AIエージェント
langroid
Langroid: a principled Python framework for building LLM multi-agent applications (from CMU/UW researchers)
★ 4.1k スターMIT
ガイドを見る →TradingAgentsは、TauricResearchが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: TauricResearch/TradingAgents）。GitHubスターは110,264、フォークは21,201で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年10月3日です。
Multi-agent LLM framework that mirrors a trading firm: analysts, researchers, trader and risk team
Decide what you want from TradingAgents and check the README and requirements to confirm it fits your setup and Python environment.
Clone the repository from GitHub (github.com/TauricResearch/TradingAgents) or use the documented install channel if you do not need the source.
Follow the README quickstart to install dependencies and set any required configuration or credentials.
Work through the README's first example end to end, then adapt it to your own project or data.
Specialized analyst agents cover fundamentals, sentiment, news and technical analysis.
Bullish and bearish researcher agents debate the analysts' findings before a decision.
A trader agent turns the research debate into concrete trading decisions.
Risk management agents and a portfolio manager review and approve positions.
Ships a CLI (`python -m cli.main`) for running the framework interactively.
Supports multiple markets and tickers, documented in the README's markets section.
Start by getting the source and entering the project directory.
git clone https://github.com/TauricResearch/TradingAgents.git
cd TradingAgentsThe README uses a conda environment on Python 3.13, then installs the package from source.
conda create -n tradingagents python=3.13
conda activate tradingagents
pip install .Start the interactive CLI and pick a ticker and date to analyse.
python -m cli.mainIt is maintained by TauricResearch and contributors on GitHub, where the repository github.com/TauricResearch/TradingAgents is the canonical home for code, issues and releases.
Yes — it is open source under the Apache-2.0 license. Read the license in the repository for what is permitted, including any conditions on redistribution or commercial use.
Yes — it requires LLM API keys (for example OpenAI) and market data API access as documented under Required APIs.
Compared with single-prompt stock analysis: TradingAgents splits the work across analyst, researcher, trader and risk agents that debate, instead of asking one model for one answer.
Clone github.com/TauricResearch/TradingAgents, follow the README quickstart to install it, and run the documented first example before adapting it to your own work.
Langroid: a principled Python framework for building LLM multi-agent applications (from CMU/UW researchers)
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