You can run an open-source AI agent on your computer by installing an agent application, starting a separate local model server, and connecting the two. A practical route is OpenHands with Ollama or LM Studio; Open Interpreter is an alternative if you prefer a terminal-based coding agent. The agent handles tasks and tool access, while the model runtime serves the language model.
What you need before installing a local AI agent
Check that your operating system and hardware fit the particular application and model you intend to use. OpenHands documents support for macOS with Docker Desktop, Linux, and Windows with WSL and Docker Desktop. Its setup guidance recommends a modern processor and at least 4GB of RAM for OpenHands itself; that figure is not a guarantee that a local language model will fit or run well. OpenHands setup documentation
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For Windows, install WSL and Ubuntu, verify that WSL 2 is in use, and enable Docker Desktop’s WSL 2 engine and integration. OpenHands says it tested Ubuntu 22.04. Run Docker-related commands from the WSL terminal.
Local model requirements vary with model size, quantization, context length, and hardware. As one model-specific example, OpenHands’ current guide for quantized Qwen3.6-35B-A3B variants calls for either a recent GPU with at least 24GB of VRAM or Apple Silicon with at least 64GB of unified memory. Those figures apply to that example, not to every local model. OpenHands local LLM documentation
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How to install and run OpenHands with a local model
1. Install OpenHands
OpenHands recommends its CLI launcher, installed with uv and Python 3.12. After installing uv, run:
uv tool install openhands --python 3.12
openhands serveThe serve command starts OpenHands; follow the terminal output to open its interface. The documentation also describes a Docker installation route. Use its current instructions and image tags rather than relying on an old, version-pinned command. If you use NVIDIA Docker, the documented CLI option for GPU support is openhands serve --gpu. To give OpenHands access to the current working directory, it documents openhands serve --mount-cwd. Review the access implications before mounting a project.
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2. Install a local model runtime
OpenHands documents local backends including LM Studio, Ollama, vLLM, and SGLang. LM Studio is the guide’s straightforward graphical option. If you choose Ollama, its installer commands are:
macOS or Linux:
curl -fsSL https://ollama.com/install.sh | shWindows PowerShell:
irm https://ollama.com/install.ps1 | iexThese commands come from Ollama’s download page. Install a model that fits your available memory and your needs for speed, context, and agent tool use. Ollama notes that local speed depends on hardware and that large models can be slow without a strong GPU; the available documentation does not establish a universal specification or a cross-machine speed comparison.
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3. Connect OpenHands to the model server
In OpenHands settings, choose the local provider and enter the model identifier and the backend’s base URL. The exact values depend on the runtime and model, so use the current backend-specific instructions in the OpenHands local LLM guide. Its LM Studio example uses a local API endpoint and the placeholder API key local-llm for a local endpoint that does not require authentication.
Networking needs attention when OpenHands runs in Docker but the model server runs directly on the host. The OpenHands guide uses host.docker.internal in its connectivity check and notes that Linux users may need to enable LM Studio’s “Serve on Local Network” setting. For Ollama, follow the guide’s model-specific configuration, including its context-length and host-binding guidance.
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4. Set an appropriate context length for the documented Ollama example
For its Qwen3.6-35B-A3B Ollama example, OpenHands says to set a context length of at least 22,000 tokens and recommends 32,768 where hardware allows. It warns that Ollama’s 4,096-token default is too small for that setup’s system prompt and tools. These numbers describe this documented model-and-agent configuration; they are not general requirements for every runtime or model.
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5. Try a small, reversible task
Start in a disposable project or a copy of a project. Ask for a bounded change, then inspect the files and confirm that the agent used tools as expected before giving it access to important work. OpenHands warns that a local model may act like a chatbot, refuse tool or file operations, or repeatedly fail tool calls. A successful connection proves the components can communicate; it does not prove that the model will reliably operate as an agent.
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What hardware does a local AI agent need?
There is no single memory requirement for all local agents. Separate the agent application’s needs from the model server’s needs, then check the specific model’s memory and context guidance. OpenHands states that effective local-model agent use requires capable hardware and models tuned for instruction following and agent-style behavior. Its setup recommendation of 4GB RAM concerns OpenHands, while the much larger figures below are tied to a named model example.
| Documented OpenHands example | Requirement or guidance |
|---|---|
| OpenHands application setup | At least 4GB RAM recommended for the OpenHands system setup; this is not a local model requirement. |
| Quantized Qwen3.6-35B-A3B | At least 24GB GPU VRAM, or at least 64GB Apple Silicon unified memory. |
| Qwen3.6-35B-A3B with Ollama | At least 22,000 tokens of context; 32,768 recommended where hardware permits. |
| Ollama default in that example | 4,096 tokens, described by OpenHands as too small for that setup’s system prompt and tools. |
These are OpenHands documentation recommendations for the stated application and example, not independent performance benchmarks. Smaller models or different quantizations may have different needs. Choosing a model that loads is only part of the decision: it must also follow instructions and make tool calls reliably enough for your task.
OpenHands or Open Interpreter?
Both offer ways to use a local model, but their interfaces differ. OpenHands provides a serve-and-UI workflow; Open Interpreter is a terminal coding-agent workflow. OpenHands documents LM Studio, Ollama, vLLM, and SGLang backends, while Open Interpreter’s quickstart describes connecting to Ollama or LM Studio.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problems| Consideration | OpenHands | Open Interpreter |
|---|---|---|
| Interface | Serve command and UI. | Interactive terminal session, started with i or interpreter. |
| Documented local model options | LM Studio, Ollama, vLLM, and SGLang. | Ollama or LM Studio in the quickstart. |
| Host setup | Documents macOS with Docker Desktop, Linux, and Windows with WSL and Docker Desktop. | Quickstart documents installer paths for macOS/Linux and Windows PowerShell; check its current setup instructions for the requirements of your system. |
For the Open Interpreter route, follow its official quickstart for the current installer. Its first-run flow prompts for provider setup, and the quickstart says its default local workflow works in the current workspace and asks before actions requiring more access. Check the permissions and access behavior in either application before using it on valuable files.
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