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tanmingtao1994-gif / fincrew

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A self-evolving multi-agent financial assistant powered by OpenClaw

18
5
100% credibility
Found Apr 02, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

FinCrew is a collaborative AI agent system for investment research, decision-making, and trade review that improves through persistent memory of experiences and user preferences.

How It Works

1
🔍 Discover FinCrew

You hear about FinCrew, a helpful team of AI advisors that analyze markets and guide your investments like a personal finance crew.

2
📥 Set up your team

Download and prepare your five specialized advisors for gathering info, spotting trends, checking charts, reviewing trades, and making decisions.

3
💭 Share your style

Tell the lead advisor your risk comfort, favorite stock types, and rules like position sizes so they remember your personal investing approach forever.

4
📝 Pick favorites

Choose stocks to track closely and trusted experts whose opinions to follow for better insights.

5
🗣️ Ask for advice

Chat with your lead advisor about a stock like 'Is NVDA a good buy?' and watch the team collaborate on analysis.

6
📊 Get smart reports

Receive clear breakdowns of news, trends, charts, risks, and buy/hold/sell calls tailored just for you.

🚀 Grow wiser together

Review past trades to learn lessons, and see your crew get smarter over time with every insight saved.

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Star Growth

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AI-Generated Review

What is fincrew?

Fincrew is a self-evolving multi-agent financial assistant built in TypeScript on OpenClaw, where five specialized agents collaborate on investment analysis, trading signals, and portfolio reviews. Users chat with the central Financial Manager for daily briefs, buy/hold/sell calls, or trade post-mortems, while it pulls stock data, news, KOL opinions from Twitter/Weibo/YouTube, and options chains via CLI tools. The standout is its memory loop that persists lessons from trades, books, and discussions, making decisions smarter over time with your custom philosophy baked in.

Why is it gaining traction?

This awesome self-evolving agent on GitHub stands out with its closed-loop learning—agents reference long-term memory in every analysis, unlike static bots that forget past mistakes. Developers dig the benchmark-ready eval suite (68% pass on 44 scenarios) and visualization UI for testing multi-agent flows, plus standalone tools for data collection without full deployment. It's a dynamically self-evolving multi-agent architecture that feels alive, evolving from real interactions.

Who should use this?

Retail traders needing quick market scans on NVDA or TSLA with KOL sentiment and technicals. Quantitative hobbyists customizing agents via config files for watchlists and risk rules. Devs prototyping self-evolving multi-agent collaboration for financial apps, leveraging its OpenClaw integration and eval framework.

Verdict

Try it if you're into self-evolving AI agents on GitHub—solid docs, deploy scripts, and evals make it dev-friendly despite 18 stars and 1.0% credibility score signaling early maturity. Production-ready for personal use, but monitor stability as it grows.

(198 words)

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