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You can run the OpenHands application on your own computer, but that alone does not mean its model requests or every other activity stay on that computer. OpenHands supports both hosted model providers and local model servers; keeping inference local requires configuring a local server separately. Your privacy boundary also depends on the integrations, credentials, network access, and host permissions you enable.
What “running OpenHands locally” means
There are two distinct choices: where the OpenHands application runs and where the language model runs. OpenHands can run on your machine while sending model requests to a hosted provider. To run inference locally, configure OpenHands to use a local model server such as LM Studio, Ollama, vLLM, or SGLang.
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| Configuration | Where the application runs | Where inference runs | What to consider |
|---|---|---|---|
| Local app with hosted model | Your machine | Hosted provider | Most hosted model routes require a provider, model, and API key. Review the provider’s handling of requests and credentials. |
| Local app with local model server | Your machine | A model server you run | Requires local model setup and sufficient hardware; local inference does not by itself establish that every application activity or integration stays on-device. |
OpenHands’ setup documentation and local LLM guide describe these as configuration options, not as a blanket guarantee that all code, credentials, or network traffic remain on your computer.
Check your machine before installing
The OpenHands setup page lists macOS with Docker Desktop, Linux, and Windows through WSL with Docker Desktop. It recommends a modern processor and at least 4GB RAM to run OpenHands. That is application guidance, not a sufficient hardware specification for running a substantial language model locally; local inference can require much more memory and a compatible GPU.
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Installation instructions, image tags, and launch conventions can change. Use the linked live setup page to confirm the current commands before running them.
Choose an installation route
CLI launcher with uv
The documented recommended route installs the OpenHands CLI using Python 3.12:
uv tool install openhands --python 3.12
openhands serveThe setup page documents --gpu for GPU support via nvidia-docker and --mount-cwd to mount the current working directory into the container. Check the live documentation for exact option behavior and prerequisites.
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Install with pip
The setup page also documents pip install openhands for Python 3.12 or newer. It notes that uv is still needed for the default MCP servers, so this route may not remove the need to install uv.
Launch the Docker image directly
OpenHands documents a direct Docker launch option that publishes the interface on port 3000 and mounts both /var/run/docker.sock and ~/.openhands. Use the exact current command on the official setup page, rather than copying an old image tag or assuming launch arguments have not changed.
Understand the access you grant
The Docker socket mount in the documented launch example is a significant trust-boundary decision: it gives the OpenHands container access to the host’s Docker service. Do not treat a container as an automatic security wall when you have mounted this socket. Review Docker’s security guidance, understand the host access your configuration allows, and run only tasks you trust with that access. The OpenHands setup page documents the mount; it does not provide an independent security assessment of its implications.
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Also review any integrations, credentials, workspace mounts, and network access you enable. Running the application locally or choosing a local model does not, by itself, prove that every request or secret stays on the machine. Avoid entering credentials or granting access until you understand how the selected configuration uses them.
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Configure a local model server
OpenHands’ local LLM guide covers LM Studio, Ollama, vLLM, and SGLang. It presents LM Studio as a straightforward server option and gives a configuration pattern using an OpenAI-compatible model identifier, a base URL, and an API key value. For a server without authentication, the guide uses a placeholder key; that value is not a real provider credential. Follow the current guide for the exact fields and server-specific settings.
Model and hardware example
The OpenHands local LLM guide names Qwen3.6-35B-A3B as a model to try and specifies, for its quantized variants, a recent GPU with at least 24GB VRAM or Apple Silicon with at least 64GB unified memory. The page dates that recommendation to 2026-05-21. These are model-specific configuration recommendations, not universal OpenHands requirements or benchmark results.
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Network binding matters
In its Linux LM Studio example, OpenHands explains that Docker cannot reach a host service bound only to 127.0.0.1 and directs users to enable “Serve on Local Network.” Changing a service’s bind address can make it reachable from other devices on the local network. Before doing so, check the server’s authentication settings and which networks can reach it; do not expose a model server more broadly than intended.
Set expectations for local models
Local model capability and tool-use reliability vary. OpenHands’ local LLM documentation warns that a model may give poor responses, take a long time, or return malformed JSON. Its guidance is explicit: “Local LLMs can have limited functionality; use a capable model and GPU-backed server for the best experience.” The LLM configuration overview provides additional context on model configuration. A local model should not be assumed equivalent to a hosted model for coding tasks.
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