Run Claude Code Locally with Ollama: What Works

Yes, you can connect Claude Code to a model served locally by Ollama by configuring Claude Code to use an Anthropic-compatible endpoint and selecting a model Ollama supports. This is a community-supported setup rather than a guarantee that every Claude Code feature works with every local model; Claude Code itself is Anthropic software, while Ollama supplies the model.

What the local setup means

Claude Code is a coding assistant that runs in a terminal and can inspect and edit a project, run commands, and work through tasks using a model. Ollama is a local model runner: after downloading a model, it serves inference on your machine through an HTTP API. In the arrangement discussed here, Claude Code is the client and Ollama is the model endpoint.

“Run Claude Code locally” can mean two different things. You can run the Claude Code program on your computer but send requests to Anthropic’s hosted service, or you can run the program and use a locally hosted model through Ollama. Only the second arrangement keeps model inference on your machine. It does not turn an open model into Claude, nor does it mean that Anthropic’s Claude model is running locally.

How the connection works

Claude Code expects an Anthropic-style interface for its model requests. Ollama provides compatibility for Anthropic’s Messages API for supported versions and models, allowing a client that speaks that protocol to connect to Ollama’s local server. The bridge is an API-compatibility layer; it does not make the model’s capabilities or behavior identical to Claude.

Typically, Ollama listens on a local address such as http://localhost:11434. Claude Code can be directed to that endpoint using environment variables, including ANTHROPIC_BASE_URL, along with a model name understood by Ollama. Exact model identifiers, variable behavior, and compatibility can change, so check the current Claude Code and Ollama documentation before copying a setup command.

For example, the general sequence is to install both tools, download a suitable model with Ollama, start its service, and launch Claude Code with the endpoint and model configured. The model must be available locally, and the identifier passed to the client must match the name Ollama exposes. A similarly named model tag is not necessarily interchangeable.

Basic setup steps

1. Install Claude Code using Anthropic’s current installation instructions, and install Ollama using its official instructions for your operating system.
2. Choose a coding-capable model that Ollama lists as compatible with its Anthropic API support. Pull it with Ollama and allow the download to finish.
3. Start Ollama and confirm that its local service is reachable. If you use a custom port or host, use that address rather than assuming the default.
4. Set Claude Code’s model endpoint and model selection according to the current Ollama instructions. Keep any required placeholder API key local; a local endpoint may not validate it as a real cloud credential.
5. Start Claude Code in a test project and ask it to explain a small file before allowing it to make changes. Confirm that the selected model and endpoint are the ones you intended.

Environment-variable names and model-selection mechanisms are implementation details, not a promise that every release supports precisely the same configuration. Prefer commands from the current official documentation over old shell snippets from a forum. If you set variables in a terminal session, they generally apply to processes started from that session; persistent settings differ between shells and operating systems.

Choosing a model and checking requirements

Local coding performance depends on the model’s quality, its context window, and the computer’s available memory and processing capacity. Larger models and longer prompts require more resources. Quantized models can reduce resource needs, but may trade some output quality for efficiency. Ollama’s model pages usually describe relevant formats and requirements; the actual experience also depends on the project size and task.

• Check context capacity: a repository with many files may exceed what a model can use effectively in one request, even if the model can technically load.
• Check tool-use ability: coding agents need to follow structured tool instructions, not just produce plausible code. Try a low-risk task before relying on a model for a multi-step change.
• Allow for latency: local generation may be slower than a hosted service, especially on hardware with limited memory or no suitable accelerator.
• Verify compatibility: API compatibility does not ensure parity for every feature, parameter, or response format.

A model that is good at ordinary chat is not automatically good at agentic coding. Compare results on a small, representative task in your own project rather than judging by a model name alone.

Limitations and common problems

The most frequent issue is a connection error: Ollama is not running, the endpoint is wrong, or a firewall or container configuration prevents Claude Code from reaching it. If Claude Code runs inside a container or remote environment, localhost refers to that environment, not necessarily the computer running Ollama. Use a reachable address and avoid exposing the service to a network unnecessarily.

Another common problem is a model-name mismatch. Check the exact local model tag in Ollama, then ensure Claude Code is configured to request that name. If the model responds but tool calls fail, the cause may be incomplete API support or a model that does not reliably follow tool-use formats. Try a supported model and consult both projects’ current compatibility notes.

Expect differences from hosted Claude. A local model may have weaker coding ability, a smaller effective context, or different behavior around edits and commands. Some Claude Code features may depend on service-side behavior that a local endpoint does not provide. A successful launch proves only that the client can communicate; it does not establish full feature equivalence.

Privacy, costs, and safety

With inference sent to Ollama on the same computer, prompts and source files need not be sent to Anthropic’s hosted model service for that inference. However, privacy depends on your configuration and surrounding tools: extensions, shell commands, telemetry, network access, and other services can still transmit information. Review the endpoint and any integrations, and do not assume that every part of a development workflow is offline.

Local use avoids per-request charges from a hosted model for inference performed locally, but it is not cost-free. You provide the hardware, electricity, storage, and time needed to download and run the model. Claude Code access and licensing are separate questions from Ollama’s local inference; consult the applicable terms for the software and model rather than assuming that a local endpoint changes them.

Give an agent the same care you would give any tool that can read files or run commands. Start in a version-controlled project, inspect proposed changes, and be cautious with destructive commands or access to secrets. A local model can still produce incorrect code or unsafe shell instructions.

Frequently Asked Questions

Can I run Anthropic’s Claude model through Ollama?
No. Ollama runs models it supports for local inference. Connecting Claude Code to Ollama uses a local model, not Anthropic’s Claude model.

Does Claude Code work with every Ollama model?
No. Models differ in API compatibility, tool-use behavior, context capacity, and coding performance. Use a model supported by the current compatibility guidance and test it.

Do I need an Anthropic API key?
A local Ollama connection may use a placeholder credential rather than a real hosted API key, depending on the current setup instructions. Do not assume this configuration grants access to Anthropic’s hosted services.

Will my code stay on my computer?
Model requests sent to a local Ollama server can stay on the machine, but other configured services or tools may communicate over a network. Check the endpoint and integrations to understand the full data flow.

Why does Claude Code say it cannot connect?
Check that Ollama is running, the base URL and port are correct, and the model is downloaded. If the client runs remotely or in a container, make sure the endpoint is reachable from that environment.

Is a local model as capable as Claude?
Not necessarily. Results depend on the chosen model and hardware, and compatibility with the API does not imply equal reasoning, coding quality, or feature support.

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