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TradingAgents: Multi-Agents LLM Financial Trading Framework

News

• [2026-10] TradingAgents v0.6.0 released with reports saved as one HTML page, a provider per model tier so the managers and analysts can run on different models, past decisions settled for every ticker while the analysts work, and company news read from Yahoo search while Yahoo's news feed is down.
• [2026-09] TradingAgents v0.5.2 released with parallel analysts for a faster analysis, a CLI that runs without prompts from flags such as --ticker and --date, the run's settings recorded in every report, and backtests that see only data published by each analysis date.
• [2026-09] TradingAgents v0.5.1 released with a package layout organised by what each module holds (import paths moved), optional Jev screening of social posts, GPT-6 Sol and Luna as the default models, and fixes to run isolation and SEC EDGAR statements.

Full release notes are in CHANGELOG.md.

Earlier news

• [2026-09] TradingAgents v0.5.0 released with point-in-time integrity across every dated path, SEC EDGAR fundamentals served as filed, backtesting over a ticker and date grid, portfolio-aware runs, and current model lineups across every provider.
• [2026-08] TradingAgents v0.4.0 released with look-ahead / point-in-time fixes across FRED macro, social sentiment, and the decision-log memory; clearer decision signals; working CLI checkpoint resume; Trader price grounding; and the GPT-5.6 and GLM-5.3 models.
• [2026-07] TradingAgents v0.3.1 released with correctness and stability fixes: Alpha Vantage look-ahead filtering, graph-router crash-safety, graph-shape-aware checkpoint resume, working crypto sentiment sources, a configurable LLM retry budget, Bedrock API-key auth, and Claude Sonnet 5 / Fable 5 support.
• [2026-06] TradingAgents v0.3.0 released with a verified data-access contract, an expanded provider registry (NVIDIA, Kimi, Groq, Mistral, Bedrock, and any OpenAI-compatible endpoint), FRED and Polymarket data vendors, a current-generation model catalog, and a CI gate.
• [2026-05] TradingAgents v0.2.5 released with the grounded Sentiment Analyst, GPT-5.5 etc. model coverage, Qwen/GLM/MiniMax dual-region support, TRADINGAGENTS_* env-var configurability with API-key auto-detection, remote Ollama support, non-US alpha benchmarks, and ticker path-traversal hardening.
• [2026-04] TradingAgents v0.2.4 released with structured-output agents (Research Manager, Trader, Portfolio Manager), LangGraph checkpoint resume, persistent decision log, DeepSeek/Qwen/GLM/Azure provider support, Docker, and a Windows UTF-8 encoding fix.
• [2026-03] TradingAgents v0.2.3 released with multi-language support, GPT-5.4 family models, unified model catalog, backtesting date fidelity, and proxy support.
• [2026-03] TradingAgents v0.2.2 released with GPT-5.4/Gemini 3.1/Claude 4.6 model coverage, five-tier rating scale, OpenAI Responses API, Anthropic effort control, and cross-platform stability.
• [2026-02] TradingAgents v0.2.0 released with multi-provider LLM support (GPT-5.x, Gemini 3.x, Claude 4.x, Grok 4.x) and improved system architecture.
• [2026-01] Trading-R1 Technical Report released, with Terminal expected to land soon.

🚀 TradingAgents | ⚡ Installation & CLI | 🎬 Demo | 📦 Package Usage | 🤝 Contributing | 📄 Citation

🎉 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 Framework

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.

Analyst Team

• Fundamentals Analyst: Evaluates company financials and performance metrics, identifying intrinsic values and potential red flags.
• Sentiment Analyst: Aggregates news headlines, StockTwits, and Reddit chatter into a single sentiment read to gauge short-term market mood.
• News Analyst: Monitors global news and macroeconomic indicators, interpreting the impact of events on market conditions.
• Technical Analyst: Utilizes technical indicators (like MACD and RSI) to detect trading patterns and forecast price movements.

The selected analysts work at the same time, each on its own tools, and the research debate starts once all of their reports are in.

Researcher Team

• Comprises both bullish and bearish researchers who critically assess the insights provided by the Analyst Team. Through structured debates, they balance potential gains against inherent risks.

Trader Agent

• Composes reports from the analysts and researchers to make informed trading decisions, determining the timing and magnitude of trades.

Risk Management and Portfolio Manager

• Continuously evaluates portfolio risk by assessing market volatility, liquidity, and other risk factors. The risk management team evaluates and adjusts trading strategies, providing assessment reports to the Portfolio Manager for final decision.
• The Portfolio Manager approves/rejects the transaction proposal. If approved, the order will be sent to the simulated exchange and executed.

Installation and CLI

Installation

Clone TradingAgents:

git clone https://github.com/TauricResearch/TradingAgents.git
cd TradingAgents

TradingAgents needs Python 3.11 or later. Create a virtual environment in any of your favorite environment managers:

conda create -n tradingagents python=3.13
conda activate tradingagents

Or with uv:

uv venv --python 3.13
source .venv/bin/activate

Install the package and its dependencies (uv pip install . with uv):

pip install .

