Rug Munch Intelligence

The scanner we needed when $CRM v1 launched. Built from that experience. Open source. Free tier for everyone.

Rug Munch Intelligence (RMI) is a suite of products that work together to protect retail crypto investors: a real-time token risk scanner with a live Telegram bot, on-chain visualization tools, a crypto-native AI agent that learns from every scam, and an open payment protocol for autonomous AI-to-API transactions. Every piece is open-source. Every piece is self-hostable. Free daily scans for the community. Premium for power users. Enterprise for teams.

The Crypto AI Agent​

RMI ships with a specialized crypto AI agent powered by Retrieval-Augmented Generation (RAG v2) that continuously trains on new scam patterns, honeypot techniques, and rug pull forensics. It gets smarter with every token scanned.

What makes it different from general-purpose AI:

The RAG Pipeline:

• Ingests scam reports from 8+ sources (Scam Sniffer, Honeypot.is, GoPlus, OpenSanctions, CryptoScamDB, ChainAbuse, internal investigations)
• Vectorizes and indexes into Qdrant for semantic search
• Connects to Neo4j wallet relationship graph (169K+ labeled nodes)
• Routes queries to the cheapest available LLM provider (7+ providers, auto-fallback)
• Returns answers with source citations — every claim is traceable

Try it at launch: @rugmunchbot on Telegram. Use /ask <question> or /ragscan <address>.

How It Works​

When you submit a token address, the scanner fires four live HTTP calls in parallel:

The scanner auto-detects chain type (EVM 0x... vs Solana base58) and applies the appropriate checks. Results return in under 3 seconds.

Risk Scoring​

Each threat adds points to a cumulative score, capped at 100:

The safety score is 100 - risk_score. Scores are additive, transparent, and every point is traceable to a specific check.

Wallet Forensics​

The wallet scanner traces funding sources, estimates PnL, identifies connected wallets, and flags known scam addresses using RMI's federated labeling system — 39 million labels from 6+ sources queried in parallel with graceful degradation.

@rugmunchbot — Telegram Bot​

Status: Deploying with RMI platform. The bot is online. Commands activate when the backend ships.

Bot capabilities:

• Inline scanning — paste a contract address anywhere in Telegram, get an instant scan result
• Tiered subscriptions: Free (limited scans/day), Premium (from $29.99/mo)
• Telegram Stars payments for upgrades
• Referral program with anti-fraud detection
• Spam protection with auto-blacklist and scam address checking
• Moderation suite for community groups

RugMaps — On-Chain Visualization​

Status: In development. Shipping with the RMI platform.

RugMaps visualizes the on-chain relationships that determine whether a token is safe.

What It Will Do​

Wallet Clustering. Identify coordinated wallet groups that buy tokens together at launch. The scanner traces funding sources to find wallets controlled by the same entity — even when they use fresh addresses.

Fund Flow Tracing. Map exactly where liquidity came from and where it goes. See every hop from CEX withdrawal to deployer wallet to liquidity pool to exit. The full path, visualized.

Deployer History. For any wallet address, see every token it has ever deployed, every contract it has interacted with, and whether those tokens survived or rugged. A reputation graph built from on-chain evidence.

Holder Distribution. Interactive treemap of token holders. Instantly see if the top 10 wallets hold 90% of supply. Click any wallet to trace its history.

Label Propagation. RMI maintains 39 million address labels across Ethereum, BSC, Arbitrum, Polygon, and Solana. Known exchange wallets, known scam addresses, MEV bots, bridge contracts, DeFi protocols — all labeled. Labels propagate through Neo4j graph relationships to surface hidden connections.

RugCharts — Market Analytics​

Status: In development. Shipping with the RMI platform.

RugCharts provides the market context the scanner needs to assess risk — and the charts the community needs to make informed decisions.

What It Will Do​

Token Analytics. Price, volume, liquidity, holder count, and transaction history for any token. Time-series data with customizable date ranges.

Trending Discovery. What's moving right now? Sort by volume, price change, new holders, or social engagement. The scanner uses this data to prioritize which tokens to flag.

