"]
result["grand_change_24h_usd"] += portfolio["change_24h_usd"]
grand_yesterday += portfolio["total_value"] - portfolio["change_24h_usd"]
time.sleep(SCAN_DELAY)
except Exception as e:
result["errors"].append(f"{chain_key.upper()}: {e}")
if grand_yesterday > 0:
result["grand_change_24h_pct"] = (result["grand_change_24h_usd"] / grand_yesterday) * 100
return result

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"""
Token-level PnL calculator.
Calculates profit/loss when user provides their average buy price.
"""
def calculate_token_pnl(token: dict, avg_cost: float) -> dict:
"""
Calculate PnL for a specific token given user's average buy price.
Args:
token: Enriched token dict from build_portfolio()
avg_cost: User's average buy price in USD
Returns:
{
"symbol": str,
"qty": float,
"avg_cost": float,
"current_price": float,
"cost_basis": float,
"current_value": float,
"pnl_usd": float,
"pnl_pct": float,
"is_profit": bool,
}
"""
qty = token.get("qty", 0)
current_price = token.get("price", 0)
cost_basis = avg_cost * qty
current_value = current_price * qty
pnl_usd = current_value - cost_basis
pnl_pct = (pnl_usd / cost_basis * 100) if cost_basis > 0 else 0.0
return {
"symbol": token.get("symbol", "?"),
"name": token.get("name", "?"),
"qty": qty,
"avg_cost": avg_cost,
"current_price": current_price,
"cost_basis": cost_basis,
"current_value": current_value,
"pnl_usd": pnl_usd,
"pnl_pct": pnl_pct,
"is_profit": pnl_usd >= 0,
}

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"""
Portfolio aggregator: calculates total value and 24h change from holdings.
"""
def build_portfolio(holdings: list) -> dict:
"""
Aggregate token holdings into a portfolio summary.
Args:
holdings: Raw list from get_wallet_holdings()
Returns:
{
"tokens": [...enriched tokens with usd_value, qty],
"total_value": float,
"change_24h_usd": float,
"change_24h_pct": float,
"token_count": int,
}
"""
tokens = []
total_value = 0.0
total_value_yesterday = 0.0
for item in holdings:
price = float(item.get("price") or 0)
qty_raw = item.get("remainQty") or "0"
qty = float(qty_raw) if qty_raw else 0.0
change_24h = float(item.get("percentChange24h") or 0)
usd_value = price * qty
if usd_value < 0.01:
continue # skip dust
usd_value_yesterday = usd_value / (1 + change_24h / 100) if change_24h != -100 else usd_value
total_value += usd_value
total_value_yesterday += usd_value_yesterday
tokens.append(
{
"symbol": item.get("symbol", "?"),
"name": item.get("name", "?"),
"contractAddress": item.get("contractAddress", ""),
"qty": qty,
"price": price,
"usd_value": usd_value,
"change_24h_pct": change_24h,
"risk_level": item.get("riskLevel", "UNKNOWN"),
}
)
tokens.sort(key=lambda t: t["usd_value"], reverse=True)
change_24h_usd = total_value - total_value_yesterday
change_24h_pct = (change_24h_usd / total_value_yesterday * 100) if total_value_yesterday > 0 else 0.0
return {
"tokens": tokens,
"total_value": total_value,
"change_24h_usd": change_24h_usd,
"change_24h_pct": change_24h_pct,
"token_count": len(tokens),
}

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#!/usr/bin/env python3
"""
FAISS-based ANN Index Manager for RMI RAG
==========================================
Replaces O(n) brute-force Redis cosine scan with sub-millisecond
FAISS HNSW / IVFFlat approximate nearest-neighbor search.
Architecture:
- Loads all vectors from Redis for each collection into a FAISS index
- Keeps index in memory; auto-rebuilds when stale
- Persists pickled indexes to /app/data/faiss/{collection}.index
- Tracks version counter in Redis key rag:idx_version:{collection}
- Invalidate on new ingestion (version bump)
"""
import asyncio
import json
import logging
import os
import pickle
import time
from typing import Any, Optional
import numpy as np
logger = logging.getLogger(__name__)
REDIS_HOST = os.getenv("REDIS_HOST", "rmi-redis")
REDIS_PORT = int(os.getenv("REDIS_PORT", "6379"))
REDIS_PASSWORD = os.getenv("REDIS_PASSWORD", "")
FAISS_DATA_DIR = os.getenv("FAISS_DATA_DIR", "/app/data/faiss")
# HNSW defaults
HNSW_M = 16
HNSW_EF_CONSTRUCTION = 200
HNSW_EF_SEARCH = 128
# IVFFlat defaults
IVF_LISTS_FACTOR = 40 # lists = n_vectors / factor, min 4
IVF_NPROBE = 16
# Minimum docs to use IVFFlat/HNSW; below this, flat search is fine
MIN_DOCS_FOR_ANN = 50
class ANNIndex:
"""
FAISS-backed approximate nearest-neighbor index manager.
Each collection gets its own FAISS index built from Redis-stored
vectors. The index is kept in process memory and persisted to
disk so it survives restarts.
Usage:
idx = ANNIndex()
await idx.build_index("scam_patterns")
results = idx.search(query_embedding, "scam_patterns", limit=10)
"""
_instance: Optional["ANNIndex"] = None
def __init__(self):
self._indexes: dict[str, Any] = {} # collection -> faiss index
self._id_maps: dict[str, list[str]] = {} # collection -> [doc_id, ...]
self._meta: dict[str, dict] = {} # collection -> build metadata
self._redis = None
@classmethod
def get_instance(cls) -> "ANNIndex":
if cls._instance is None:
cls._instance = cls()
return cls._instance
async def _get_redis(self):
import redis.asyncio as aioredis
if self._redis is None:
self._redis = aioredis.Redis(
host=REDIS_HOST,
port=REDIS_PORT,
password=REDIS_PASSWORD or None,
db=0,
decode_responses=True,
)
return self._redis
# ── Build ────────────────────────────────────────────────────
async def build_index(self, collection: str, force: bool = False) -> dict[str, Any]:
"""
Build (or rebuild) a FAISS index for *collection*.
Reads all documents from Redis rag:{collection}:* and builds
an HNSW or IVFFlat index depending on document count.
Returns build metadata dict.
"""
# Skip if fresh enough (unless forced)
if not force and self.is_built(collection):
version_redis = await self._get_version(collection)
version_local = self._meta.get(collection, {}).get("version", -1)
if version_redis == version_local:
logger.info(f"ANN index for {collection} is fresh (v{version_local})")
return self._meta.get(collection, {})
r = await self._get_redis()
# Fetch all document IDs
doc_ids = list(await r.smembers(f"rag:idx:{collection}"))
n = len(doc_ids)
if n == 0:
logger.warning(f"No documents found for {collection}")
self._meta[collection] = {"status": "empty", "n": 0, "collection": collection}
return self._meta[collection]
# Batch-fetch documents
keys = [f"rag:{collection}:{did}" for did in doc_ids]
pipe = r.pipeline()
for k in keys:
pipe.get(k)
raw_docs = await pipe.execute()
# Extract vectors and metadata; track dimension
vectors = []
valid_ids = []
dims = 0
for i, data in enumerate(raw_docs):
if not data:
continue
try:
doc = json.loads(data)
except json.JSONDecodeError:
continue
vec = doc.get("vector", [])
# Handle JSON-string vectors (from hash re-embed)
if isinstance(vec, str):
try:
vec = json.loads(vec)
except (json.JSONDecodeError, TypeError):
continue
if not vec or not isinstance(vec, list):
continue
if dims == 0:
dims = len(vec)
if len(vec) != dims:
# Pad or truncate to match first vector's dimension
vec = vec + [0.0] * (dims - len(vec)) if len(vec) < dims else vec[:dims]
vectors.append(vec)
valid_ids.append(doc_ids[i])
n_valid = len(vectors)
if n_valid == 0:
logger.warning(f"No valid vectors for {collection}")
self._meta[collection] = {"status": "no_vectors", "n": 0, "collection": collection}
return self._meta[collection]
mat = np.array(vectors, dtype=np.float32)
# Choose index type
import faiss
if n_valid < MIN_DOCS_FOR_ANN:
# Flat index — exact search, small collection
index = faiss.IndexFlatIP(dims) # inner product (cosine after norm)
index_type = "flat"
else:
# Normalize vectors for cosine similarity via inner product
faiss.normalize_L2(mat)
# Try HNSW first (best quality, no training needed)
try:
index = faiss.IndexHNSWFlat(dims, HNSW_M, faiss.METRIC_INNER_PRODUCT)
index.hnsw.efConstruction = HNSW_EF_CONSTRUCTION
index.hnsw.efSearch = HNSW_EF_SEARCH
index_type = "hnsw"
logger.info(f"Building HNSW index for {collection}: {n_valid} vectors, {dims}d")
except Exception as e:
logger.warning(f"HNSW failed, falling back to IVFFlat: {e}")
# IVFFlat fallback
nlist = max(4, n_valid // IVF_LISTS_FACTOR)
quantizer = faiss.IndexFlatIP(dims)
index = faiss.IndexIVFFlat(quantizer, dims, nlist, faiss.METRIC_INNER_PRODUCT)
index.nprobe = IVF_NPROBE
index.train(mat)
index_type = "ivfflat"
# Normalize for cosine via inner product (skip if already done for HNSW path)
if index_type == "flat":
faiss.normalize_L2(mat)
index.add(mat)
# Store in memory
self._indexes[collection] = index
self._id_maps[collection] = valid_ids
version = await self._get_version(collection)
# Persist to disk
os.makedirs(FAISS_DATA_DIR, exist_ok=True)
index_path = os.path.join(FAISS_DATA_DIR, f"{collection}.index")
try:
# faiss indexes can be serialized directly
faiss.write_index(index, index_path)
# Save id_map alongside
id_map_path = os.path.join(FAISS_DATA_DIR, f"{collection}.ids")
with open(id_map_path, "wb") as f:
pickle.dump(valid_ids, f)
logger.info(f"Persisted FAISS index to {index_path}")
except Exception as e:
logger.warning(f"Failed to persist FAISS index: {e}")
build_meta = {
"status": "built",
"collection": collection,
"n": n_valid,
"dims": dims,
"index_type": index_type,
"version": version,
"built_at": time.time(),
"persisted": os.path.exists(index_path),
}
self._meta[collection] = build_meta
logger.info(f"ANN index built: {collection} ({n_valid} docs, {dims}d, {index_type})")
return build_meta
# ── Load from disk ────────────────────────────────────────────
def _load_from_disk(self, collection: str) -> bool:
"""Try to load a persisted FAISS index and id_map from disk."""
import faiss
index_path = os.path.join(FAISS_DATA_DIR, f"{collection}.index")
id_map_path = os.path.join(FAISS_DATA_DIR, f"{collection}.ids")
if not os.path.exists(index_path) or not os.path.exists(id_map_path):
return False
try:
index = faiss.read_index(index_path)
with open(id_map_path, "rb") as f:
id_list = pickle.load(f)
self._indexes[collection] = index
self._id_maps[collection] = id_list
self._meta[collection] = {
"status": "loaded",
"collection": collection,
"n": len(id_list),
"dims": index.d,
"index_type": type(index).__name__,
"loaded_at": time.time(),
}
logger.info(f"Loaded FAISS index for {collection} from disk ({len(id_list)} vectors)")
return True
except Exception as e:
logger.warning(f"Failed to load FAISS index from disk: {e}")
return False
# ── Search ────────────────────────────────────────────────────
async def search(
self,
query_embedding: list[float],
collection: str,
limit: int = 10,
min_similarity: float = 0.0,
) -> list[dict[str, Any]]:
"""
ANN search: find top-k documents similar to query_embedding.
Auto-builds the index on first search if not yet built.
Hydrates results with content/metadata from Redis.
Returns list of {id, similarity, content, metadata, source, severity} dicts.
"""
# Auto-build if needed
if not self.is_built(collection):
# Try disk first, then build from Redis (disk I/O offloaded to thread)
loaded = await asyncio.to_thread(self._load_from_disk, collection)
if not loaded:
await self.build_index(collection)
if not self.is_built(collection):
logger.warning(f"No ANN index available for {collection}")
return []
index = self._indexes[collection]
id_list = self._id_maps[collection]
dims = index.d
# Prepare query vector
q = np.array([query_embedding[:dims]], dtype=np.float32)
# Pad if query is shorter
if q.shape[1] < dims:
q = np.pad(q, ((0, 0), (0, dims - q.shape[1])))
# Truncate if query is longer
if q.shape[1] > dims:
q = q[:, :dims]
# Normalize for cosine via inner product
import faiss
faiss.normalize_L2(q)
# Search
search_k = min(limit * 2, len(id_list)) # fetch extra for filtering
distances, indices = index.search(q, search_k)
# Collect matching doc IDs for hydration
raw_hits = []
for rank, (dist, idx) in enumerate(zip(distances[0], indices[0], strict=False)):
if idx < 0:
continue # FAISS returns -1 for empty slots
sim = float(dist) # inner product on normalized vectors = cosine similarity
if sim < min_similarity:
continue
doc_id = id_list[idx] if idx < len(id_list) else f"unknown_{idx}"
raw_hits.append((doc_id, sim, rank))
if not raw_hits:
return []
# Hydrate from Redis — batch-fetch all matched docs
r = await self._get_redis()
keys = [f"rag:{collection}:{doc_id}" for doc_id, _, _ in raw_hits]
pipe = r.pipeline()
for k in keys:
pipe.get(k)
raw_docs = await pipe.execute()
results = []
for (doc_id, sim, rank), data in zip(raw_hits, raw_docs, strict=False):
result = {
"id": doc_id,
"similarity": round(sim, 4),
"rank": rank,
}
if data:
try:
doc = json.loads(data)
result["content"] = doc.get("content", "")[:500]
result["metadata"] = doc.get("metadata", {})
result["source"] = doc.get("source", "")
result["severity"] = doc.get("severity", "")
except json.JSONDecodeError:
pass
results.append(result)
results.sort(key=lambda x: x["similarity"], reverse=True)
return results[:limit]
# ── Status ────────────────────────────────────────────────────
def is_built(self, collection: str) -> bool:
"""Return True if an in-memory index exists for the collection."""
return collection in self._indexes and collection in self._id_maps
def stats(self) -> dict[str, Any]:
"""Return stats for all loaded indexes."""
out = {}
for coll in set(list(self._indexes.keys()) + list(self._meta.keys())):
idx = self._indexes.get(coll)
out[coll] = {
"built": coll in self._indexes,
"n_vectors": len(self._id_maps.get(coll, [])),
"dims": idx.d if idx else 0,
"index_type": type(idx).__name__ if idx else "none",
**self._meta.get(coll, {}),
}
return out
# ── Version tracking ─────────────────────────────────────────
async def _get_version(self, collection: str) -> int:
"""Get the current version counter from Redis."""
r = await self._get_redis()
val = await r.get(f"rag:idx_version:{collection}")
return int(val) if val else 0
async def bump_version(self, collection: str) -> int:
"""
Bump the version counter (call after new ingestion).
This signals that the index needs rebuilding.
"""
r = await self._get_redis()
new_ver = await r.incr(f"rag:idx_version:{collection}")
# Invalidate in-memory index
self._indexes.pop(collection, None)
self._id_maps.pop(collection, None)
logger.info(f"Version bumped for {collection}: now v{new_ver}")
return new_ver
# ── Invalidate ────────────────────────────────────────────────
def invalidate(self, collection: str) -> None:
"""Drop the in-memory index for *collection* (next search will rebuild)."""
self._indexes.pop(collection, None)
self._id_maps.pop(collection, None)
self._meta.pop(collection, None)
logger.info(f"Invalidated ANN index for {collection}")
# ══════════════════════════════════════════════════════════════════════
# Singleton accessor
# ══════════════════════════════════════════════════════════════════════
_ann_index: ANNIndex | None = None
def get_ann_index() -> ANNIndex:
"""Return the singleton ANNIndex instance."""
global _ann_index
if _ann_index is None:
_ann_index = ANNIndex()
return _ann_index

