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You can run OpenHands on your own computer, but that does not automatically mean its AI inference—or every credential and network request—stays there. The key choice is whether OpenHands connects to a hosted model provider or to a model server running locally. You also need to understand the host access granted by the Docker setup.
What running OpenHands locally does—and does not—mean
OpenHands supports running its application on a personal machine and configuring it to use either a hosted model provider or a local model server. These are separate decisions: installing OpenHands locally does not, by itself, make model inference local.
Most hosted-provider configurations require a provider, model, and API key. For local inference, OpenHands documents connections to model servers including LM Studio, Ollama, vLLM, and SGLang. Even with a local model server, the setup documentation does not establish that every application activity, integration, credential, or network request remains on the machine. Treat “local” as a description of the components you have configured, not a blanket privacy guarantee.
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OpenHands’ documentation for setup and local models is available in its local setup guide and local LLM guide.
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Choose an installation route
The official setup guide documents a CLI launcher installed with uv, a pip installation, and direct Docker launch. Its listed platforms are macOS with Docker Desktop, Linux, and Windows through WSL with Docker Desktop. The guide recommends a modern processor and at least 4GB of RAM to run OpenHands; that baseline is for the application, not for running a capable local model.
Recommended CLI launcher
The guide’s recommended installation command is:
uv tool install openhands --python 3.12Then start the service with:
openhands serveThe CLI offers --gpu for GPU support through nvidia-docker and --mount-cwd to mount the current working directory into the container. Review the current setup guide for the exact invocation and prerequisites before adding options.
pip installation
The documented pip option is pip install openhands for Python 3.12 or newer. The setup guide notes that uv is still needed for the default MCP servers, so pip alone may not cover that part of the default configuration.
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Direct Docker launch
OpenHands also documents a direct Docker command that publishes the interface on port 3000 and mounts both /var/run/docker.sock and ~/.openhands. Because image tags and launch conventions can change, use the command on the live setup page rather than relying on a copied command that may have aged.
Understand the Docker socket trust boundary
The Docker example mounts the host’s Docker socket into the OpenHands container. This is a sensitive host-access decision, not just a routine file mount: it enables interaction with the host’s Docker daemon. Before running tasks, understand the access your configuration grants and only run work you trust in that environment. OpenHands’ setup page documents the mount; consult Docker’s security guidance for Docker-specific security considerations.
The guide also mounts ~/.openhands for OpenHands data. Consider which local files and services are accessible through your chosen mounts and integrations; do not infer from the application running on your machine that the configuration is isolated from the host.
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Connect OpenHands to a local model server
OpenHands’ local-model guide recommends LM Studio as a straightforward server option and gives an example using Qwen3.6-35B-A3B. The configuration uses an OpenAI-compatible model identifier, a base URL, and an API key field. For servers without authentication, the guide uses a placeholder key value; it is not a real provider credential.
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The guide also covers Ollama, Atomic Chat, vLLM, and SGLang. Follow the current instructions for your chosen server, since the base URL, model identifier, authentication, and network settings depend on how that server is configured.
Check how the server is bound to the network
A container may not be able to reach a service bound only to the host’s loopback address. OpenHands’ Linux LM Studio example notes that Docker cannot reach a host service bound only to 127.0.0.1 and instructs users to enable “Serve on Local Network.” Changing the bind setting can make the model server reachable from other devices on the local network. Review its authentication and network exposure before enabling that setting.
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Separate OpenHands requirements from local-model requirements
OpenHands’ setup guide recommends at least 4GB of RAM for running the application. The model requirements can be substantially higher. For quantized variants of its documented Qwen3.6-35B-A3B recommendation, the local LLM guide specifies either a recent GPU with at least 24GB of VRAM or Apple Silicon with at least 64GB of unified memory. The guide dates that model recommendation to 2026-05-21; it is guidance for that model configuration, not a universal OpenHands minimum.
OpenHands cautions that local models can have limited functionality and that tool-use reliability varies. A model may respond poorly, take a long time, or return malformed JSON. A locally served model is therefore not automatically equivalent to a hosted model for coding-agent tasks. The LLM configuration overview provides further context on model setup.
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Make a deliberate privacy and access choice
- If your priority is simpler setup: use a hosted model provider, understanding that model requests go to that provider and that its API key is part of the configuration.
- If your priority is local inference: configure OpenHands to use a local model server, verify the endpoint and model settings, and account for the local hardware needed by your chosen model.
- If your priority is limiting host access: inspect the Docker socket mount, working-directory mounts, integrations, and server network binding before running tasks.
- If you need a firm data-residency guarantee: do not rely on the word “local” alone. Review the exact version, configuration, integrations, and network behavior relevant to your use case.
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