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Open-source Python framework with 52k+ GitHub stars for building autonomous AI agents with multi-agent orchestration, browser automation, MCP support, and custom tool integration.
Editor's take: “Capable AI tool with a focused use case and functional feature set” — Sohail Akhtar
Reviewed by Sohail Akhtar
Lead Editor & Founder
What we like
Limitations
| Plan | Details |
|---|---|
| Free | The full framework, including source code, documentation, and all agent modes, is available at no cost under the MIT License on GitHub. Users pay only for LLM API usage charged directly by their chosen provider. |
| Paid | No paid tier exists. OpenManus is entirely open-source with no commercial licensing requirements. |
OpenManus is free and open-source under the MIT License. There is no platform cost for the framework itself. Users are responsible for their own LLM API costs, such as OpenAI API charges for GPT-4o, which are billed directly by the LLM provider based on token usage. No subscription, usage fee, or invitation is required to access or deploy the framework.
Quick Summary
OpenManus is an open-source AI agent framework developed by core contributors of the MetaGPT project that enables developers to build, deploy, and customize fully autonomous general-purpose AI agents without requiring invitation codes, cloud subscriptions, or proprietary platform access. It was created in March 2025 as an open alternative to the invite-only Manus AI agent and has since accumulated over 52,000 GitHub stars. The framework supports multi-agent orchestration, browser automation, tool integration, data analysis, and is extendable with custom tools through a modular Python-based architecture.
Associated Tags
open source AI agents, autonomous AI agent, multi-agent framework, browser automation, MCP support, reinforcement learning agents, self-hosted AI, Manus AI alternative
Who should use OpenManus?
Discover practical workflows and real-world scenarios where OpenManus delivers key solutions.
A developer clones the repository, configures their OpenAI API key, and uses the multi-agent flow mode to build an autonomous research assistant that browses the web, extracts data, and compiles a structured report.
An AI researcher uses OpenManus-RL to apply GRPO-based reinforcement learning to fine-tune an LLM agent's planning behavior on a custom task dataset, without needing access to a commercial training platform.
A data engineer activates the DataAnalysis Agent in config.toml to automate a recurring pipeline that ingests raw CSV data, performs statistical analysis, and generates visualizations on a scheduled basis.
A team building an internal knowledge assistant extends the BaseTool class to create a custom tool that queries their internal database, then integrates it into a general agent workflow that answers employee questions autonomously.
A developer evaluates OpenManus as a reference architecture when designing a proprietary agentic product, studying its modular tool integration layer and prompt structures as a foundation for a more specialized implementation.
A researcher studying AI agent capabilities deploys OpenManus locally to run controlled experiments comparing GPT-4o and local model performance on autonomous task completion benchmarks without sending data to external platforms.
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