Docker

Alternatively, run with Docker:

cp .env.example .env  # add your API keys
docker compose run --rm tradingagents

After updating the repository, rebuild the image with docker compose build.

Results, reports, the memory log and the cache live in the tradingagents_data volume. To keep them in a folder on the host instead, create the folder and point TRADINGAGENTS_DATA_DIR at it, in .env or the shell: mkdir -p data && TRADINGAGENTS_DATA_DIR=./data docker compose run --rm tradingagents.

For local models with Ollama:

docker compose --profile ollama run --rm tradingagents-ollama

Required APIs

TradingAgents supports multiple LLM providers. Set the API key for your chosen provider:

export OPENAI_API_KEY=...          # OpenAI (GPT)
export GOOGLE_API_KEY=...          # Google (Gemini)
export ANTHROPIC_API_KEY=...       # Anthropic (Claude)
export XAI_API_KEY=...             # xAI (Grok)
export DEEPSEEK_API_KEY=...        # DeepSeek
export DASHSCOPE_API_KEY=...       # Qwen (international, dashscope-intl.aliyuncs.com)
export DASHSCOPE_CN_API_KEY=...    # Qwen (China, dashscope.aliyuncs.com)
export ZHIPU_API_KEY=...           # GLM via Z.AI (international)
export ZHIPU_CN_API_KEY=...        # GLM via BigModel (China, open.bigmodel.cn)
export MINIMAX_API_KEY=...         # MiniMax (global, api.minimax.io)
export MINIMAX_CN_API_KEY=...      # MiniMax (China, api.minimaxi.com)
export OPENROUTER_API_KEY=...      # OpenRouter
export MISTRAL_API_KEY=...         # Mistral
export MOONSHOT_API_KEY=...        # Kimi (Moonshot)
export GROQ_API_KEY=...            # Groq
export NVIDIA_API_KEY=...          # NVIDIA NIM
export FRED_API_KEY=...            # FRED macro data (free, optional)
export ALPHA_VANTAGE_API_KEY=...   # Alpha Vantage
export TYPESAFE_API_KEY=...        # Jev social-post screening (optional)

For Azure OpenAI, copy .env.enterprise.example to .env.enterprise and fill in your credentials.

For AWS Bedrock, install the extra with pip install ".[bedrock]", set llm_provider: "bedrock", configure AWS credentials (environment variables, ~/.aws/credentials, or an IAM role) and AWS_DEFAULT_REGION, and use a Bedrock model ID, e.g. us.anthropic.claude-opus-5-5.

For local models, configure Ollama with llm_provider: "ollama". The default endpoint is http://localhost:11434/v1; set OLLAMA_BASE_URL to point at a remote ollama-serve. Pull models with ollama pull <name>, and pick "Custom model ID" in the CLI for any model not listed by default.

For any other OpenAI-compatible server (vLLM, LM Studio, llama.cpp, or a custom relay), use llm_provider: "openai_compatible" and set the endpoint via backend_url (or TRADINGAGENTS_LLM_BACKEND_URL), e.g. http://localhost:8000/v1 for vLLM or http://localhost:1234/v1 for LM Studio. The model is whatever your server serves. No key is needed for local servers; set OPENAI_COMPATIBLE_API_KEY when the endpoint requires one.

With TYPESAFE_API_KEY set, the Sentiment Analyst screens StockTwits and Reddit posts with TypeSafe's Jev before reading them. Posts that are not about the company are dropped, and each source opens with a count of the remaining posts by stance: bullish, bearish, neutral, or unclear. Without the key, posts pass through unscreened. jev-latest moves with new releases; set TYPESAFE_DEFAULT_MODEL to a versioned ID such as jev-1.13.0 to hold it fixed across runs. To reach Jev through OpenRouter, put an OpenRouter key in TYPESAFE_API_KEY and set TYPESAFE_BASE_URL=https://openrouter.ai/api.

Alternatively, copy .env.example to .env and fill in your keys:

cp .env.example .env

CLI Usage

Launch the interactive CLI:

tradingagents          # installed command
python -m cli.main     # alternative: run directly from source

You will see a screen where you can select your desired tickers, analysis date, LLM provider, research depth, and more. Your previous run's answers come back as the defaults, so pressing Enter accepts them. The TRADINGAGENTS_* variables in .env still skip their step entirely.

To run without questions, for a scheduled job or a script, answer the per-run steps with flags and the rest with TRADINGAGENTS_* variables: ```bash export TRADINGAGENTS_LLM_PROVIDER=openai TRADINGAGENTS_QUICK_THINK_LLM=gpt-6-luna TRADINGAGENTS_DEEP_THINK_LLM=gpt-6-sol export TRADINGAGENTS_OUTPUT_LANGUAGE=English TRADINGAGENTS_MAX_DEBATE_ROUNDS=1 TRADINGAGENTS_MAX_RISK_ROUNDS=1 tradingagents --ticker NVDA --date 2026-09-23 --analysts market,new