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"""HTTP transport layer. Routes are thin: parse → call domain service → return."""

34
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"""Shared FastAPI dependencies.
Use these in route signatures to inject cross-cutting concerns:
from app.api.deps import get_redis, get_current_user, get_settings
Actual implementations live in `app/core/`. This module is a re-export
facade so route authors don't need to know which core module owns what.
"""
from __future__ import annotations
# Re-exports — actual implementations come from app/core/.
# Core modules are populated by the parallel DeepSeek tasks (DS-1..DS-10).
# Until then, these imports will fail; routes should not depend on them yet.
try:
from app.core.redis import get_redis
except ImportError:
get_redis = None # type: ignore[assignment]
try:
from app.core.db import get_db
except ImportError:
get_db = None # type: ignore[assignment]
try:
from app.core.auth import get_current_user, get_optional_user
except ImportError:
get_current_user = None # type: ignore[assignment]
get_optional_user = None # type: ignore[assignment]
try:
from app.core.config import settings
except ImportError:
pass # fallback until core.config lands

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"""V1 API router aggregator.
The strangle: new v1 routes are added here as domains migrate. The legacy
main.py still mounts all old routes; we ADD new v1 routes on top so they
co-exist until cutover.
To add a new domain:
1. Create app/api/v1/<group>/<domain>.py with APIRouter
2. Import and append it to `api_v1_router` below
3. Mount the route prefix in the domain's __init__.py
"""
from __future__ import annotations
from fastapi import APIRouter
# Aggregator list — populated as domains migrate.
# Each entry is an APIRouter from app/api/v1/<group>/<domain>.py.
api_v1_router: list[APIRouter] = []
# Aggregator router — single mount point for v1.
# When domains migrate, replace this with a real aggregator:
# from app.api.v1.public import router as public_router
# api_v1_router.append(public_router)
router = APIRouter(prefix="/api/v1", tags=["v1"])
# ── Migrated domains ───────────────────────────────────────────────────
# Each migrated domain is imported here. The router exposes endpoints
# at /api/v1/<domain>/* (path defined per-router).
#
# During strangelfig, the LEGACY /api/v1/<domain>/* endpoints remain
# mounted in main.py. The new v1 router is mounted at the same path
# (FastAPI handles prefix-based routing) — first match wins, so the
# legacy stays until we explicitly remove it.
from app.api.v1.auth.alerts import router as alerts_router # noqa: E402
api_v1_router.append(alerts_router)
from app.api.v1.public.wallet import router as wallet_router # noqa: E402
api_v1_router.append(wallet_router)
from app.api.v1.public.token import router as token_router # noqa: E402
api_v1_router.append(token_router)
from app.api.v1.public.scanner import router as scanner_router # noqa: E402
api_v1_router.append(scanner_router)
# x402 moved to app.domain.x402 (T34 v2)
# Old app/api/v1/x402/payments.py removed to avoid model conflicts
from app.api.v1.rag.search import router as rag_v2_router # noqa: E402
api_v1_router.append(rag_v2_router)
from app.api.v1.admin.alerts_webhook import router as admin_alerts_webhook_router # noqa: E402
api_v1_router.append(admin_alerts_webhook_router)
from app.api.v1.catalog import router as catalog_router # noqa: E402
api_v1_router.append(catalog_router)
def build_v1_router() -> APIRouter:
"""Construct the v1 aggregator with all migrated routes mounted."""
aggregated = APIRouter(prefix="/api/v1")
for r in api_v1_router:
aggregated.include_router(r)
return aggregated

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"""Admin routes — admin role required.
Target: user management, system config, ops, bulletin moderation.
"""

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"""Admin alerts webhook — /api/v1/admin/alerts/webhook.
Stub endpoint for receiving alert webhooks from external sources
(monitoring, observability platforms).
"""
from __future__ import annotations
from typing import Any
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
router = APIRouter(prefix="/alerts", tags=["admin"])
class AlertWebhookPayload(BaseModel):
"""Generic webhook payload from external alert sources."""
source: str # "prometheus" | "grafana" | "sentry" | "custom"
severity: str # "info" | "warning" | "critical"
title: str
description: str | None = None
labels: dict[str, str] = {}
@router.post("/webhook")
async def receive_alert_webhook(payload: AlertWebhookPayload) -> dict[str, Any]:
"""Receive an alert webhook from external monitoring."""
raise HTTPException(
status_code=501,
detail="Alert webhook ingestion not yet implemented — pending T08 GlitchTip wiring",
)

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"""T07 GlitchTip test endpoint.
POST /api/v1/_test/glitchtip
{"type": "error", "message": "test error"}
POST /api/v1/_test/exception
→ triggers a real exception, captured by GlitchTip
POST /api/v1/_test/message
→ captures an info-level message
Used for:
- Verifying the GlitchTip pipeline works
- Smoke testing after deploys
- Demonstrating the secret-scrubbing before_send hook
"""
from __future__ import annotations
import logging
from fastapi import APIRouter
from pydantic import BaseModel
log = logging.getLogger(__name__)
router = APIRouter(prefix="/api/v1/_test", tags=["test"])
class GlitchtipTestRequest(BaseModel):
type: str = "error" # error | exception | message
message: str = "test event from RMI"
secret: str | None = None # should be REDACTED in Sentry
@router.post("/glitchtip")
async def test_glitchtip(req: GlitchtipTestRequest) -> dict:
"""Trigger a test event. Tests the secret-scrubbing before_send hook."""
if req.type == "exception":
try:
raise ValueError(req.message)
except Exception as e:
try:
from app.core.observability import capture_exception
capture_exception(e, secret=req.secret, route="/api/v1/_test/glitchtip")
except ImportError:
log.exception("test_exception_no_sentry")
return {"captured": "exception", "message": req.message}
if req.type == "message":
try:
from app.core.observability import capture_message
capture_message(req.message, level="warning", secret=req.secret)
except ImportError:
log.warning(f"test_message_no_sentry: {req.message}")
return {"captured": "message", "message": req.message}
# default: error log + capture
log.error(f"test_error: {req.message} (secret={req.secret})")
try:
from app.core.observability import capture_message
capture_message(req.message, level="error", secret=req.secret)
except ImportError:
pass
return {"captured": "error", "message": req.message, "secret_redacted_in_sentry": True}

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"""Authenticated routes — JWT required.
Target: portfolio, alerts, intel feeds, profile, settings.
"""

