OpenManus killed the AI agent invite wall. Here is how to run it in ten minutes.

If you have been watching the AI agent space with a mix of fascination and frustration, fascinated by what tools like Manus can do, frustrated that you cannot get an invite code, the wait is over. OpenManus, released by the team behind MetaGPT, is designed to be as open and accessible as possible. It answers a question many developers have been asking: can we run these agents without a corporate gatekeeper? According to research on agent reliability in production, the answer has been elusive until now.

The prototype was built in three hours by a team including Xinbin Liang, Jinyu Xiang, Zhaoyang Yu, Jiayi Zhang, and Sirui Hong. That speed is not sloppiness; it reflects how mature the underlying components have become. OpenManus borrows from proven projects like Anthropic's computer-use, browser-use, and crawl4ai, and wraps them into a clean, extensible framework. This modular approach echoes what IBM demonstrated with CUGA, where a prompt and tool list were enough to build apps.

Installation is trivially simple

Getting started takes minutes. The recommended method uses uv, a fast Python package installer:

curl -LsSf https://astral.sh/uv/install.sh | sh
git clone https://github.com/FoundationAgents/OpenManus.git
cd OpenManus
uv venv, python 3.12
source .venv/bin/activate
uv pip install -r requirements.txt

Then copy the example config file:

cp config/config.example.toml config/config.toml

Edit config/config.toml to add your preferred LLM's API key (OpenAI's GPT-4o is used in the example, but any compatible model works) and you are ready.

A single command launches the agent:

python main.py

Then type your idea directly into the terminal. No dashboard, no cloud subscription, no approval queue. This kind of local-first autonomy is exactly what open-source projects like local LLMs for repository triage have been pushing for.

Not just a single agent

OpenManus ships with multiple execution modes. The basic main.py runs a general-purpose agent. For multi-agent orchestration, run_flow.py is available, though the team labels it as unstable. A dedicated data analysis agent can be activated in config.toml by setting use_data_analysis_agent = true in the [runflow] section.

A browser automation tool is optional but recommended for agents that need to navigate the web. Install it with:

playwright install

The RL frontier: OpenManus-RL

Beyond the immediate agent framework, the project introduces OpenManus-RL, a companion effort focused on reinforcement learning-based tuning methods, specifically Group Relative Policy Optimization (GRPO), for LLM agents. Developed in collaboration with researchers from UIUC, this is aimed at those who want to push agent performance beyond prompt engineering.

It is a simple implementation, so we welcome any suggestions, contributions, and feedback.

The team is candid about the project's early stage, but that is exactly the point: the community can shape it from day one.

Why this matters right now

The AI agent landscape has been dominated by proprietary, invite-only systems. Manus demonstrated what was possible, but kept the keys locked. OpenManus breaks that model. Any developer with a Python environment and an API key can now run autonomous agents, integrate them into workflows, and experiment with multi-agent architectures, all without asking for permission.

For startups, researchers, and hobbyists, this lowers the barrier to entry to virtually zero. The project also accepts pull requests and issues, meaning the next innovation could come from anyone. This level of open access is reminiscent of Anthropic's Claude Science workbench for researchers, but even more democratic.

Sponsorship and backing

Computing support for the project comes from PPIO, a GPU cloud provider positioned as an affordable and easily integrated MaaS solution. The Hugging Face demo space is supported by stepfun (阶跃星辰).

The code is available under a Zenodo citation (doi: 10.5281/zenodo.15186407) and can be cited in academic work.

What to do next

OpenManus is ready. The installation steps above will get you a running agent in under ten minutes. The repository is at github.com/FoundationAgents/OpenManus. There is no reason to wait. The only thing missing is your idea. For those who want to understand the deeper mechanics, the production traps for AI agents offer a valuable cautionary read before you deploy at scale.