Awesome GitHub RepositoriesMulti-Agent Research Frameworks

TradingAgents is an autonomous financial research and simulation framework that coordinates specialized agents to analyze market data and execute investment strategies. The system functions as a multi-agent debate environment where independent units critique financial insights through structured, adversarial reasoning to improve decision accuracy and mitigate investment risks. The platform distinguishes itself through a risk-gated transaction pipeline that validates all proposed financial actions against market volatility and liquidity constraints before execution on a simulated exchange. To

DeepResearch is an autonomous research agent framework designed to orchestrate multi-step information gathering and complex reasoning tasks. The platform functions as an agent orchestration system that manages the entire lifecycle of autonomous research, from initial planning and web navigation to the synthesis of evidence-backed reports. The framework distinguishes itself through a specialized training pipeline that supports the development and fine-tuning of autonomous models using reinforcement learning and structured knowledge graph synthesis. By employing parallel agent coordination, the

This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

AutoResearchClaw is an agentic system designed to automate the scientific research process. It functions as an autonomous research agent and workflow automator that manages the entire lifecycle of a project, from initial hypothesis generation and literature review to experimental execution and the production of LaTeX-formatted academic papers. The system distinguishes itself through a multi-agent research pipeline that utilizes structured debates for hypothesis refinement and peer review. It employs a branch-and-merge architecture to explore parallel research directions and integrates human-i

This project is a comprehensive AI infrastructure that combines an LLM agent orchestration framework, an autonomous research system, and a local AI environment. It centers on the creation of a personal knowledge graph and a programmatic prompt engineering library to provide long-term memory and optimized reasoning for artificial intelligence tasks. The system is distinguished by its ability to compose multi-agent teams using specialized personas and deterministic skills to execute complex workflows. It features an autonomous research pipeline capable of deep investigations and adversarial ana

Feynman is an open-source AI research agent that coordinates multi-agent workflows to search papers, run experiments, and produce cited research briefs. It orchestrates parallel researcher agents that independently investigate subtopics, then synthesizes and verifies findings through a multi-step orchestration loop, enabling deep research across academic papers, web sources, and code. The tool distinguishes itself through several specialized capabilities, including paper claim verification that audits research paper claims against actual code implementations to identify mismatches and validat

FinRobot is an AI-powered financial analysis framework that coordinates multiple specialized agents to automate equity research, financial analysis, and investment risk assessment. At its core, it functions as a multi-agent orchestration system where a director and task manager allocate financial tasks to the most suitable large language models based on performance metrics and task requirements. The framework distinguishes itself through its ability to execute complex multi-step financial workflows by routing tasks through perception, reasoning, and action modules. It generates professional e

AgentLaboratory is a multi-agent research system that automates the entire scientific experimentation process, from literature review through experiment execution to report generation, using a sequence of specialized AI agents. The system orchestrates a team of language-model-driven agents—a literature reviewer, experimental planner, executor, and report writer—to autonomously complete an end-to-end research workflow. The system distinguishes itself by saving progress at every checkpoint, enabling seamless recovery and continuation after interruptions or failures. Agents build on each other's

ms-agent is an LLM agent framework and multi-agent orchestration system designed to build autonomous entities that combine large language models with tool calling and structured workflows. It serves as a tool integration platform and workflow engine for executing complex tasks through the coordination of specialized agents. The project distinguishes itself through a multimodal agent workflow engine capable of automating the production of text, images, and video. It features a sandboxed code execution environment for running generated code and quantitative data analysis in isolated containers,

EvoScientist is an autonomous AI scientist and multi-agent research framework designed to plan, code, and execute end-to-end scientific research workflows. It functions as an agentic workflow orchestrator that uses a state-machine to coordinate specialized agents through iterative phases of planning, execution, and verification. The system is distinguished by a persistent knowledge graph memory that distills agent interactions into reusable skills and a hub for integrating external tools via the Model Context Protocol. It features a provider-agnostic model layer for switching between language

QuantMuse is an algorithmic trading platform and quantitative trading framework that integrates large language models with mathematical analysis to automate market insights and trading strategies. It functions as a system for building, backtesting, and executing strategies using both historical and real-time market data. The framework is distinguished by its use of large language models for financial analysis and sentiment extraction from news and social media. It utilizes autonomous agents with chain-of-thought reasoning to generate market intelligence and strategic reports, while employing

This project provides a comprehensive framework for building, deploying, and orchestrating autonomous agents within a decentralized network. It serves as a collection of patterns and examples for developing intelligent software entities capable of performing complex tasks, making decisions, and interacting with other agents to achieve shared goals. The framework distinguishes itself through its focus on multi-agent orchestration and decentralized communication. It enables the coordination of specialized agent teams that collaborate on workflows through structured messaging protocols, allowing