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"""Auth alerts router — /api/v1/alerts/*.
Stub implementation for the alerts domain. Real implementations
will wire up to user-configured alert rules and notification channels
(email, Telegram, webhook). For now, returns 501 Not Implemented
for actual alert operations, with version metadata.
"""
from __future__ import annotations
from typing import Any
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
router = APIRouter(prefix="/alerts", tags=["alerts"])
class AlertRule(BaseModel):
"""Schema for an alert rule (creation/edit)."""
name: str
subject_type: str # "token" | "wallet" | "deployer"
subject_id: str
trigger: str # "risk_score_above" | "deployer_rug" | "news_mention"
threshold: float | None = None
channels: list[str] = [] # ["email", "telegram", "webhook"]
class AlertList(BaseModel):
"""Response for GET /api/v1/alerts."""
count: int
items: list[dict[str, Any]] = []
@router.get("", response_model=AlertList)
async def list_alerts() -> AlertList:
"""List all configured alert rules for the authenticated user.
TODO: wire up to Postgres once auth context is established.
Returns empty list as a stub so the factory can mount successfully.
"""
return AlertList(count=0, items=[])
@router.post("", status_code=501)
async def create_alert(rule: AlertRule) -> dict[str, str]:
"""Create a new alert rule.
Returns 501 until alert persistence is wired up. Stub so the
factory mounts this route without crashing.
"""
raise HTTPException(
status_code=501,
detail="Alert persistence not yet implemented — coming in v5.1",
)
@router.delete("/{rule_id}", status_code=501)
async def delete_alert(rule_id: str) -> dict[str, str]:
"""Delete an alert rule by ID."""
raise HTTPException(
status_code=501,
detail="Alert persistence not yet implemented — coming in v5.1",
)

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"""Catalog v1 routes — thin HTTP layer."""
from .router import router
__all__ = ["router"]

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"""T27B HTTP routes — CatalogService endpoints.
Per v4.0 §T27. The thin HTTP layer over app.catalog.service.CatalogService.
"""
from __future__ import annotations
from typing import Any
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, Field
from app.catalog.models import Chain
from app.catalog.service import get_catalog
router = APIRouter(prefix="/api/v1/catalog", tags=["catalog"])
# ── Request models ───────────────────────────────────────────────
class RagIngestRequest(BaseModel):
content: str = Field(..., min_length=1)
collection: str = "scam_intel"
doc_id: str | None = None
metadata: dict[str, Any] = Field(default_factory=dict)
class RagSearchRequest(BaseModel):
query: str = Field(..., min_length=1)
collection: str = "scam_intel"
top_k: int = Field(default=5, ge=1, le=50)
class ResolveEntityRequest(BaseModel):
wallet_id: str
max_chains: int = Field(default=5, ge=1, le=20)
class FindRiskyTokensRequest(BaseModel):
min_rug_count: int = Field(default=1, ge=1)
chain: str | None = None
limit: int = Field(default=50, ge=1, le=200)
class AttachRagRequest(BaseModel):
chain: str
address: str
qdrant_point_id: str
# ── Health / introspection ───────────────────────────────────────
@router.get("/stats")
async def stats() -> dict:
"""Catalog stats: which stores are reachable + entity counts."""
return await get_catalog().stats()
@router.get("/probe")
async def probe() -> dict:
"""Probe which stores are reachable from this container."""
return await get_catalog().probe_stores()
# ── Token endpoints ─────────────────────────────────────────────
@router.get("/tokens/{chain}/{address}")
async def get_token(chain: str, address: str) -> dict:
"""Get a token by chain+address. Returns full Token model + provenance."""
try:
c = Chain(chain)
except ValueError:
raise HTTPException(400, f"unknown chain: {chain}")
tok = await get_catalog().get_token(c, address)
if not tok:
raise HTTPException(404, "token not found")
return tok.model_dump(mode="json")
@router.get("/tokens/{chain}/{address}/risk")
async def get_token_risk(chain: str, address: str) -> dict:
"""Recipe 3 — Real-time risk score. Composes Redis + Postgres + Neo4j."""
try:
c = Chain(chain)
except ValueError:
raise HTTPException(400, f"unknown chain: {chain}")
return await get_catalog().get_token_risk(c, address)
@router.post("/tokens/risky-by-deployer")
async def risky_tokens(req: FindRiskyTokensRequest) -> dict:
"""Recipe 1 — Find tokens deployed by wallets with rug history."""
chain_enum = None
if req.chain:
try:
chain_enum = Chain(req.chain)
except ValueError:
raise HTTPException(400, f"unknown chain: {req.chain}")
tokens = await get_catalog().find_tokens_by_deployer_history(
min_rug_count=req.min_rug_count, chain=chain_enum, limit=req.limit
)
return {
"count": len(tokens),
"tokens": [t.model_dump(mode="json") for t in tokens],
}
# ── Wallet endpoints ─────────────────────────────────────────────
@router.get("/wallets/{chain}/{address}")
async def get_wallet(chain: str, address: str) -> dict:
try:
c = Chain(chain)
except ValueError:
raise HTTPException(400, f"unknown chain: {chain}")
w = await get_catalog().get_wallet(c, address)
if not w:
raise HTTPException(404, "wallet not found")
return w.model_dump(mode="json")
# ── Entity resolution (Recipe 5) ────────────────────────────────
@router.post("/entities/resolve")
async def resolve_entity(req: ResolveEntityRequest) -> dict:
"""Cross-chain entity resolution via Neo4j Cypher."""
return await get_catalog().resolve_entity(req.wallet_id, req.max_chains)
# ── RAG bridge endpoints ────────────────────────────────────────
@router.post("/rag/search")
async def rag_search(req: RagSearchRequest) -> dict:
"""Search the RAG system. Returns ranked hits with RRF scores."""
hits = await get_catalog().rag_search(
query=req.query, collection=req.collection, top_k=req.top_k
)
return {"count": len(hits), "hits": hits}
@router.post("/rag/ingest")
async def rag_ingest(req: RagIngestRequest) -> dict:
"""Ingest content into RAG. Returns qdrant_point_id for cross-store linking."""
return await get_catalog().rag_ingest(
content=req.content,
collection=req.collection,
doc_id=req.doc_id,
metadata=req.metadata,
)
@router.post("/tokens/{chain}/{address}/attach-rag")
async def attach_rag(chain: str, address: str, req: AttachRagRequest) -> dict:
"""Link an existing RAG embedding (Qdrant point) to a Token row."""
try:
c = Chain(chain)
except ValueError:
raise HTTPException(400, f"unknown chain: {chain}")
ok = await get_catalog().attach_rag_to_token(c, address, req.qdrant_point_id)
if not ok:
raise HTTPException(404, "token not found or update failed")
return {"ok": True, "chain": chain, "address": address, "rag_embedding_id": req.qdrant_point_id}

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"""MCP v1 routes."""
from .router import router
__all__ = ["router"]

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"""T33 MCP Server — HTTP wrapper for SSE transport.
Per v4.0 §T33. Endpoints:
POST /mcp JSON-RPC 2.0 endpoint
GET /mcp/tools Tool catalog
POST /mcp/call/{tool_id} Direct tool execution (no JSON-RPC)
The server speaks the Model Context Protocol natively. Claude Desktop
and Cursor connect via:
{"mcpServers": {"rugmunch": {"url": "https://mcp.rugmunch.io/mcp", "transport": "sse"}}}
"""
from __future__ import annotations
import json
import logging
from typing import Any
from fastapi import APIRouter, Request
from pydantic import BaseModel
from app.mcp.server import (
MCP_PROTOCOL_VERSION,
MCP_SERVER_VERSION,
TOOL_CATALOG,
TOOL_DEPRECATED,
TOOL_SUCCESSORS,
TOOL_VERSIONS,
call_tool,
)
router = APIRouter(prefix="/mcp", tags=["mcp"])
log = logging.getLogger(__name__)
class JsonRpcRequest(BaseModel):
jsonrpc: str = "2.0"
method: str
params: dict[str, Any] = {}
id: int | str | None = None
class JsonRpcResponse(BaseModel):
jsonrpc: str = "2.0"
result: Any | None = None
error: dict | None = None
id: int | str | None = None
@router.post("")
async def jsonrpc_handler(req: JsonRpcRequest) -> dict:
"""JSON-RPC 2.0 endpoint for MCP clients.
Methods:
- initialize → returns server info
- tools/list → returns tool catalog
- tools/call → dispatches to backend
- resources/list → empty
- prompts/list → empty
"""
if req.jsonrpc != "2.0":
return {"jsonrpc": "2.0", "error": {"code": -32600, "message": "invalid jsonrpc version"}, "id": req.id}
if req.method == "initialize":
return {
"jsonrpc": "2.0",
"result": {
"protocolVersion": "2024-11-05",
"serverInfo": {
"name": "rugmunch-intelligence",
"version": MCP_SERVER_VERSION,
"description": "Crypto intelligence platform — 13+ chains, 8 MCP tools, x402 paid tier",
},
"capabilities": {"tools": {}, "resources": {}, "prompts": {}},
},
"id": req.id,
}
if req.method == "tools/list":
return {
"jsonrpc": "2.0",
"result": {"tools": TOOL_CATALOG},
"id": req.id,
}
if req.method == "tools/call":
name = req.params.get("name", "")
arguments = req.params.get("arguments", {})
if not name:
return {"jsonrpc": "2.0", "error": {"code": -32602, "message": "tool name required"}, "id": req.id}
result = await call_tool(name, arguments)
return {
"jsonrpc": "2.0",
"result": {
"content": [{"type": "text", "text": json.dumps(result, default=str)[:50000]}],
"isError": "error" in result,
},
"id": req.id,
}
if req.method == "resources/list":
return {"jsonrpc": "2.0", "result": {"resources": []}, "id": req.id}
if req.method == "prompts/list":
return {"jsonrpc": "2.0", "result": {"prompts": []}, "id": req.id}
if req.method == "notifications/initialized":
return {"jsonrpc": "2.0", "result": {}, "id": req.id}
return {
"jsonrpc": "2.0",
"error": {"code": -32601, "message": f"method not found: {req.method}"},
"id": req.id,
}
@router.get("/tools")
async def list_tools() -> dict:
"""Plain JSON endpoint (for direct integration, no JSON-RPC)."""
return {"server": "rugmunch-intelligence", "version": MCP_SERVER_VERSION, "tools": TOOL_CATALOG}
@router.get("/info")
async def server_info() -> dict:
"""Server metadata: version, protocol, tool count, capabilities.
Use this to discover the MCP server's capabilities without listing all tools.
Equivalent to MCP initialize handshake.
"""
return {
"server": "rugmunch-intelligence",
"server_version": MCP_SERVER_VERSION,
"protocol_version": MCP_PROTOCOL_VERSION,
"tool_count": len(TOOL_CATALOG),
"tools_versioned": sum(1 for t in TOOL_CATALOG if t["name"] in TOOL_VERSIONS),
"tools_deprecated": list(TOOL_DEPRECATED),
"tools_with_successors": list(TOOL_SUCCESSORS.keys()),
"capabilities": ["tools", "resources", "prompts"],
"endpoints": {
"jsonrpc": "/mcp",
"tools_list": "/mcp/tools",
"server_info": "/mcp/info",
"direct_call": "/mcp/call/{tool_id}",
},
"auth": {
"free_tier_daily": 5,
"pro_tier": "x402 micropayment per call",
"x402_endpoint": "https://x402.rugmunch.io",
},
"links": {
"homepage": "https://rugmunch.io",
"mcp_endpoint": "https://mcp.rugmunch.io/mcp",
"status": "https://status.rugmunch.io",
"docs": "https://github.com/Rug-Munch-Media-LLC/rmi-docs",
},
}
@router.post("/call/{tool_id}")
async def direct_call(tool_id: str, request: Request) -> dict:
"""Direct tool execution (no JSON-RPC). For curl/scripts."""
body = await request.json() if request.headers.get("content-type", "").startswith("application/json") else {}
arguments = body.get("arguments", body) if isinstance(body, dict) else {}
result = await call_tool(tool_id, arguments)
return result

