---
summary: "Fixes for common Ollama failures in OpenClaw"
read_when:
  - Ollama is not detected, or no models are available
  - You hit connection refused, garbled output, or timeouts
  - A WSL2 setup reboots repeatedly
title: "Ollama troubleshooting"
sidebarTitle: "Troubleshooting"
---

## Troubleshooting

<AccordionGroup>
  <Accordion title="WSL2 crash loop (repeated reboots)">
    On WSL2 with NVIDIA/CUDA, the official Ollama Linux installer creates an
    `ollama.service` systemd unit with `Restart=always`. If that service
    autostarts and loads a GPU-backed model during WSL2 boot, Ollama can pin
    host memory while loading; Hyper-V memory reclaim cannot always reclaim
    those pages, so Windows can terminate the WSL2 VM, systemd restarts
    Ollama, and the loop repeats.

    Evidence: repeated WSL2 reboots/terminations, high CPU in `app.slice` or
    `ollama.service` right after WSL2 startup, and SIGTERM from systemd rather
    than the Linux OOM killer.

    OpenClaw logs a startup warning when it detects WSL2, `ollama.service`
    enabled with `Restart=always`, and visible CUDA markers.

    Mitigation:

    ```bash
    sudo systemctl disable ollama
    ```

    On the Windows side, add this to `%USERPROFILE%\.wslconfig`, then run
    `wsl --shutdown`:

    ```ini
    [experimental]
    autoMemoryReclaim=disabled
    ```

    Or shorten keep-alive / start Ollama manually only when needed:

    ```bash
    export OLLAMA_KEEP_ALIVE=5m
    ollama serve
    ```

    See [ollama/ollama#11317](https://github.com/ollama/ollama/issues/11317).

  </Accordion>

  <Accordion title="Ollama not detected">
    Confirm Ollama is running and is in the agent's model scope. For ambient
    localhost discovery, set `OLLAMA_API_KEY` (or an auth profile). A nonempty
    manual model list skips discovery; an explicit self-hosted endpoint with
    `models: []` does not:

    ```bash
    ollama serve
    curl http://localhost:11434/api/tags
    ```

  </Accordion>

  <Accordion title="No models available">
    Pull the model locally, or define it explicitly in
    `models.providers.ollama`:

    ```bash
    ollama list  # See what's installed
    ollama pull gemma4
    ollama pull gpt-oss:20b
    ollama pull llama3.3     # Or another model
    ```

  </Accordion>

  <Accordion title="Connection refused">
    ```bash
    # Check if Ollama is running
    ps aux | grep ollama

    # Or restart Ollama
    ollama serve
    ```

  </Accordion>

  <Accordion title="Remote host works with curl but not OpenClaw">
    Verify from the same machine and runtime that runs the Gateway:

    ```bash
    openclaw gateway status --deep
    curl http://ollama-host:11434/api/tags
    ```

    Common causes:

    - `baseUrl` points at `localhost`, but the Gateway runs in Docker or on another host.
    - The URL uses `/v1`, selecting OpenAI-compatible behavior instead of native Ollama.
    - The remote host needs firewall or LAN binding changes.
    - The model is on your laptop's daemon but not the remote one.

  </Accordion>

  <Accordion title="Model outputs tool JSON as text">
    Usually the provider is in OpenAI-compatible mode, or the model cannot
    handle tool schemas. Prefer native mode:

    ```json5
    {
      models: {
        providers: {
          ollama: {
            baseUrl: "http://ollama-host:11434",
            api: "ollama",
          },
        },
      },
    }
    ```

    If a small local model still fails on tool schemas, set
    `compat.supportsTools: false` on that model entry and retest.

  </Accordion>

  <Accordion title="Kimi or GLM returns garbled symbols">
    Hosted Kimi/GLM responses that are long, non-linguistic symbol runs are
    treated as a failed provider call rather than a successful reply, so
    normal retry/fallback/error handling takes over instead of persisting
    corrupted text into the session.

    If it recurs, capture the model name, the current session file, and
    whether the run used `Cloud + Local` or `Cloud only`, then try a fresh
    session and a fallback model:

    ```bash
    openclaw infer model run --model ollama/kimi-k2.5:cloud --prompt "Reply with exactly: ok" --json
    openclaw models set ollama/gemma4
    ```

  </Accordion>

  <Accordion title="Cold local model times out">
    Large local models can need a long first load. Scope the timeout to the
    Ollama provider and optionally keep the model loaded between turns:

    ```json5
    {
      models: {
        providers: {
          ollama: {
            timeoutSeconds: 300,
            models: [
              {
                id: "gemma4:26b",
                name: "gemma4:26b",
                params: { keep_alive: "15m" },
              },
            ],
          },
        },
      },
    }
    ```

    If the host itself is slow to accept connections, `timeoutSeconds` also
    extends the guarded connect timeout for this provider.

  </Accordion>

  <Accordion title="Large-context model is too slow or runs out of memory">
    Many models advertise contexts larger than your hardware can run
    comfortably. Native requests forward the effective `contextTokens` unless
    `params.num_ctx` overrides it. Cap both OpenClaw's budget and Ollama's request
    context for predictable first-token latency:

    ```json5
    {
      models: {
        providers: {
          ollama: {
            maxTokens: 8192,
            models: [
              {
                id: "qwen3.5:9b",
                name: "qwen3.5:9b",
                contextTokens: 32768,
                params: { num_ctx: 32768, thinking: false },
              },
            ],
          },
        },
      },
    }
    ```

    Lower the model entry's `contextTokens` if OpenClaw sends too much prompt. Lower
    `params.num_ctx` if Ollama's runtime context is too large for the machine.
    Lower `maxTokens` if generation runs too long.

  </Accordion>
</AccordionGroup>

<Note>
More help: [Troubleshooting](/help/troubleshooting) and [FAQ](/help/faq).
</Note>