How to run mini-me on Ollama

This guide shows how to point mm at Ollama through its OpenAI-compatible endpoint. It assumes Ollama is installed and you know how to build mm.

Steps

Pull the model. Ollama's OpenAI API does not pull a missing model: a question to it fails with model '…' not found:
```
ollama pull qwen3.5:2b
```
Make sure the server runs on localhost:11434 (the desktop app starts it; otherwise ollama serve).
Run the demo configuration from its directory (its skillsDir is ../skills):
```
cd demos/ollama
../../mm agent.ollama.yaml
```
Check the banner. provider: ollama confirms the provider. ctx: 32768 (/api/ps) means the model was already loaded and mm read the window it is served with; ctx: unknown means it was not loaded yet — mm asks again before the next question.

Variants

Another model. Set model: to the name exactly as ollama list spells it (qwen3.5:9b, …).
From a Docker sandbox. Nothing to change: when localhost:11434 does not answer, mm falls back to http://host.docker.internal:11434/v1, the Ollama on the host.
A larger window. The OpenAI API cannot set num_ctx per request: start Ollama with OLLAMA_CONTEXT_LENGTH, and put the same value in contextWindow if you want compression to know it from the first question.
ollama.com's API. Set baseUrl to its OpenAI endpoint and export the key in OLLAMA_API_KEY (or name another variable under apiKeyEnv).
Keep your own agent.yaml. AGENT_PROVIDER=ollama ./mm works as long as the file leaves baseUrl empty.

What differs from the llama.cpp servers

No Reading the prompt … line: Ollama sends no progress while it reads, so firstTokenTimeout has to cover the whole prompt and the model's loading.
The summary of a compaction turns reasoning off with reasoning_effort: "none" — Ollama ignores chat_template_kwargs.

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# How to run mini-me on Ollama

This guide shows how to point `mm` at [Ollama](https://ollama.com) through its OpenAI-compatible endpoint. It assumes Ollama is installed and you know how to build `mm`.

## Steps

1. Pull the model. Ollama's OpenAI API does not pull a missing model: a question to it fails with `model '…' not found`:

   ```bash
   ollama pull qwen3.5:2b
   ```

2. Make sure the server runs on `localhost:11434` (the desktop app starts it; otherwise `ollama serve`).

3. Run the demo configuration from its directory (its `skillsDir` is `../skills`):

   ```bash
   cd demos/ollama
   ../../mm agent.ollama.yaml
   ```

4. Check the banner. `provider: ollama` confirms the provider. `ctx: 32768 (/api/ps)` means the model was already loaded and `mm` read the window it is served with; `ctx: unknown` means it was not loaded yet — `mm` asks again before the next question.

## Variants

- **Another model.** Set `model:` to the name exactly as `ollama list` spells it (`qwen3.5:9b`, …).
- **From a Docker sandbox.** Nothing to change: when `localhost:11434` does not answer, `mm` falls back to `http://host.docker.internal:11434/v1`, the Ollama on the host.
- **A larger window.** The OpenAI API cannot set `num_ctx` per request: start Ollama with `OLLAMA_CONTEXT_LENGTH`, and put the same value in `contextWindow` if you want compression to know it from the first question.
- **ollama.com's API.** Set `baseUrl` to its OpenAI endpoint and export the key in `OLLAMA_API_KEY` (or name another variable under `apiKeyEnv`).
- **Keep your own `agent.yaml`.** `AGENT_PROVIDER=ollama ./mm` works as long as the file leaves `baseUrl` empty.

## What differs from the llama.cpp servers

- No `Reading the prompt …` line: Ollama sends no progress while it reads, so `firstTokenTimeout` has to cover the whole prompt and the model's loading.
- The summary of a compaction turns reasoning off with `reasoning_effort: "none"` — Ollama ignores `chat_template_kwargs`.

## See also

- [Providers: one implementation per protocol](../explanation/providers.md) — what was measured on Ollama, and why each server keeps its own entry.
- [Configuration reference](../reference/configuration.md) — provider defaults.