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"""Public routes — no authentication required.
Target: scanner, wallet lookup, token info, pricing, health.
"""

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"""Public scanner endpoints — /api/v1/scanner/*.
Stub for unauthenticated token scans. Real implementation will
trigger the scanner pipeline (honeypot detection, flash loan checks,
oracle manipulation analysis).
"""
from __future__ import annotations
from typing import Any
from fastapi import APIRouter, HTTPException, Query
from pydantic import BaseModel
router = APIRouter(prefix="/scanner", tags=["scanner"])
class ScanRequest(BaseModel):
"""Request to scan a token or wallet."""
chain: str = "ethereum"
address: str
depth: str = "standard" # "quick" | "standard" | "deep"
class ScanResult(BaseModel):
"""Result of a scan."""
scan_id: str
status: str # "queued" | "running" | "completed" | "failed"
risk_score: int | None = None
risk_tier: str | None = None
findings: list[str] = []
@router.post("/scan", response_model=ScanResult)
async def scan(req: ScanRequest) -> ScanResult:
"""Queue a token/wallet scan."""
raise HTTPException(
status_code=501,
detail="Scanner pipeline not yet wired — uses app.domain.scanner (T06+)",
)
@router.get("/result/{scan_id}", response_model=ScanResult)
async def get_scan_result(scan_id: str) -> ScanResult:
"""Get the result of a previously queued scan."""
raise HTTPException(
status_code=501,
detail="Scan result retrieval not yet implemented",
)
@router.get("/quick")
async def quick_scan(
chain: str = Query("ethereum"),
address: str = Query(...),
) -> dict[str, Any]:
"""Quick scan (free tier, no persistence)."""
raise HTTPException(
status_code=501,
detail="Quick scan uses cached shield — see caching_shield module",
)

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"""Public token endpoints — /api/v1/token/*.
Stub for unauthenticated token queries. Real implementation will
fetch token metadata, holders, liquidity, and risk score.
"""
from __future__ import annotations
from typing import Any
from fastapi import APIRouter, HTTPException, Query
from pydantic import BaseModel
router = APIRouter(prefix="/token", tags=["token"])
class TokenSummary(BaseModel):
"""Basic token metadata."""
chain: str
address: str
name: str | None = None
symbol: str | None = None
decimals: int | None = None
deployed_at: str | None = None
deployer: str | None = None
class TokenRisk(BaseModel):
"""Token risk assessment."""
address: str
chain: str
risk_score: int
risk_tier: str
factors: list[str] = []
@router.get("/{address}", response_model=TokenSummary)
async def get_token(
address: str,
chain: str = Query("ethereum"),
) -> TokenSummary:
"""Fetch basic token metadata."""
raise HTTPException(
status_code=501,
detail="Token lookup not yet implemented — coming in v5.1",
)
@router.get("/{address}/risk", response_model=TokenRisk)
async def get_token_risk(address: str, chain: str = "ethereum") -> TokenRisk:
"""Compute the risk score for a token (uses Bayesian reputation + on-chain checks)."""
raise HTTPException(
status_code=501,
detail="Token risk uses T01 Bayesian + T02 scanner pipeline — pending wiring",
)
@router.get("/{address}/holders")
async def get_token_holders(address: str, chain: str = "ethereum", top: int = 50) -> dict[str, Any]:
"""Return top holders distribution for a token."""
raise HTTPException(
status_code=501,
detail="Holder distribution not yet implemented — uses Postgres + Neo4j",
)

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"""Public wallet endpoints — /api/v1/wallet/*.
Stub for unauthenticated wallet queries. Real implementation will
resolve wallets, fetch labels, and return balance/history data.
"""
from __future__ import annotations
from typing import Any
from fastapi import APIRouter, HTTPException, Query
from pydantic import BaseModel
router = APIRouter(prefix="/wallet", tags=["wallet"])
class WalletResolveResponse(BaseModel):
"""Response for wallet resolution."""
chain: str
address: str
labels: list[str] = []
entity: str | None = None
balance_usd: float | None = None
tx_count: int | None = None
@router.get("/{address}", response_model=WalletResolveResponse)
async def resolve_wallet(
address: str,
chain: str = Query("ethereum", description="Blockchain (ethereum, solana, base, etc.)"),
) -> WalletResolveResponse:
"""Resolve a wallet address to its labels + summary.
Returns 501 stub until label resolution is wired up.
"""
raise HTTPException(
status_code=501,
detail="Wallet resolution not yet implemented — coming in v5.1",
)
@router.get("/{address}/labels", response_model=list[dict[str, Any]])
async def get_wallet_labels(address: str, chain: str = "ethereum") -> list[dict[str, Any]]:
"""Return labels for a wallet from all federated sources."""
raise HTTPException(
status_code=501,
detail="Federated labels API pending — see T11",
)
@router.get("/{address}/history")
async def get_wallet_history(address: str, chain: str = "ethereum") -> dict[str, Any]:
"""Return transaction history summary for a wallet."""
raise HTTPException(
status_code=501,
detail="Wallet history pending — uses Neo4j + Postgres in v5.1",
)

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"""V1 RAG route — thin HTTP layer over app.rag.
The RAG system is the most coupled module (14 legacy files). This
facade exposes the most-used operations: search, ingest, feedback.
"""
from __future__ import annotations
from typing import Annotated, Any
from fastapi import APIRouter, Depends, HTTPException
from pydantic import BaseModel, Field
from app.rag import (
FeedbackRecord,
IngestRequest,
IngestResult,
RAGService,
SearchRequest,
SearchResponse,
)
from app.rag.engine import bulk_ingest as engine_bulk_ingest
from app.rag.engine import get_stats as engine_get_stats
router = APIRouter(prefix="/api/v1/rag/v2", tags=["rag"])
def _service() -> RAGService:
return RAGService()
class BulkIngestRequest(BaseModel):
collection: str = "scam_intel"
items: list[dict[str, Any]] = Field(default_factory=list)
@router.post("/search", response_model=SearchResponse)
async def search(
req: SearchRequest,
svc: Annotated[RAGService, Depends(_service)],
) -> SearchResponse:
"""RAG search. Returns Pydantic response with hits + scores."""
return await svc.search(req)
@router.post("/ingest", response_model=IngestResult)
async def ingest(
req: IngestRequest,
svc: Annotated[RAGService, Depends(_service)],
) -> IngestResult:
"""Ingest a document into the RAG system."""
return await svc.ingest(req)
@router.post("/feedback", response_model=IngestResult)
async def feedback(
record: FeedbackRecord,
svc: Annotated[RAGService, Depends(_service)],
) -> IngestResult:
"""Record scanner → RAG feedback. Ingests known scam into known_scams collection."""
ok = await svc.record_feedback(record)
return IngestResult(
doc_id=record.token_address,
collection="known_scams",
status="ok" if ok else "failed",
)
@router.get("/stats")
async def stats() -> dict:
"""Per-collection vector counts + active embedder backend."""
return engine_get_stats()
@router.post("/bulk-ingest")
async def bulk(req: BulkIngestRequest) -> dict:
"""Ingest many items into a collection sequentially (max 500 per call)."""
if not req.items:
raise HTTPException(status_code=400, detail="items must be non-empty")
if len(req.items) > 500:
raise HTTPException(status_code=400, detail="bulk limit 500 per call")
return await engine_bulk_ingest(items=req.items, collection=req.collection)

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"""x402 paid routes — crypto micropayment gated.
Target: tools (split from legacy x402_tools.py), tokens, wallets, defi, security.
"""

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"""WebSocket endpoints.
Target: real-time alerts, scanner results, intel feeds.
"""

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"""
RMI Apify Integration -- Free/cheap external actors as MCP tools.
Actors: Arkham wallet intelligence, web scraping, Twitter data.
"""
import logging
import os
from typing import Any
logger = logging.getLogger("rmi_apify")
APIFY_TOKEN = os.getenv("APIFY_API_TOKEN", "")
APIFY_BASE = "https://api.apify.com/v2"
def _apify_call(actor_id: str, run_input: dict, timeout: int = 120) -> dict | None:
"""Run an Apify actor and return results."""
if not APIFY_TOKEN:
return {"error": "APIFY_API_TOKEN not configured"}
import httpx
try:
# Start the actor run
resp = httpx.post(
f"{APIFY_BASE}/acts/{actor_id}/runs?waitForFinish={timeout}",
headers={"Authorization": f"Bearer {APIFY_TOKEN}", "Content-Type": "application/json"},
json=run_input,
timeout=timeout + 30,
)
if resp.status_code != 200 and resp.status_code != 201:
return {"error": f"Actor start failed: HTTP {resp.status_code}"}
run_data = resp.json().get("data", {})
dataset_id = run_data.get("defaultDatasetId")
if not dataset_id:
return {"error": "No dataset ID returned"}
# Fetch results
items_resp = httpx.get(
f"{APIFY_BASE}/datasets/{dataset_id}/items",
headers={"Authorization": f"Bearer {APIFY_TOKEN}"},
timeout=30,
)
if items_resp.status_code != 200:
return {"error": f"Dataset fetch failed: HTTP {items_resp.status_code}"}
return {"data": items_resp.json(), "run_id": run_data.get("id")}
except Exception as e:
return {"error": str(e)[:200]}
def arkham_wallet_intel(address: str) -> dict[str, Any]:
"""Get Arkham Intelligence wallet data. Near-free via Apify (~$0.03/wallet)."""
return _apify_call(
"BFRkJAsA9XBVgzoce",
{
"walletAddresses": [address],
"dataType": "intelligence",
"proxyConfiguration": {"useApifyProxy": True},
},
)
def arkham_wallet_portfolio(address: str, date: str | None = None) -> dict[str, Any]:
"""Get Arkham wallet portfolio/holdings."""
return _apify_call(
"BFRkJAsA9XBVgzoce",
{
"walletAddresses": [address],
"dataType": "portfolio",
"portfolioDate": date,
"proxyConfiguration": {"useApifyProxy": True},
},
)

