TradingAgents is an open-source multi-agent AI trading framework that simulates a hedge fund by coordinating LLM-powered agents for market analysis, strategy, and execution.

Simulate a full AI hedge fund using multiple agents for analysis, strategy, and risk management.

TradingAgents introduces a new approach to algorithmic trading by using multiple AI agents that collaborate like a professional trading team. Instead of relying on a single model, the system divides responsibilities across agents that specialize in market research, sentiment analysis, technical signals, and risk evaluation. These agents interact through structured communication and debates, leading to more informed and explainable trading decisions.

Core Features & Capabilities

Ideal for researchers, quant developers, AI engineers, fintech builders, and advanced traders who want to explore multi-agent AI systems, test trading strategies, and build intelligent financial decision-making frameworks.

• simulate a trading firm using multiple ai agents with specialized roles
• combine fundamental, sentiment, and technical analysis in one system
• run collaborative decision-making workflows with agent discussions
• evaluate strategies with backtesting and simulation tools
• build custom trading pipelines using modular open-source components

Why Developers Choose TradingAgents

Clone the repository from GitHub, install dependencies, and configure your preferred LLM backend. Run simulations or backtests using the multi-agent framework, customize agent roles and workflows, and experiment with different trading strategies to evaluate performance and decision-making approaches.

“TradingAgents shows how multiple AI agents can collaborate like a real trading firm to improve decision-making and strategy design.”

Getting Started with TradingAgents

By modeling trading as a collaborative multi-agent system, TradingAgents demonstrates how AI can improve financial reasoning, strategy generation, and risk management through structured interaction rather than isolated decision-making.