crewai-mcp-crypto-intel-agents GitHub Details, Stars and Alternatives | OpenRepoFinder
vinkius-labs / repository
crewai-mcp-crypto-intel-agents
AI agent crew that generates institutional-grade crypto market intelligence reports with real-time data from CoinGecko, Binance, SEC EDGAR, FRED and more using CrewAI + 15 MCP servers + Google Gemini.
A transparent discovery signal based on current public GitHub metadata.
Recent activity35% weight
72
Community adoption25% weight
5
Maintenance state20% weight
100
License clarity10% weight
100
Project information10% weight
100
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
README preview
CrewAI MCP Crypto Intel Agents
A multi-agent system that generates institutional-grade cryptocurrency market intelligence reports with real-time price data, on-chain analytics, macroeconomic context, and social sentiment — powered by CrewAI, Google Gemini, and 15 production MCP servers from the Vinkius AI Gateway.
Why Crypto Intelligence Needs Real-Time Data Sources
The cryptocurrency market operates 24/7 across thousands of trading pairs, dozens of blockchains, and millions of social media conversations. Making informed decisions requires synthesizing data from fundamentally different domains — exchange prices, blockchain network metrics, Federal Reserve monetary policy, SEC filings, and Crypto Twitter sentiment — often within a window of hours.
Most crypto analysis available online falls into two categories: surface-level price commentary that adds nothing, or paywalled institutional research that costs $50,000+ per year. There is almost nothing in between — no publicly available analysis that combines verified exchange data with on-chain metrics, macro correlations, and quantified social sentiment in a single coherent report.
This project fills that gap. Four specialized AI agents collaborate to produce an institutional-quality intelligence report for any cryptocurrency, using real-time data from 15 MCP servers. The agents do not speculate or fabricate — they query live market data, blockchain explorers, economic databases, and social platforms, then synthesize their findings into a scenario-based analysis with proper risk disclaimers.
How It Works
The system operates as a sequential pipeline of four agents, each connected to curated MCP servers hosted on the Vinkius AI Gateway.
Phase 1 — Market Data Collection
The first agent builds a quantitative market profile. It connects to five MCP servers:
CoinGecko for comprehensive market data, historical prices, and global metrics
CoinMarketCap for rankings, market pairs, and exchange volume distribution
Binance for real-time order book data, recent trades, and ticker information
Alpha Vantage for technical indicators, forex rates, and equity correlations
ALGORITHMICALLY RELATED
Similar Open-Source Projects
Selected from shared topics, language and repository description—not editorial ratings.
An AI-powered travel planning application that uses Gemini LLM and CrewAI to automate trip planning with multiple specialized AI agents. This project demonstrates agentic AI collaboration to search flights, find hotels, and generate personalized travel itineraries.
Twelve Data for time series analysis and cross-asset technical comparisons
The output includes current prices cross-referenced across exchanges, RSI, MACD, moving averages, support/resistance levels, and volatility metrics — all with source attribution.
Phase 2 — On-Chain and Macroeconomic Analysis
The second agent connects crypto to the real world. It uses four MCP servers:
Blockchain.com for on-chain metrics — active addresses, transaction counts, hash rate, whale movements, exchange flows
SEC EDGAR for regulatory filings — ETF applications, enforcement actions, insider trades at crypto companies
FRED (Federal Reserve Economic Data) for macro indicators — Fed funds rate, CPI, DXY, yield curves
CoinMarketCal for the crypto event calendar — token unlocks, protocol upgrades, halvings, conference dates
This phase answers the question institutional investors always ask: "What is the macro context, and is the regulatory environment getting better or worse?"
Phase 3 — Sentiment Intelligence
The third agent mines social media and news. It connects to four MCP servers:
X/Twitter for Crypto Twitter mentions, volume trends, and key influencer positions
NewsAPI for crypto news headlines from the past 7 days, categorized by sentiment
Exa AI for semantic search across Reddit, Telegram summaries, and crypto forums
CoinDesk for the Bitcoin Price Index and market reference data
The output quantifies sentiment: "Social sentiment shifted from 65% bullish to 48% over the past 72 hours, driven primarily by regulatory uncertainty in 3 jurisdictions."
Phase 4 — Intelligence Report
The fourth agent compiles everything into a structured report. It uses two MCP servers for SEO optimization:
SEMrush for crypto keyword research and content gap analysis
SerpAPI for current SERP landscape and competitor report analysis
The final report includes scenario analysis (bull/base/bear cases with probability weightings), ranked risk factors, key levels to watch, and an upcoming catalyst calendar — all framed with appropriate disclaimers, never as predictions.
Why MCP Servers Are Essential for Financial Intelligence
Building a crypto intelligence system without MCP would require custom integrations with 15 different APIs — each with authentication, rate limiting, response parsing, and maintenance. The CoinGecko API alone has had 4 breaking changes in the past year. Maintaining all of these simultaneously is a full-time engineering job.
The Vinkius AI Gateway provides over 2,600 production-ready MCP servers that handle all of this complexity. Each server is an authenticated SSE endpoint:
https://edge.vinkius.com/<your_token>/mcp
This project uses 15 of those 2,600+ servers. The financial data coverage on the Vinkius AI Gateway extends far beyond what this project uses:
Bull / Base / Bear cases with probability weightings
Risk Factors
Top 5 risks ranked by probability and impact
Key Levels to Watch
Support, resistance, and trigger points
Upcoming Catalysts
Events calendar with potential impact ratings
Disclaimer
Standard financial research disclaimer
Technical Details
Framework:CrewAI with Flows and @CrewBase decorators
LLM: Google Gemini 2.0 Flash (free tier, ~15 RPM)
State Management: Pydantic models for type-safe data flow between agents
MCP Integration: Native CrewAI mcps= field with SSE transport to Vinkius AI Gateway
CLI: Typer with Rich console output
Rate Limiting:max_rpm=10 per agent to stay within Gemini free tier limits
Disclaimer
This tool generates research reports for informational purposes only. It does not constitute financial advice, investment recommendations, or trading signals. Always conduct your own research and consult with qualified financial advisors before making investment decisions. Cryptocurrency investments carry significant risk, including the potential loss of all invested capital.
FAQ
What is MCP?
The Model Context Protocol is an open standard for connecting AI systems to external tools and data sources. See modelcontextprotocol.io.
6 AI agents collaborate to build complete web applications in minutes. CrewAI-powered software development team that generates FastAPI backends, responsive frontends, tests, and deployment configs - demonstrating 95% automation with human oversight for the final 5%.
This project demonstrates how to build multi-agent AI systems using CrewAI and Python with a local Mistral-7B Instruct (GGUF) model. It includes agent collaboration for tasks like competitor analysis and content strategy generation, running fully offline using llama-cpp-python.