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"""
Arkham Intelligence API Connector
Entity labeling, wallet attribution, institutional tracking, sanctions screening.
Base URL: https://api.arkhamintelligence.com
Auth header: API-Key (from /root/.secrets/arkham_api_key or ARKHAM_API_KEY env var)
"""
import asyncio
import logging
import os
import time
from typing import Any
import httpx
logger = logging.getLogger(__name__)
# ── Auth ────────────────────────────────────────────────────────────────────
ARKHAM_API_KEY = os.getenv("ARKHAM_API_KEY", "").strip()
if not ARKHAM_API_KEY:
# Fallback to reading from secrets file
_secrets_paths = ["/root/.secrets/arkham_api_key"]
for _sp in _secrets_paths:
if os.path.exists(_sp):
with open(_sp) as _f:
ARKHAM_API_KEY = _f.read().strip()
break
BASE_URL = "https://api.arkhamintelligence.com"
# ── Simple TTL Cache ────────────────────────────────────────────────────────
class _TTLCache:
"""In-memory cache with per-key TTL for rate-limited API responses."""
def __init__(self, default_ttl: int = 120):
self._store: dict[str, tuple[Any, float]] = {}
self._ttl = default_ttl
def get(self, key: str) -> Any | None:
entry = self._store.get(key)
if entry is None:
return None
value, expires = entry
if time.monotonic() > expires:
del self._store[key]
return None
return value
def set(self, key: str, value: Any, ttl: int | None = None):
ttl = ttl if ttl is not None else self._ttl
self._store[key] = (value, time.monotonic() + ttl)
def clear(self):
self._store.clear()
# ── Client ───────────────────────────────────────────────────────────────────
class ArkhamClient:
"""Async client for Arkham Intelligence REST API.
Provides entity resolution, label lookup, portfolio history,
with rate limiting and in-memory caching."""
def __init__(self, cache_ttl: int = 120):
if not ARKHAM_API_KEY:
logger.warning("ARKHAM_API_KEY not set — ArkhamClient will return auth errors")
self.headers = {
"API-Key": ARKHAM_API_KEY,
"accept": "application/json",
}
self.client = httpx.AsyncClient(timeout=30.0)
self.last_call = 0.0
self._cache = _TTLCache(default_ttl=cache_ttl)
# ── Helpers ──────────────────────────────────────────────────────────
async def _call(
self,
endpoint: str,
params: dict | None = None,
*,
use_cache: bool = True,
cache_ttl: int | None = None,
) -> dict:
"""Core HTTP GET with rate limiting, caching, and error handling.
Args:
endpoint: Path appended to BASE_URL (include leading /).
params: Optional query parameters.
use_cache: Whether to check/store in the TTL cache.
cache_ttl: Override default TTL for this call.
Returns:
JSON response as dict, or {"error": ...} on failure.
"""
cache_key = f"{endpoint}:{params!s}" if use_cache else None
if cache_key:
cached = self._cache.get(cache_key)
if cached is not None:
return cached
# Rate limit: 0.6 s between calls
now = time.monotonic()
wait = 0.6 - (now - self.last_call)
if wait > 0:
await asyncio.sleep(wait)
self.last_call = time.monotonic()
url = f"{BASE_URL}{endpoint}"
try:
r = await self.client.get(
url,
headers=self.headers,
params=params or {},
)
if r.status_code == 200:
data = r.json()
if cache_key:
self._cache.set(cache_key, data, ttl=cache_ttl)
return data
elif r.status_code == 429:
logger.warning("Arkham rate limit hit (429)")
return {"error": "Rate limited by Arkham API", "status": 429}
elif r.status_code == 401:
return {"error": "Invalid or missing API key", "status": 401}
elif r.status_code == 404:
return {"error": "Resource not found", "status": 404}
else:
return {
"error": f"HTTP {r.status_code}",
"status": r.status_code,
"body": r.text[:500],
}
except httpx.TimeoutException:
return {"error": "Request timed out", "status": 504}
except Exception as e:
logger.exception("Arkham API call failed")
return {"error": str(e)}
# ── Public API Methods ───────────────────────────────────────────────
async def get_entity(self, address: str) -> dict:
"""Resolve a blockchain address to a known entity.
Returns entity name, category, and attribution metadata."""
return await self._call(
f"/entities/{address}",
cache_ttl=300, # entity resolution is fairly static
)
async def get_labels(self, page: int = 0, limit: int = 100) -> dict:
"""Fetch all known labels from Arkham's database.
Returns:
dict with 'labels' list and pagination metadata."""
return await self._call(
"/labels",
params={"page": page, "limit": limit},
cache_ttl=300,
)
async def get_portfolio(self, address: str) -> dict:
"""Get historical portfolio holdings for an entity/address.
Returns:
dict with token balances, historical snapshots, and P&L data."""
return await self._call(
f"/entities/{address}/portfolio",
cache_ttl=120, # portfolio data changes faster
)
async def close(self):
"""Clean up the underlying HTTP client."""
await self.client.aclose()

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"""
Wallet Authentication Helpers
=============================
Wallet signature verification and user creation.
"""
import logging
import os
from datetime import datetime
from typing import Any
logger = logging.getLogger(__name__)
def verify_wallet_signature(message: str, signature: str, address: str) -> bool:
"""
Verify a wallet signature.
NOTE: This is a validation stub. In production, use a proper signing library
(e.g., web3.py for Ethereum, @solana/web3.js for Solana) to verify signatures.
For now, returns True if all fields are non-empty (basic validation).
"""
if not message or not signature or not address:
return False
# Ensure address format looks valid (basic check)
if len(address) < 20:
return False
# Basic signature length check
if len(signature) < 60: # Typical sig is 65 hex chars for EVM
return False
return True
def decode_signature(signature: str) -> tuple:
"""
Decode a wallet signature into r, s, v components.
Returns (r_hex, s_hex, v_int) for signature verification.
"""
import binascii
sig_bytes = binascii.unhexlify(signature.replace("0x", ""))
r = sig_bytes[:32].hex()
s = sig_bytes[32:64].hex()
v = sig_bytes[64]
return r, s, v
async def get_or_create_wallet_user(address: str, chain: str = "base") -> dict[str, Any]:
"""
Get or create a user based on wallet address.
Returns dict with:
- access_token
- refresh_token
- id
- email
- display_name
- tier
- role
- created_at
"""
import hashlib
import json
# Derive user_id from wallet address
user_id = hashlib.sha256(address.lower().encode()).hexdigest()[:32]
# Try to load existing user
r = None
try:
import redis
r = redis.Redis(
host=os.getenv("REDIS_HOST", "localhost"),
port=int(os.getenv("REDIS_PORT", "6379")),
password=os.getenv("REDIS_PASSWORD", ""),
decode_responses=True,
)
except Exception as e:
logger.warning(f"Redis not available for wallet user lookup: {e}")
user = None
if r:
data = r.hget("rmi:wallet_users", address.lower())
if data:
user = json.loads(data)
# Create new user if doesn't exist
if not user:
# Generate fake email for wallet users (no email required for wallet auth)
email = f"{address.lower()}@wallet.rmi"
display_name = f"Wallet User {address[2:8].upper()}"
user = {
"id": user_id,
"address": address.lower(),
"email": email,
"display_name": display_name,
"chain": chain,
"tier": "FREE",
"role": "USER",
"created_at": datetime.utcnow().isoformat(),
"xp": 0,
"level": 1,
"badges": [],
"scans_remaining": 5,
"scans_used": 0,
}
if r:
r.hset("rmi:wallet_users", address.lower(), json.dumps(user))
# Also store in main users hash
r.hset("rmi:users", user_id, json.dumps(user))
# Generate JWT token
from app.auth import _create_jwt
# Use email if available, otherwise derive from address
email = user.get("email") or f"{address.lower()}@wallet.rmi"
token = _create_jwt(user_id, email, user.get("tier", "FREE"), user.get("role", "USER"), address)
return {
"access_token": token,
"refresh_token": token,
"id": user_id,
"email": email,
"display_name": user.get("display_name", display_name),
"tier": user.get("tier", "FREE"),
"role": user.get("role", "USER"),
"created_at": user.get("created_at"),
"address": address,
"chain": chain,
}
async def verify_auth_token(token: str) -> dict[str, Any] | None:
"""
Verify a JWT token and return user info.
Returns:
Dict with user info if valid, None if invalid/expired
"""
from app.auth import _verify_jwt
try:
user = _verify_jwt(token)
if user:
# Return user info in expected format
return {
"id": user.get("user_id"),
"email": user.get("email"),
"address": user.get("wallet_address"),
"tier": user.get("tier", "FREE"),
"role": user.get("role", "USER"),
}
except Exception as e:
logger.debug(f"Token verification failed: {e}")
return None

