---
summary: "Run OpenClaw with Ollama (cloud and local models)"
read_when:
- You want to run OpenClaw with cloud or local models via Ollama
- You need Ollama setup and configuration guidance
- You want Ollama vision models for image understanding
title: "Ollama"
---
OpenClaw integrates with Ollama's native API (`/api/chat`) for hosted cloud models and local/self-hosted Ollama servers. You can use Ollama in three modes: `Cloud + Local` through a reachable Ollama host, `Cloud only` against `https://ollama.com`, or `Local only` against a reachable Ollama host.
**Remote Ollama users**: Do not use the `/v1` OpenAI-compatible URL (`http://host:11434/v1`) with OpenClaw. This breaks tool calling and models may output raw tool JSON as plain text. Use the native Ollama API URL instead: `baseUrl: "http://host:11434"` (no `/v1`).
Ollama provider config uses `baseUrl` as the canonical key. OpenClaw also accepts `baseURL` for compatibility with OpenAI SDK-style examples, but new config should prefer `baseUrl`.
## Auth rules
Local and LAN Ollama hosts do not need a real bearer token. OpenClaw uses the local `ollama-local` marker only for loopback, private-network, `.local`, and bare-hostname Ollama base URLs.
Remote public hosts and Ollama Cloud (`https://ollama.com`) require a real credential through `OLLAMA_API_KEY`, an auth profile, or the provider's `apiKey`.
Custom provider ids that set `api: "ollama"` follow the same rules. For example, an `ollama-remote` provider that points at a private LAN Ollama host can use `apiKey: "ollama-local"` and sub-agents will resolve that marker through the Ollama provider hook instead of treating it as a missing credential.
When Ollama is used for memory embeddings, bearer auth is scoped to the host where it was declared:
- A provider-level key is sent only to that provider's Ollama host.
- `agents.*.memorySearch.remote.apiKey` is sent only to its remote embedding host.
- A pure `OLLAMA_API_KEY` env value is treated as the Ollama Cloud convention, not sent to local or self-hosted hosts by default.
## Getting started
Choose your preferred setup method and mode.
**Best for:** fastest path to a working Ollama cloud or local setup.
```bash
openclaw onboard
```
Select **Ollama** from the provider list.
- **Cloud + Local** — local Ollama host plus cloud models routed through that host
- **Cloud only** — hosted Ollama models via `https://ollama.com`
- **Local only** — local models only
`Cloud only` prompts for `OLLAMA_API_KEY` and suggests hosted cloud defaults. `Cloud + Local` and `Local only` ask for an Ollama base URL, discover available models, and auto-pull the selected local model if it is not available yet. When Ollama reports an installed `:latest` tag such as `gemma4:latest`, setup shows that installed model once instead of showing both `gemma4` and `gemma4:latest` or pulling the bare alias again. `Cloud + Local` also checks whether that Ollama host is signed in for cloud access.
```bash
openclaw models list --provider ollama
```
### Non-interactive mode
```bash
openclaw onboard --non-interactive \
--auth-choice ollama \
--accept-risk
```
Optionally specify a custom base URL or model:
```bash
openclaw onboard --non-interactive \
--auth-choice ollama \
--custom-base-url "http://ollama-host:11434" \
--custom-model-id "qwen3.5:27b" \
--accept-risk
```
**Best for:** full control over cloud or local setup.
- **Cloud + Local**: install Ollama, sign in with `ollama signin`, and route cloud requests through that host
- **Cloud only**: use `https://ollama.com` with an `OLLAMA_API_KEY`
- **Local only**: install Ollama from [ollama.com/download](https://ollama.com/download)
```bash
ollama pull gemma4
# or
ollama pull gpt-oss:20b
# or
ollama pull llama3.3
```
For `Cloud only`, use your real `OLLAMA_API_KEY`. For host-backed setups, any placeholder value works:
```bash
# Cloud
export OLLAMA_API_KEY="your-ollama-api-key"
# Local-only
export OLLAMA_API_KEY="ollama-local"
# Or configure in your config file
openclaw config set models.providers.ollama.apiKey "OLLAMA_API_KEY"
```
```bash
openclaw models list
openclaw models set ollama/gemma4
```
Or set the default in config:
```json5
{
agents: {
defaults: {
model: { primary: "ollama/gemma4" },
},
},
}
```
## Cloud models
`Cloud + Local` uses a reachable Ollama host as the control point for both local and cloud models. This is Ollama's preferred hybrid flow.