Cross-Chain View. Ethereum, BSC, Arbitrum, Polygon, Solana — all in one dashboard. Compare activity across chains. Spot capital rotation in real time.

News & Sentiment Overlay. Market data overlaid with news mentions, social volume, and sentiment scoring from RMI's intelligence feed. See not just what's moving, but why.

API Access. All chart data available through the same REST API. Build your own dashboards. Integrate into your own tools. MIT licensed.

x402 MCP — AI Agent Payments​

Status: In development. Shipping with the RMI platform.

x402 is an open payment protocol that lets AI agents pay for API access using on-chain settlement. RMI implements x402 as its native monetization layer — every API call, every premium scan, every intelligence report flows through x402.

What It Does​

HTTP 402 Payment Required. When an AI agent (or human) hits a paid RMI endpoint, they receive an HTTP 402 response with a payment invoice. The agent pays on-chain, presents the transaction hash, and the gateway verifies and serves the response. All in one request cycle.

MCP Server Integration. RMI exposes its entire scanner, labeler, and intelligence feed as MCP tools. AI agents (Claude, Hermes, Cursor, opencode) can call rmi_scan_token, rmi_label_address, rmi_trending_tokens, rmi_news_feed and more. Paid tools automatically trigger x402 invoices when the free tier is exhausted.

Supported Chains for Settlement. USDC on Solana, Base, Arbitrum, and Ethereum. Settlement finality in under 2 seconds on Solana, under 15 seconds on EVM L2s.

Free Tier. Every API key gets 100 free scans per day. Sufficient for individual use. Power users and AI agents upgrade through x402 credits.

Transparency. All payments are on-chain. All invoices are signed. Anyone can audit the payment flow. No trust required.

MCP Tools Available:

• rmi_scan_token — Full risk scan with all checks
• rmi_wallet_scan — Forensic wallet analysis
• rmi_label_address — Address label lookup (39M+ labels)
• rmi_trending_tokens — Hot tokens across all tracked chains
• rmi_token_price — Live price from DexScreener
• rmi_news_feed — Aggregated crypto news
• rmi_due_diligence — Comprehensive DD report
• rmi_compare_tokens — Side-by-side comparison
• rmi_ask_rag — AI-powered scam intelligence query

Architecture​

All four products share the same backend. One codebase. One API. One deployment.

Data Sources (10+ external providers, queried in parallel):

• DexScreener — market data, pairs, social links
• GoPlus Security — contract safety, taxes, honeypot detection
• Honeypot.is — secondary honeypot verification
• Helius — Solana transaction parsing, bundle detection
• Etherscan/Blockscout — contract verification, source code
• CoinGecko — historical prices, market caps
• Jupiter — Solana liquidity and quote simulation
• Birdeye — Solana token metadata
• 6 label sources — 39M address labels across 5 chains

Database Layer:

• Postgres — primary storage, user accounts, scan history
• Redis — cache layer, rate limiting, Celery queue
• Neo4j — wallet relationship graph (169K+ labeled nodes)
• ClickHouse — analytics, 39M address labels, time-series data
• DuckDB — embedded Parquet querying for label CSVs
• Qdrant — vector embeddings for RAG-powered scam intelligence

AI Integration:

• RAG v2 — semantic search over scam reports, token analyses, and on-chain evidence
• AI Copilot — natural language queries with source citations
• MCP server — exposes scanner tools to external AI agents
• LLM provider routing — auto-selects cheapest available AI provider

The Investigation​

When RMI ships, it includes the full investigation into $CRM v1. Every wallet. Every transaction. The on-chain evidence that proves exactly how the manipulation occurred — delivered by the same scanner that would have caught it in real time.

The proof of the tool is the investigation. The investigation proves the claim.

For Developers​

git clone https://git.rugmunch.io/RugMunchMedia/rmi-backend.git
cd rmi-backend
docker compose up -d
# POST /scanner/scan with a token address
# Full API docs at /docs when the platform launches

The scanner API is fully open. Self-host it. Audit it. Build on it. All 290+ Python files, all 4 database backends, all 10+ external provider integrations — documented, containerized, and licensed MIT.

Quick Links​