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"""
Auto-Labeling RAG System — Behavioral wallet labeling.
========================================================
Watches for on-chain patterns and automatically labels wallets over time.
Uses FAISS similarity search against known labeled wallets.
When a wallet matches known scam/deployer/actor patterns, it gets auto-labeled.
Label categories:
- repeat_deployer: Created 3+ tokens that rugged
- funding_funnel: Received funds from known scam wallets
- wash_trader: Circular transaction patterns
- sniper_bot: Consistent sub-block-10 entries
- sandwich_bot: MEV sandwich attack patterns
- dust_attacker: Dust-level transfers to many addresses
- honeypot_deployer: Deployed contracts with transfer restrictions
- drainer_wallet: Receives from known phishing victims
- cex_deposit_launderer: Moves through CEX to obfuscate
- mixer_user: Interacts with sanctioned mixers
- pig_butchering: Slow buildup then sudden drain pattern
"""
import logging
import os
import time
from collections import Counter, defaultdict
from datetime import datetime
logger = logging.getLogger(__name__)
# ── Label Definitions ─────────────────────────────────────────
AUTO_LABELS = {
"repeat_deployer_3": {
"name": "Serial Deployer (3+)",
"description": "Deployed 3+ tokens that later rugged or were abandoned",
"entity_type": "scam_deployer",
"risk_score": 85,
"confidence_threshold": 0.7,
"icon": "🏭",
},
"repeat_deployer_5": {
"name": "Rug Pull Factory (5+)",
"description": "Deployed 5+ rug pull tokens — professional scam operation",
"entity_type": "scam_operation",
"risk_score": 95,
"confidence_threshold": 0.8,
"icon": "☠️",
},
"funding_funnel": {
"name": "Funding Funnel",
"description": "Received funds from 3+ known scam wallets — likely launderer",
"entity_type": "money_launderer",
"risk_score": 80,
"confidence_threshold": 0.6,
"icon": "💰",
},
"sniper_bot": {
"name": "Sniper Bot",
"description": "Consistently buys tokens in first 10 blocks of launch",
"entity_type": "trading_bot",
"risk_score": 30,
"confidence_threshold": 0.8,
"icon": "🎯",
},
"sandwich_bot": {
"name": "Sandwich Bot",
"description": "Detected sandwich attack patterns — front-running trades",
"entity_type": "mev_bot",
"risk_score": 60,
"confidence_threshold": 0.7,
"icon": "🥪",
},
"mixer_user": {
"name": "Mixer User",
"description": "Interacts with Tornado Cash or other sanctioned mixers",
"entity_type": "mixer_user",
"risk_score": 75,
"confidence_threshold": 0.6,
"icon": "🌪️",
},
"drainer_wallet": {
"name": "Wallet Drainer",
"description": "Receives funds from known phishing/exploit victim wallets",
"entity_type": "drainer",
"risk_score": 90,
"confidence_threshold": 0.7,
"icon": "🪝",
},
"honeypot_deployer": {
"name": "Honeypot Deployer",
"description": "Deployed contracts with sell restrictions or transfer blocks",
"entity_type": "scam_deployer",
"risk_score": 88,
"confidence_threshold": 0.7,
"icon": "🍯",
},
"wash_trader": {
"name": "Wash Trader",
"description": "Circular transaction patterns — trading with self/controlled wallets",
"entity_type": "wash_trader",
"risk_score": 70,
"confidence_threshold": 0.65,
"icon": "🔄",
},
"dust_attacker": {
"name": "Dust Attacker",
"description": "Sends dust amounts to 100+ addresses — phishing or tracking attempt",
"entity_type": "dust_attacker",
"risk_score": 45,
"confidence_threshold": 0.8,
"icon": "💨",
},
"pig_butchering": {
"name": "Pig Butchering Operator",
"description": "Gradual fund accumulation then sudden drain to exchange — scam pattern",
"entity_type": "scam_operation",
"risk_score": 92,
"confidence_threshold": 0.7,
"icon": "🐷",
},
"cex_launderer": {
"name": "CEX Launderer",
"description": "Routes through multiple CEX deposit addresses to break trace",
"entity_type": "money_launderer",
"risk_score": 78,
"confidence_threshold": 0.65,
"icon": "🏦",
},
"sleeping_agent": {
"name": "Sleeping Agent",
"description": "Wallet dormant 90+ days then suddenly active — potential sleeper",
"entity_type": "suspicious",
"risk_score": 55,
"confidence_threshold": 0.6,
"icon": "😴",
},
"flash_loan_attacker": {
"name": "Flash Loan Attacker",
"description": "Used flash loans for rapid price manipulation or exploits",
"entity_type": "exploiter",
"risk_score": 85,
"confidence_threshold": 0.7,
"icon": "⚡",
},
}
class AutoLabeler:
"""Auto-labeling engine that watches wallets and assigns labels based on behavior."""
def __init__(self):
self.labels_applied = Counter()
self.pending_observations = defaultdict(list)
self.last_run = None
self._known_scam_wallets = set()
self._known_mixers = set()
self._known_exchanges = set()
self._initialize_known_sets()
def _initialize_known_sets(self):
"""Load known scam wallets and mixers from our label database."""
clean_dir = os.path.join(os.environ.get("RMI_DATA_DIR", "/app/data"), "wallet-labels-clean")
for chain in ["ethereum", "solana"]:
path = os.path.join(clean_dir, f"wallet_labels_{chain}.csv")
if not os.path.exists(path):
continue
import csv
with open(path) as f:
for row in csv.DictReader(f):
addr = row["address"].lower()
etype = row.get("entity_type", "")
if etype in (
"malicious",
"phishing_scam",
"scam",
"exploiter",
"drainer",
"nation_state_actor",
"scam_operation",
"scam_deployer",
"money_launderer",
):
self._known_scam_wallets.add(addr)
if etype == "mixer" or "tornado" in row.get("name", "").lower():
self._known_mixers.add(addr)
if etype == "exchange" or etype == "exchange_deposit":
self._known_exchanges.add(addr)
logger.info(
f"AutoLabeler initialized: {len(self._known_scam_wallets):,} known scams, "
f"{len(self._known_mixers):,} mixers, {len(self._known_exchanges):,} exchanges"
)
async def observe_wallet(self, address: str, chain: str, observations: dict) -> list[dict]:
"""Record observations about a wallet and check if any labels should be applied."""
key = f"{chain}:{address.lower()}"
self.pending_observations[key].append(
{
"timestamp": time.time(),
**observations,
}
)
# Check label rules — return only NEW labels not already applied
existing_label_keys = {line_list["label_key"] for line_list in self.pending_observations.get(f"_labels_{key}", [])}
new_labels = await self._check_labels(address, chain, self.pending_observations[key])
unique_new = [line_list for line_list in new_labels if line_list["label_key"] not in existing_label_keys]
# Track applied labels to prevent duplicates
if f"_labels_{key}" not in self.pending_observations:
self.pending_observations[f"_labels_{key}"] = []
self.pending_observations[f"_labels_{key}"].extend(unique_new)
for label in unique_new:
self.labels_applied[label["label_key"]] += 1
return unique_new
async def _check_labels(self, address: str, chain: str, history: list[dict]) -> list[dict]:
"""Check all label rules against wallet history."""
applied = []
deploy_count = sum(1 for o in history if o.get("event") == "token_deployed")
if deploy_count >= 5:
applied.append(self._create_label("repeat_deployer_5", address, chain, {"deployments": deploy_count}))
elif deploy_count >= 3:
applied.append(self._create_label("repeat_deployer_3", address, chain, {"deployments": deploy_count}))
# Check funding sources
funders = set()
for o in history:
if o.get("event") == "received_funds" and o.get("from_address"):
funders.add(o["from_address"].lower())
scam_funders = funders & self._known_scam_wallets
if len(scam_funders) >= 3:
applied.append(
self._create_label("funding_funnel", address, chain, {"scam_funders": list(scam_funders)[:5]})
)
# Check mixer interaction
mixer_interactions = sum(
1
for o in history
if o.get("counterparty", "").lower() in self._known_mixers or "tornado" in o.get("protocol", "").lower()
)
if mixer_interactions >= 1:
applied.append(self._create_label("mixer_user", address, chain, {"mixer_interactions": mixer_interactions}))
# Check CEX laundering pattern
cex_deposits = set()
for o in history:
if o.get("event") == "deposited_to_exchange" and o.get("exchange"):
cex_deposits.add(o["exchange"])
if len(cex_deposits) >= 3 and len(scam_funders) >= 1:
applied.append(self._create_label("cex_launderer", address, chain, {"exchanges_used": list(cex_deposits)}))
# Check draining pattern
victim_funds = sum(
1
for o in history
if o.get("counterparty", "").lower() in self._known_scam_wallets and o.get("event") == "received_funds"
)
if victim_funds >= 5:
applied.append(self._create_label("drainer_wallet", address, chain, {"victim_count": victim_funds}))
# Check sleeping agent
if len(history) >= 2:
timestamps = sorted(o.get("timestamp", 0) for o in history)
gap = timestamps[-1] - timestamps[0]
if gap > 90 * 86400: # 90+ days dormancy
applied.append(self._create_label("sleeping_agent", address, chain, {"dormant_days": int(gap / 86400)}))
# Track applied labels
for label in applied:
self.labels_applied[label["label_key"]] += 1
return applied
def _create_label(self, label_key: str, address: str, chain: str, evidence: dict) -> dict:
"""Create a label entry."""
config = AUTO_LABELS[label_key]
return {
"address": address,
"chain": chain,
"label_key": label_key,
"name": config["name"],
"description": config["description"],
"entity_type": config["entity_type"],
"risk_score": config["risk_score"],
"icon": config["icon"],
"source": "auto_labeler",
"applied_at": datetime.now().isoformat(),
"evidence": evidence,
}
async def batch_analyze(self, observations: list[dict]) -> dict:
"""Batch analyze multiple wallet observations and return all labels."""
results = {"labels_applied": [], "stats": {}}
for obs in observations:
addr = obs.get("address", "")
chain = obs.get("chain", "ethereum")
if addr:
labels = await self.observe_wallet(addr, chain, obs.get("events", []))
results["labels_applied"].extend(labels)
results["stats"] = {
"total_wallets_analyzed": len(observations),
"labels_applied": len(results["labels_applied"]),
"label_counts": dict(self.labels_applied.most_common()),
"known_scam_wallets": len(self._known_scam_wallets),
"known_mixers": len(self._known_mixers),
}
return results
def get_stats(self) -> dict:
"""Get auto-labeler statistics."""
return {
"labels_applied_total": sum(self.labels_applied.values()),
"label_counts": dict(self.labels_applied.most_common()),
"known_scam_wallets": len(self._known_scam_wallets),
"known_mixers": len(self._known_mixers),
"known_exchanges": len(self._known_exchanges),
"pending_observations": sum(len(v) for v in self.pending_observations.values()),
"last_run": self.last_run,
}
# Singleton
_auto_labeler: AutoLabeler | None = None
def get_auto_labeler() -> AutoLabeler:
global _auto_labeler
if _auto_labeler is None:
_auto_labeler = AutoLabeler()
return _auto_labeler

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"""
BigQuery Wallet Analytics Pipeline
====================================
Streams wallet labels, scan results, and embedding usage to BigQuery.
All usage counts against free tier (1TB queries/month — we'll use <1%).
"""
import logging
from datetime import UTC, datetime
from app.gcloud_manager import get_gcloud
logger = logging.getLogger("bigquery.pipeline")
async def stream_wallet_labels(labels: list) -> dict:
"""Stream wallet labels to BigQuery rmi_production.wallet_labels."""
if not labels:
return {"streamed": 0, "errors": 0}
gcloud = get_gcloud()
rows = []
for w in labels:
rows.append(
{
"address": w.get("address", ""),
"chain": w.get("chain", "ethereum"),
"label": w.get("label", ""),
"persona": w.get("persona", ""),
"risk_score": float(w.get("risk_score", 0)),
"tx_count": int(w.get("tx_count", 0)),
"volume_usd": float(w.get("volume_usd", 0)),
"last_updated": datetime.now(UTC).isoformat(),
}
)
ok = await gcloud.bigquery_insert("rmi_production", "wallet_labels", rows)
return {"streamed": len(rows) if ok else 0, "errors": 0 if ok else len(rows)}
async def stream_scan_result(result: dict) -> bool:
"""Stream a single scan result to BigQuery."""
gcloud = get_gcloud()
return await gcloud.bigquery_insert(
"rmi_production",
"scan_results",
[
{
"token_address": result.get("address", ""),
"chain": result.get("chain", "ethereum"),
"risk_level": result.get("risk_level", "unknown"),
"is_scam": result.get("is_scam", False),
"score": int(result.get("score", 0)),
"flags": result.get("flags", []),
"scan_timestamp": datetime.now(UTC).isoformat(),
}
],
)
async def stream_embedding_usage(provider: str, task: str, dims: int, count: int = 1):
"""Log embedding usage for analytics."""
gcloud = get_gcloud()
await gcloud.bigquery_insert(
"rmi_production",
"embedding_usage",
[
{
"provider": provider,
"task": task,
"dims": dims,
"call_count": count,
"timestamp": datetime.now(UTC).isoformat(),
}
],
)
async def top_scam_wallets(limit: int = 10) -> list:
"""Query BigQuery for top scam-flagged wallets."""
gcloud = get_gcloud()
return await gcloud.bigquery_query(f"""
SELECT address, chain, label, risk_score
FROM rmi_production.wallet_labels
WHERE risk_score > 0.7
ORDER BY risk_score DESC
LIMIT {limit}
""")
async def daily_scan_stats() -> list:
"""Get today's scan statistics from BigQuery."""
gcloud = get_gcloud()
return await gcloud.bigquery_query("""
SELECT risk_level, COUNT(*) as count
FROM rmi_production.scan_results
WHERE scan_timestamp >= TIMESTAMP_TRUNC(CURRENT_TIMESTAMP(), DAY)
GROUP BY risk_level
ORDER BY count DESC
""")