Use **Cloud + Local** during setup. OpenClaw prompts for the Ollama base URL, discovers local models from that host, and checks whether the host is signed in for cloud access with `ollama signin`. When the host is signed in, OpenClaw also suggests hosted cloud defaults such as `kimi-k2.5:cloud`, `minimax-m2.7:cloud`, and `glm-5.1:cloud`.
If the host is not signed in yet, OpenClaw keeps the setup local-only until you run `ollama signin`.
`Cloud only` runs against Ollama's hosted API at `https://ollama.com`.
Use **Cloud only** during setup. OpenClaw prompts for `OLLAMA_API_KEY`, sets `baseUrl: "https://ollama.com"`, and seeds the hosted cloud model list. This path does **not** require a local Ollama server or `ollama signin`.
The cloud model list shown during `openclaw onboard` is populated live from `https://ollama.com/api/tags`, capped at 500 entries, so the picker reflects the current hosted catalog rather than a static seed. If `ollama.com` is unreachable or returns no models at setup time, OpenClaw falls back to the previous hardcoded suggestions so onboarding still completes.
In local-only mode, OpenClaw discovers models from the configured Ollama instance. This path is for local or self-hosted Ollama servers.
OpenClaw currently suggests `gemma4` as the local default.
## Model discovery (implicit provider)
When you set `OLLAMA_API_KEY` (or an auth profile) and **do not** define `models.providers.ollama` or another custom remote provider with `api: "ollama"`, OpenClaw discovers models from the local Ollama instance at `http://127.0.0.1:11434`.
| Behavior | Detail |
| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Catalog query | Queries `/api/tags` |
| Capability detection | Uses best-effort `/api/show` lookups to read `contextWindow`, expanded `num_ctx` Modelfile parameters, and capabilities including vision/tools |
| Vision models | Models with a `vision` capability reported by `/api/show` are marked as image-capable (`input: ["text", "image"]`), so OpenClaw auto-injects images into the prompt |
| Reasoning detection | Marks `reasoning` with a model-name heuristic (`r1`, `reasoning`, `think`) |
| Token limits | Sets `maxTokens` to the default Ollama max-token cap used by OpenClaw |
| Costs | Sets all costs to `0` |
This avoids manual model entries while keeping the catalog aligned with the local Ollama instance.
```bash
# See what models are available
ollama list
openclaw models list
```
To add a new model, simply pull it with Ollama:
```bash
ollama pull mistral
```
The new model will be automatically discovered and available to use.
If you set `models.providers.ollama` explicitly, or configure a custom remote provider such as `models.providers.ollama-cloud` with `api: "ollama"`, auto-discovery is skipped and you must define models manually. Loopback custom providers such as `http://127.0.0.2:11434` are still treated as local. See the explicit config section below.
## Vision and image description
The bundled Ollama plugin registers Ollama as an image-capable media-understanding provider. This lets OpenClaw route explicit image-description requests and configured image-model defaults through local or hosted Ollama vision models.
For local vision, pull a model that supports images:
```bash
ollama pull qwen2.5vl:7b
export OLLAMA_API_KEY="ollama-local"
```
Then verify with the infer CLI:
```bash
openclaw infer image describe \
--file ./photo.jpg \
--model ollama/qwen2.5vl:7b \
--json
```
`--model` must be a full `` ref. When it is set, `openclaw infer image describe` runs that model directly instead of skipping description because the model supports native vision.
To make Ollama the default image-understanding model for inbound media, configure `agents.defaults.imageModel`:
```json5
{
agents: {
defaults: {
imageModel: {
primary: "ollama/qwen2.5vl:7b",
},
},
},
}
```
Slow local vision models can need a longer image-understanding timeout than cloud models. They can also crash or stop when Ollama tries to allocate the full advertised vision context on constrained hardware. Set a capability timeout, and cap `num_ctx` on the model entry when you only need a normal image-description turn:
```json5
{
models: {
providers: {
ollama: {
models: [
{
id: "qwen2.5vl:7b",
name: "qwen2.5vl:7b",
input: ["text", "image"],
params: { num_ctx: 2048, keep_alive: "1m" },
},
],
},
},
},
tools: {
media: {
image: {
timeoutSeconds: 180,
models: [{ provider: "ollama", model: "qwen2.5vl:7b", timeoutSeconds: 300 }],
},
},
},
}
```
This timeout applies to inbound image understanding and to the explicit `image` tool the agent can call during a turn. Provider-level `models.providers.ollama.timeoutSeconds` still controls the underlying Ollama HTTP request guard for normal model calls.
