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OpenManus

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Basic Information

OpenManus is an open-source project that aims to reproduce and make accessible the capabilities of the Manus AI agent by providing a modular, containerized multi-agent framework. It is intended for developers and researchers who want to build, deploy, and experiment with autonomous agents that can collaborate to perform complex tasks. The repository bundles backend agent logic in Python, a Next.js frontend, a FastAPI server, CLI tooling and Docker Compose orchestration to run a reproducible environment. The stated use cases include personalized travel planning, data and stock analysis, and content generation. The project emphasizes extensibility, allowing contributors to add new agents, tools, LLM integrations and workflows while mirroring Manus-style autonomous task execution and community-driven development.

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App URL
https://github.com/henryalps/OpenManus
Github Repository
https://github.com/henryalps/OpenManus/blob/main/README.md

App Details

Features
The repository documents a multi-agent architecture with several example agents and node implementations such as browser_agent, coder_agent, coordinator, reporter_agent and research_agent. It is containerized with Docker and Docker Compose and includes separate frontend and backend containers, a FastAPI server for task delegation, and a Next.js web UI. Tool integrations listed include web browsing, code execution and data retrieval. The project includes a CLI client for testing, API endpoints for submitting tasks and checking status, a modular source tree for agents, tools, prompts and workflows, and documentation and configuration files to customize environment variables and volumes. The project is community-oriented and licensed under the UNLICENSE.
Use Cases
OpenManus helps teams prototype and evaluate multi-agent autonomous systems without building orchestration from scratch by providing a ready-made, containerized stack and example agents. Developers can run the system locally via docker-compose to expose a FastAPI task API, a CLI client and a Next.js web UI, enabling experimentation with agent coordination, tool integration, and workflow graphs. Researchers can extend LLM adapters, integrate new tools, and test tasks similar to the GAIA benchmark. The modular layout and Dockerized deployment make it easier to reproduce runs, share configurations, and iterate on agent logic, prompts and services while encouraging community contributions and extensions.

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