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import asyncio
import os
from datetime import datetime
import httpx
BIRDEYE_API_KEY = os.getenv("BIRDEYE_API_KEY", "")
BASE_URL = "https://public-api.birdeye.so"
HEADERS = {"X-API-KEY": BIRDEYE_API_KEY, "accept": "application/json"}
class BirdeyeClient:
def __init__(self):
self.headers = HEADERS
self.client = httpx.AsyncClient(timeout=30.0)
self.last_call = 0
async def _call(self, endpoint: str, params: dict | None = None) -> dict:
import time
now = time.time()
wait = 0.6 - (now - self.last_call)
if wait > 0:
await asyncio.sleep(wait)
self.last_call = time.time()
try:
r = await self.client.get(f"{BASE_URL}{endpoint}", headers=self.headers, params=params or {})
return r.json() if r.status_code == 200 else {"error": f"HTTP {r.status_code}"}
except Exception as e:
return {"error": str(e)}
async def get_price(self, address: str) -> dict:
return await self._call("/defi/price", {"address": address})
async def get_token_overview(self, address: str) -> dict:
return await self._call("/defi/token_overview", {"address": address})
async def get_new_listings(self, limit: int = 20) -> list:
r = await self._call("/defi/v2/tokens/new_listing", {"limit": limit, "offset": 0})
return r.get("data", {}).get("items", []) if isinstance(r, dict) else []
async def security_scan(self, address: str) -> dict:
"""Derived security analysis using ALL Birdeye market data"""
overview = await self.get_token_overview(address)
await asyncio.sleep(0.6)
d = overview.get("data", {}) if isinstance(overview, dict) else {}
if not d:
return {"address": address, "error": "No data", "risk_score": -1}
score = 0
flags = []
signals = []
# 1. LIQUIDITY HEALTH (0-25 pts)
mcap = d.get("marketCap", 0) or 0
liq = d.get("liquidity", 0) or 0
if mcap > 0 and liq > 0:
ratio = liq / mcap
if ratio < 0.05:
score += 25
flags.append("CRITICAL: Liquidity/MCap < 5% — easy manipulation")
elif ratio < 0.15:
score += 15
flags.append("WARNING: Low liquidity ratio")
elif ratio > 0.5:
signals.append("Strong liquidity backing")
else:
signals.append("Normal liquidity levels")
# 2. PRICE VOLATILITY (0-20 pts)
changes = [abs(d.get(f"priceChange{t}Percent") or 0) for t in ["1m", "5m", "30m"]]
avg_chg = sum(changes) / max(len(changes), 1)
if avg_chg > 20:
score += 20
flags.append("EXTREME volatility — pump/dump in progress")
elif avg_chg > 5:
score += 10
flags.append("High volatility — watch for manipulation")
elif avg_chg < 1:
signals.append("Stable price action")
# 3. HOLDER HEALTH (0-20 pts)
holders = d.get("holder", 0) or 0
if holders < 20:
score += 20
flags.append(f"Very few holders ({holders}) — high concentration")
elif holders < 100:
score += 10
flags.append(f"Low holder count ({holders})")
elif holders > 500:
signals.append(f"Healthy holder base ({holders:,}) wallets")
# Check wallet change for suspicious activity
uw_change = d.get("uniqueWallet30mChangePercent", 0) or 0
if uw_change > 50:
flags.append(f"Suspicious +{uw_change:.0f}% wallet growth in 30m — possible bots")
elif uw_change > 20:
flags.append(f"Rapid wallet growth +{uw_change:.0f}%")
# 4. TRADE ACTIVITY (0-15 pts)
last_trade = d.get("lastTradeUnixTime", 0) or 0
if last_trade > 0:
mins = (datetime.utcnow().timestamp() - last_trade) / 60
if mins > 60:
score += 15
flags.append(f"No trades for {int(mins)} min — possible dead token")
elif mins > 30:
score += 5
flags.append(f"Low activity — last trade {int(mins)} min ago")
else:
signals.append("Active trading")
# 5. METADATA QUALITY (0-10 pts)
ext = d.get("extensions", {})
has_web = bool(ext.get("website"))
has_social = bool(ext.get("twitter") or ext.get("discord"))
has_desc = bool(ext.get("description"))
if not has_web and not has_social:
score += 10
flags.append("No website or socials — anonymous project")
elif not has_web:
score += 5
flags.append("No website — transparency concern")
elif has_desc:
signals.append("Complete metadata — transparent project")
# 6. VOLUME/MARKET CAP RATIO (0-10 pts) — wash trading detection
v24h = d.get("v24hUSD", 0) or 0
if mcap > 0 and v24h > 0:
v_ratio = v24h / mcap
if v_ratio > 5:
score += 10
flags.append(f"Volume {v_ratio:.1f}x MarketCap — WASH TRADING likely")
elif v_ratio > 2:
score += 5
flags.append(f"Volume {v_ratio:.1f}x MarketCap — possible wash trading")
elif v_ratio > 0.1:
signals.append("Healthy volume/market cap ratio")
# 7. BUY/SELL RATIO ANALYSIS (bonus signal)
buy24h = d.get("buy24h", 0) or 0
sell24h = d.get("sell24h", 0) or 0
if buy24h > 0 and sell24h > 0:
if sell24h > buy24h * 2:
flags.append("Heavy sell pressure — 2x more sells than buys")
elif buy24h > sell24h * 1.5:
signals.append("Buy pressure dominant — bullish signal")
# VERDICT
if score >= 60:
verdict = "HIGH RISK"
elif score >= 35:
verdict = "MEDIUM RISK"
elif score >= 15:
verdict = "LOW-MEDIUM RISK"
else:
verdict = "LOW RISK"
return {
"address": address,
"token_name": d.get("name", "Unknown"),
"symbol": d.get("symbol", "???"),
"risk_score": min(score, 100),
"risk_level": verdict,
"price": d.get("price", 0),
"market_cap": mcap,
"liquidity": liq,
"fdv": d.get("fdv", 0),
"holders": holders,
"number_markets": d.get("numberMarkets", 0),
"volume_24h": v24h,
"buy_24h": buy24h,
"sell_24h": sell24h,
"price_change_24h": d.get("priceChange24hPercent", 0),
"wallet_growth_30m": uw_change,
"last_trade": d.get("lastTradeHumanTime", ""),
"flags": flags,
"positive_signals": signals,
"metadata": {"website": has_web, "socials": has_social, "description": has_desc},
"analyzed_at": datetime.utcnow().isoformat(),
"birdeye_powered": True,
}
async def new_token_radar(self, limit: int = 20, min_liquidity: float = 1000) -> dict:
tokens = await self.get_new_listings(limit)
scored = []
for t in tokens:
liq = t.get("liquidity", 0) or 0
if liq < min_liquidity:
continue
score = 0
reasons = []
if liq > 10000:
score += 25
reasons.append("Good liquidity")
uw = t.get("uniqueWallet30m", 0) or 0
if uw > 50:
score += min(uw * 0.2, 20)
reasons.append(f"{uw} recent wallets")
score += min(t.get("trade24h", 0) or 0 * 0.01, 10)
scored.append({**t, "opportunity_score": min(score, 50), "score_reasons": reasons})
return {
"tokens": sorted(scored, key=lambda x: x.get("opportunity_score", 0), reverse=True),
"count": len(scored),
}
# ── Wallet Intelligence ──────────────────────────────────────────────────
async def get_wallet_networth(self, wallet: str) -> dict:
"""Get wallet net worth in USD and token breakdown."""
return await self._call("/v1/wallet/networth", {"wallet": wallet})
async def get_wallet_pnl(self, wallet: str, timeframe: str = "7d") -> dict:
"""Get wallet profit/loss for a given timeframe."""
return await self._call("/v1/wallet/pnl", {"wallet": wallet, "time_frame": timeframe})
async def get_wallet_smart_money_status(self, wallet: str) -> dict:
"""Check if wallet is tagged as smart money."""
return await self._call("/v1/wallet/smart_money", {"wallet": wallet})
async def close(self):
await self.client.aclose()

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"""
Blockchair API Integration - Bitcoin/Litecoin/Ethereum/Solana Blockchain API
============================================================================
Access blockchain data for:
- Transaction lookups
- Address balance checks
- Block information
- Transaction status
"""
import logging
from typing import Any
import httpx
logger = logging.getLogger(__name__)
# ─── BLOCKCHAIR API ENDPOINTS ─────────────────────────────────────
BLOCKCHAIR_API = "https://api.blockchair.com"
ENDPOINTS = {
"bitcoin": "/bitcoin",
"ethereum": "/ethereum",
"solana": "/solana",
"litecoin": "/litecoin",
"bitcoin_cash": "/bitcoin-cash",
"bitcoin_sv": "/bitcoin-sv",
"search": "/v2/search",
"stats": "/v2/stats",
"block": "/v2/block/{chain}/{id}",
"address": "/v2/address/{chain}/{address}",
"transaction": "/v2/transaction/{chain}/{hash}",
"mempool": "/v2/mempool/{chain}",
}
# ─── BLOCKCHAIR CLIENT ────────────────────────────────────────────
class BlockchairClient:
"""Client for Blockchair API."""
def __init__(self, api_key: str | None = None, timeout: int = 30):
self.api_key = api_key or ""
self.timeout = timeout
self._available = self._check_availability()
def _check_availability(self) -> bool:
"""Check if Blockchair API is accessible."""
try:
# Public API - no key required for basic access
response = httpx.get(f"{BLOCKCHAIR_API}/bitcoin/stats", timeout=5)
return response.status_code == 200
except Exception:
return False
def get_chain_stats(self, chain: str = "bitcoin") -> dict[str, Any]:
"""
Get blockchain statistics.
Args:
chain: Chain name (bitcoin, ethereum, solana, etc.)
Returns:
Chain statistics
"""
endpoint = ENDPOINTS.get(chain, "/bitcoin")
try:
response = httpx.get(f"{BLOCKCHAIR_API}{endpoint}/stats", timeout=self.timeout)
response.raise_for_status()
return response.json()["data"]["stats"]
except Exception as e:
logger.error(f"Error fetching stats for {chain}: {e}")
return {}
def get_address_info(self, address: str, chain: str = "bitcoin") -> dict[str, Any] | None:
"""
Get address information.
Args:
address: Blockchain address
chain: Chain name
Returns:
Address data or None
"""
try:
response = httpx.get(
f"{BLOCKCHAIR_API}{ENDPOINTS['address'].format(chain=chain, address=address)}",
timeout=self.timeout,
)
response.raise_for_status()
return response.json()["data"][address]
except Exception as e:
logger.error(f"Error fetching address {address} info: {e}")
return None
def get_transaction(self, tx_hash: str, chain: str = "bitcoin") -> dict[str, Any] | None:
"""
Get transaction details.
Args:
tx_hash: Transaction hash
chain: Chain name
Returns:
Transaction data or None
"""
try:
response = httpx.get(
f"{BLOCKCHAIR_API}{ENDPOINTS['transaction'].format(chain=chain, hash=tx_hash)}",
timeout=self.timeout,
)
response.raise_for_status()
return response.json()["data"][tx_hash]
except Exception as e:
logger.error(f"Error fetching transaction {tx_hash}: {e}")
return None
def search(self, query: str) -> dict[str, Any]:
"""
Search for addresses, transactions, blocks.
Args:
query: Search query
Returns:
Search results
"""
try:
response = httpx.get(
f"{BLOCKCHAIR_API}{ENDPOINTS['search']}",
params={"query": query},
timeout=self.timeout,
)
response.raise_for_status()
return response.json()["data"]
except Exception as e:
logger.error(f"Error during search: {e}")
return {}
def get_blocks(self, chain: str = "bitcoin", limit: int = 10) -> list[dict[str, Any]]:
"""
Get recent blocks.
Args:
chain: Chain name
limit: Number of blocks
Returns:
List of block data
"""
try:
response = httpx.get(
f"{BLOCKCHAIR_API}{ENDPOINTS.get(chain, '/bitcoin')}/blocks",
params={"limit": limit},
timeout=self.timeout,
)
response.raise_for_status()
return response.json()["data"]
except Exception as e:
logger.error(f"Error fetching blocks for {chain}: {e}")
return []
def get_mempool(self, chain: str) -> dict[str, Any]:
"""
Get mempool status.
Args:
chain: Chain name
Returns:
Mempool data
"""
try:
response = httpx.get(f"{BLOCKCHAIR_API}{ENDPOINTS['mempool'].format(chain=chain)}", timeout=self.timeout)
response.raise_for_status()
return response.json()["data"]
except Exception as e:
logger.error(f"Error fetching mempool for {chain}: {e}")
return {}
def get_block(self, chain: str, block_id: int) -> dict[str, Any] | None:
"""
Get specific block data.
Args:
chain: Chain name
block_id: Block number or hash
Returns:
Block data or None
"""
try:
response = httpx.get(
f"{BLOCKCHAIR_API}{ENDPOINTS['block'].format(chain=chain, id=block_id)}",
timeout=self.timeout,
)
response.raise_for_status()
return response.json()["data"]
except Exception as e:
logger.error(f"Error fetching block {block_id} for {chain}: {e}")
return None
# ─── GLOBAL SINGLETON ─────────────────────────────────────────────
_client: BlockchairClient | None = None
def get_blockchair_client(chain: str = "bitcoin") -> BlockchairClient:
"""Get or create Blockchair client instance."""
global _client
if _client is None:
_client = BlockchairClient()
return _client
def get_address_balance(address: str, chain: str = "bitcoin") -> dict[str, Any] | None:
"""Get address balance from Blockchair."""
client = get_blockchair_client(chain)
return client.get_address_info(address)
def search_blockchain(query: str) -> dict[str, Any]:
"""Search Blockchair for blockchain data."""
client = get_blockchair_client()
return client.search(query)

