AI Agents
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View guide →// agents · TauricResearch/TradingAgents
TradingAgents
TradingAgents is an open-source ai agents project by TauricResearch (TauricResearch/TradingAgents on GitHub). It has 110,264 GitHub stars and 21,201 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 3 October 2026.
Multi-agent LLM framework that mirrors a trading firm: analysts, researchers, trader and risk team
★ 110.3k stars⑂ 21.2k forksApache-2.0updated 2026-10
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
so here's a placeholder. The guide below is complete regardless.
// project facts
Project facts
- Repository
- TauricResearch/TradingAgents
- GitHub stars
- 110,264
- Forks
- 21,201
- License
- Apache-2.0
- Last updated on GitHub
- 3 October 2026
- Category
- AI Agents
- Listing
- Full install guide
- Official site
- Official site
// how it works
How it works
- Clarify your goal
Decide what you want from TradingAgents and check the README and requirements to confirm it fits your setup and Python environment.
- Get the code
Clone the repository from GitHub (github.com/TauricResearch/TradingAgents) or use the documented install channel if you do not need the source.
- Install and configure
Follow the README quickstart to install dependencies and set any required configuration or credentials.
- Run your first task
Work through the README's first example end to end, then adapt it to your own project or data.
// features
Why people love it
Analyst team
Specialized analyst agents cover fundamentals, sentiment, news and technical analysis.
Researcher team
Bullish and bearish researcher agents debate the analysts' findings before a decision.
Trader agent
A trader agent turns the research debate into concrete trading decisions.
Risk and portfolio management
Risk management agents and a portfolio manager review and approve positions.
CLI included
Ships a CLI (`python -m cli.main`) for running the framework interactively.
Multiple markets
Supports multiple markets and tickers, documented in the README's markets section.
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// run it
Run it yourself
- Clone the repository
Start by getting the source and entering the project directory.
git clone https://github.com/TauricResearch/TradingAgents.git cd TradingAgents - Create the environment and install
The 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 . - Run the CLI
Start the interactive CLI and pick a ticker and date to analyse.
python -m cli.main
// use cases
What you can build
- Researching a stock with a structured multi-agent debate
- Backtesting agent-driven trading ideas on historical dates
- Studying multi-agent collaboration patterns in a finance setting
- Generating analyst-style reports for a ticker
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// faq
Questions, answered
Who maintains TradingAgents?
It is maintained by TauricResearch and contributors on GitHub, where the repository github.com/TauricResearch/TradingAgents is the canonical home for code, issues and releases.
Is TradingAgents free to use?
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.
Does it need a paid API key?
Yes — it requires LLM API keys (for example OpenAI) and market data API access as documented under Required APIs.
How does TradingAgents compare to alternatives?
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.
How do I get started?
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.
// keep exploring
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Get TradingAgents
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