TauricResearch/TradingAgents

Installation

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

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

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

CLI Usage

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:

Each flag skips only its own question. Run without a terminal, a missing answer stops the run before it starts and names the flag or variable to set.

At start-up the interactive CLI mentions a newer release and notices from Tauric Research. It checks at most once a day, in the background, and sends nothing about your runs; set TRADINGAGENTSNOUPDATECHECK=1 to turn it off. Runs without a terminal never check.

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

Python Usage

To use TradingAgents inside your code, you can import the tradingagents module and initialize a TradingAgentsGraph() object. The .propagate() function will return a decision. You can run main.py, here's also a quick example:

You can also adjust the default configuration to set your own choice of LLMs, debate rounds, etc.

The quick model serves the analysts, researchers, debaters and trader; the deep model serves the research and portfolio managers. Each can run on its own provider, for example the managers on Claude while the rest run on OpenAI:

A tier on its own provider uses that provider's key and default endpoint; set quickthinkbackendurl or deepthinkbackendurl for a local or relay endpoint. The TRADINGAGENTSDEEPTHINKPROVIDER and TRADINGAGENTSQUICKTHINKPROVIDER variables set them for the CLI, together with the tier's model variable.

from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG

ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy())

# forward propagate
state, decision = ta.propagate("NVDA", "2026-09-01")
print(decision)

# the same report tree the CLI saves, under results_dir/reports
ta.save_reports(state, "NVDA")

Implementation Details

We built TradingAgents with LangGraph to ensure flexibility and modularity. The framework supports multiple LLM providers: OpenAI, Google, Anthropic, xAI, DeepSeek, Qwen (Alibaba DashScope, international and China endpoints), GLM (Zhipu), MiniMax (global + China), OpenRouter, Ollama for local models, Azure OpenAI for enterprise, and your own ChatGPT plan.

News

- [2026-10] TradingAgents v0.7.0 released with runs on your own ChatGPT Plus or Pro plan, backtests scored from the first close after each decision, a note at start-up when a newer release is out, and a wheel attached to each GitHub release. - [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 a

• [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
• [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
• [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;
• [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 sentime
• [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 Polymar
• [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-
• [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 provi
• [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.

Analyst Team

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.

• 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.

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.