259
app/brand_marketing_generator.py Normal file
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"""
RMI Marketing Content Generator - Creates all marketing graphics with EXACT brand compliance.
Uses detective character, purple/gold colors, circular frames.
NO DEVIATION from brand guidelines.
"""
import logging
import os
from datetime import UTC, datetime
from PIL import Image, ImageDraw, ImageFont
logger = logging.getLogger(__name__)
# ── Paths ────────────────────────────────────────────────────
CHARACTER_PATH = "/root/backend/assets/characters/detective-character.png"
LOGO_PATH = "/root/backend/assets/logos/rugmunch-logo.jpg"
OUTPUT_DIR = "/root/backend/assets/marketing_generated"
os.makedirs(OUTPUT_DIR, exist_ok=True)
# ── Brand Colors (EXACT - NO DEVIATION) ──────────────────────
BRAND = {
"purple": "#2D1B36",
"purple_light": "#3D2346",
"gold": "#D4AF37",
"gold_light": "#F1D475",
"gold_dark": "#AA8828",
"cyan": "#00FFFF",
"white": "#FFFFFF",
"green_alert": "#00FF88", # ONLY for rugpulls/scams
"red_danger": "#FF4444", # ONLY for losses
}
# ── Marketing Content Types ──────────────────────────────────
CONTENT_TYPES = {
"feature_showcase": {
"title": "Feature Showcase",
"size": (1200, 675),
"bg_color": BRAND["purple"],
"text_color": BRAND["gold"],
"include_character": True,
},
"stats_announcement": {
"title": "Stats/Metrics",
"size": (1200, 675),
"bg_color": BRAND["purple"],
"text_color": BRAND["gold"],
"include_character": False,
},
"launch_announcement": {
"title": "Launch Announcement",
"size": (1200, 675),
"bg_color": BRAND["purple"],
"text_color": BRAND["gold"],
"include_character": True,
},
"platform_overview": {
"title": "Platform Overview",
"size": (1920, 1080),
"bg_color": BRAND["purple"],
"text_color": BRAND["gold"],
"include_character": True,
},
"premium_promo": {
"title": "Premium Tier Promo",
"size": (1080, 1080),
"bg_color": BRAND["purple"],
"text_color": BRAND["gold"],
"include_character": True,
},
}
# ── Content Copy Templates ───────────────────────────────────
MARKETING_COPY = {
"smart_money": {
"headline": "SMART MONEY TRACKING",
"subhead": "Follow The Whales, Profit Like Them",
"body": "Track 1,000+ labeled whale wallets in real-time. See what VCs and funds buy BEFORE the pump.",
"stats": ["1,000+ Wallets", "Real-Time Alerts", "8 Chains"],
},
"rug_detection": {
"headline": "RUGPULL DETECTION",
"subhead": "2-Minute Alert Speed",
"body": "7-method detection engine catches rugs before you ape. 2,530+ scams tracked.",
"stats": ["7 Methods", "2-Min Alerts", "2,530+ Scams"],
},
"kol_scorecards": {
"headline": "KOL SCORECARDS",
"subhead": "No More Fake Gurus",
"body": "500+ influencers tracked. Verified on-chain performance. Win rate transparency.",
"stats": ["500+ KOLs", "On-Chain Verified", "Win Rates"],
},
"platform_launch": {
"headline": "RUG MUNCH INTELLIGENCE",
"subhead": "Track Smart Money. Avoid Rugs. Find Alpha.",
"body": "40+ features live. Real-time alerts. Professional crypto intelligence.",
"cta": "Join Free - rugmunch.io",
},
"premium_tier": {
"headline": "PREMIUM INTELLIGENCE",
"subhead": "For Serious Traders Only",
"body": "Smart money tracking. Insider alerts. Exchange flows. Cluster analysis.",
"price": "$29/mo",
"features": ["Real-Time Alerts", "Smart Money Tracking", "500+ KOLs", "API Access"],
},
}
# ── Graphics Generation Functions ────────────────────────────
def create_gradient_background(size, color1, color2):
"""Create purple gradient background."""
img = Image.new("RGB", size, color1)
draw = ImageDraw.Draw(img)
for y in range(size[1]):
alpha = y / size[1]
r = int(int(color1[1:3], 16) * (1 - alpha) + int(color2[1:3], 16) * alpha)
g = int(int(color1[3:5], 16) * (1 - alpha) + int(color2[3:5], 16) * alpha)
b = int(int(color1[5:7], 16) * (1 - alpha) + int(color2[5:7], 16) * alpha)
draw.line([(0, y), (size[0], y)], fill=(r, g, b))
return img
def add_circular_frame(img, color=BRAND["gold"], width=5):
"""Add gold circular frame."""
draw = ImageDraw.Draw(img)
margin = 20
draw.ellipse([margin, margin, img.size[0] - margin, img.size[1] - margin], outline=color, width=width)
return img
def add_text_centered(img, text, position, font_size, color, font_path=None):
"""Add centered text."""
draw = ImageDraw.Draw(img)
try:
font = ImageFont.truetype(font_path or "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", font_size)
except Exception:
font = ImageFont.load_default()
bbox = draw.textbbox((0, 0), text, font=font)
text_width = bbox[2] - bbox[0]
x = position[0] - text_width // 2
draw.text((x, position[1]), text, fill=color, font=font)
return img
def generate_feature_graphic(feature_key: str) -> dict:
"""Generate feature showcase graphic."""
config = CONTENT_TYPES["feature_showcase"]
copy = MARKETING_COPY.get(feature_key)
if not copy:
return {"error": f"Unknown feature: {feature_key}"}
# Create background
img = create_gradient_background(config["size"], BRAND["purple"], BRAND["purple_light"])
# Add circular frame
img = add_circular_frame(img, BRAND["gold"], width=5)
ImageDraw.Draw(img)
# Add headline
add_text_centered(img, copy["headline"], (config["size"][0] // 2, 150), 72, BRAND["gold"])
# Add subhead
add_text_centered(img, copy["subhead"], (config["size"][0] // 2, 250), 48, BRAND["white"])
# Add body
add_text_centered(img, copy["body"], (config["size"][0] // 2, 350), 36, BRAND["white"])
# Add stats
y = 450
for stat in copy.get("stats", []):
add_text_centered(img, f"● {stat}", (config["size"][0] // 2, y), 32, BRAND["cyan"])
y += 50
# Add watermark
add_text_centered(img, "@cryptorugmunch", (config["size"][0] // 2, config["size"][1] - 80), 28, BRAND["gold"])
# Save
filename = f"feature_{feature_key}_{datetime.now(UTC).strftime('%Y%m%d_%H%M%S')}.png"
output_path = os.path.join(OUTPUT_DIR, filename)
img.save(output_path, "PNG")
return {
"status": "success",
"feature": feature_key,
"image_path": output_path,
"filename": filename,
}
def generate_launch_graphic() -> dict:
"""Generate platform launch announcement."""
config = CONTENT_TYPES["launch_announcement"]
copy = MARKETING_COPY["platform_launch"]
# Create background
img = create_gradient_background(config["size"], BRAND["purple"], BRAND["purple_light"])
img = add_circular_frame(img, BRAND["gold"], width=5)
# Add text
add_text_centered(img, copy["headline"], (config["size"][0] // 2, 200), 80, BRAND["gold"])
add_text_centered(img, copy["subhead"], (config["size"][0] // 2, 320), 48, BRAND["white"])
add_text_centered(img, copy["body"], (config["size"][0] // 2, 420), 36, BRAND["white"])
add_text_centered(img, copy.get("cta", ""), (config["size"][0] // 2, 550), 42, BRAND["cyan"])
add_text_centered(img, "@cryptorugmunch", (config["size"][0] // 2, config["size"][1] - 80), 28, BRAND["gold"])
# Save
filename = f"launch_{datetime.now(UTC).strftime('%Y%m%d_%H%M%S')}.png"
output_path = os.path.join(OUTPUT_DIR, filename)
img.save(output_path, "PNG")
return {
"status": "success",
"type": "launch",
"image_path": output_path,
"filename": filename,
}
def generate_all_marketing_graphics() -> list[dict]:
"""Generate all marketing graphics."""
results = []
# Feature graphics
for feature in ["smart_money", "rug_detection", "kol_scorecards"]:
result = generate_feature_graphic(feature)
results.append(result)
# Launch graphic
result = generate_launch_graphic()
results.append(result)
return results
if __name__ == "__main__":
print("Generating marketing graphics with EXACT brand compliance...")
results = generate_all_marketing_graphics()
print(f"\n✅ Generated {len(results)} graphics:")
for r in results:
if r.get("status") == "success":
print(f" ✅ {r.get('type') or r.get('feature')}: {r.get('filename')}")
else:
print(f" ❌ {r.get('error')}")
print(f"\n📁 All graphics saved to: {OUTPUT_DIR}")

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