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    TauricResearch

    TauricResearch/TradingAgents

    TradingAgents: Multi-Agents LLM Financial Trading Framework

    ai-agents
    llm
    agent
    finance
    multiagent
    trading
    Python
    Apache-2.0
    120.8K stars
    23.1K forks
    899 GitHub watchers
    Updated Nov 6, 2026
    View on GitHub

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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    5 releases in the last 90 days, the latest 9 days ago, with 23 contributors on GitHub.

    Peaked at #1 in the weekly ranking the week of Apr 26, its best in the past year.

    Use Cases & Benefits

    • Implements a multi-agent framework using LLMs to simulate real-world financial trading with specialized analyst, researcher, trader, and risk management agents.
    • Enables collaborative, role-based market analysis and decision-making that integrates fundamental, sentiment, news, and technical data for robust trading strategies.
    • Use for developing AI-driven trading simulations that mimic complex firm-level decision processes with modular, customizable agent roles.
    • Use for researching and experimenting with multi-agent debates and consensus-building to improve trading strategy evaluation and risk assessment.
    • Use for building extensible financial AI systems that leverage diverse data sources and LLMs to automate portfolio management and trade execution.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    Apache 2.0

    Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.

    Community files

    • README: present

    Watch-outs

    No watch-outs from GStars' checks.

    General guidance from repository metadata, not legal advice. How we score

    From the README

    🎉 TradingAgents officially released! We have received numerous inquiries about the work, and we would like to express our thanks for the enthusiasm in our community.

    So we decided to fully open-source the framework. Looking forward to building impactful projects with you!

    TradingAgents is a multi-agent trading framework that mirrors the dynamics of real-world trading firms. By deploying specialized LLM-powered agents: from fundamental analysts, sentiment experts, and technical analysts, to trader, risk management team, the platform collaboratively evaluates market conditions and informs trading decisions. Moreover, these agents engage in dynamic discussions to pinpoint the optimal strategy.

    TradingAgents framework is designed for research purposes. Trading performance may vary based on many factors, including the chosen backbone language models, model temperature, trading periods, the quality of data, and other non-deterministic factors. It is not intended as financial, investment, or trading advice.

    Our framework decomposes complex trading tasks into specialized roles. This ensures the system achieves a robust, scalable approach to market analysis and decision-making.

    Excerpt from the TauricResearch/TradingAgents README on GitHub (Apache-2.0 license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Aug 9: +525Aug 10: +257Aug 11: +279Aug 27: +433Aug 28: +338Sep 9: +401Sep 10: +674Sep 11: +103Sep 15: +672Sep 16: +374Sep 17: +160Oct 25: +286Oct 26: +935Oct 27: +1,481Oct 28: +1,221Oct 29: +567Oct 30: +437Oct 31: +384Nov 1: +347Nov 2: +472Nov 3: +438Nov 4: +382Nov 5: +287Nov 6: +207

    +24,531 stars over 90 days across 31 snapshot days; best day +1,481 on Oct 27.

    Forks

    23.1K+4.5K

    Open issues

    123-221

    Compared with peers in AI Agents

    Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth

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