OpenManus

OpenManus is a general-purpose AI agent framework hosted on GitHub under the MIT License, built by researchers and engineers from the MetaGPT project including Xinbin Liang, Jinyu Xiang, Zhaoyang Yu, Jiayi Zhang, and Sirui Hong. The framework provides a modular agent architecture organized into a core agent engine handling reasoning, planning, and execution; a tool integration layer for connecting external APIs and services; and a multi-modal processing layer supporting text, vision inputs, and browser automation via Playwright. Agents are configured through a config.toml file where users specify their LLM provider and API key—defaulting to GPT-4o but compatible with other OpenAI-compatible endpoints and local model servers. Three main execution modes are available: a single general agent via main.py, an MCP (Model Context Protocol) tool version via run_mcp.py, and a multi-agent flow mode via run_flow.py. A DataAnalysis Agent is built into the flow mode for data analysis and visualization tasks. OpenManus-RL, developed collaboratively with researchers from UIUC, extends the framework with reinforcement learning-based tuning methods such as GRPO for improving LLM agent behavior through policy optimization. OpenManus is used by developers and researchers who need a fully controllable, self-hostable AI agent without the access restrictions and cost overhead of commercial agent platforms. AI researchers use OpenManus-RL to experiment with reinforcement learning approaches for training and fine-tuning LLM-based agents. Browse AI solutions. Teams building internal automation tools use the modular tool integration layer to connect the agent to their existing APIs and data systems. Data engineers use the DataAnalysis Agent mode to automate analytical workflows. Developers exploring agentic architectures reference OpenManus as a lightweight, readable starting point that acknowledges contributions from established projects including browser-use, MetaGPT, OpenHands, and SWE-agent. OpenManus's core advantages are its MIT License allowing both personal and commercial use, its zero platform cost beyond LLM API charges, and the active community of over 52,000 starred contributors providing ongoing improvements and extensions. Because the framework is self-hosted, users maintain full control over data, model selection, and tool access without platform-level restrictions. The main limitation is that setup requires Python 3.12, familiarity with terminal-based installation, and configuration of LLM API credentials, creating a meaningful technical barrier for non-developer users. The project moves quickly, which means documentation can lag behind new features, and production deployments require additional engineering work for reliability, monitoring, and error recovery that commercial agent platforms typically handle automatically See similar solutions.