Live-verify the explicit image tool against local Ollama with:
```bash
OPENCLAW_LIVE_TEST=1 OPENCLAW_LIVE_OLLAMA_IMAGE=1 \
pnpm test:live -- src/agents/tools/image-tool.ollama.live.test.ts
```
If you define `models.providers.ollama.models` manually, mark vision models with image input support:
```json5
{
id: "qwen2.5vl:7b",
name: "qwen2.5vl:7b",
input: ["text", "image"],
contextWindow: 128000,
maxTokens: 8192,
}
```
OpenClaw rejects image-description requests for models that are not marked image-capable. With implicit discovery, OpenClaw reads this from Ollama when `/api/show` reports a vision capability.
## Configuration
The simplest local-only enablement path is via environment variable:
```bash
export OLLAMA_API_KEY="ollama-local"
```
If `OLLAMA_API_KEY` is set, you can omit `apiKey` in the provider entry and OpenClaw will fill it for availability checks.
Use explicit config when you want hosted cloud setup, Ollama runs on another host/port, you want to force specific context windows or model lists, or you want fully manual model definitions.
```json5
{
models: {
providers: {
ollama: {
baseUrl: "https://ollama.com",
apiKey: "OLLAMA_API_KEY",
api: "ollama",
models: [
{
id: "kimi-k2.5:cloud",
name: "kimi-k2.5:cloud",
reasoning: false,
input: ["text", "image"],
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
contextWindow: 128000,
maxTokens: 8192
}
]
}
}
}
}
```
If Ollama is running on a different host or port (explicit config disables auto-discovery, so define models manually):
```json5
{
models: {
providers: {
ollama: {
apiKey: "ollama-local",
baseUrl: "http://ollama-host:11434", // No /v1 - use native Ollama API URL
api: "ollama", // Set explicitly to guarantee native tool-calling behavior
timeoutSeconds: 300, // Optional: give cold local models longer to connect and stream
models: [
{
id: "qwen3:32b",
name: "qwen3:32b",
params: {
keep_alive: "15m", // Optional: keep the model loaded between turns
},
},
],
},
},
},
}
```
Do not add `/v1` to the URL. The `/v1` path uses OpenAI-compatible mode, where tool calling is not reliable. Use the base Ollama URL without a path suffix.
## Common recipes
Use these as starting points and replace model IDs with the exact names from `ollama list` or `openclaw models list --provider ollama`.
Use this when Ollama runs on the same machine as the Gateway and you want OpenClaw to discover the installed models automatically.
```bash
ollama serve
ollama pull gemma4
export OLLAMA_API_KEY="ollama-local"
openclaw models list --provider ollama
openclaw models set ollama/gemma4
```
This path keeps config minimal. Do not add a `models.providers.ollama` block unless you want to define models manually.
Use native Ollama URLs for LAN hosts. Do not add `/v1`.
```json5
{
models: {
providers: {
ollama: {
baseUrl: "http://gpu-box.local:11434",
apiKey: "ollama-local",
api: "ollama",
timeoutSeconds: 300,
contextWindow: 32768,
maxTokens: 8192,
models: [
{
id: "qwen3.5:9b",
name: "qwen3.5:9b",
reasoning: true,
input: ["text"],
params: {
num_ctx: 32768,
thinking: false,
keep_alive: "15m",
},
},
],
},
},
},
agents: {
defaults: {
model: { primary: "ollama/qwen3.5:9b" },
},
},
}
```
`contextWindow` is the OpenClaw-side context budget. `params.num_ctx` is sent to Ollama for the request. Keep them aligned when your hardware cannot run the model's full advertised context.
Use this when you do not run a local daemon and want hosted Ollama models directly.
```bash
export OLLAMA_API_KEY="your-ollama-api-key"
```
```json5
{
models: {
providers: {
ollama: {
baseUrl: "https://ollama.com",
apiKey: "OLLAMA_API_KEY",
api: "ollama",
models: [
{
id: "kimi-k2.5:cloud",
name: "kimi-k2.5:cloud",
reasoning: false,
input: ["text", "image"],
contextWindow: 128000,
maxTokens: 8192,
},
],
},
},
},
agents: {
defaults: {
model: { primary: "ollama/kimi-k2.5:cloud" },
},
},
}
```
Use this when a local or LAN Ollama daemon is signed in with `ollama signin` and should serve both local models and `:cloud` models.
```bash
ollama signin
ollama pull gemma4
```
```json5
{
models: {
providers: {
ollama: {
baseUrl: "http://127.0.0.1:11434",
apiKey: "ollama-local",
api: "ollama",
timeoutSeconds: 300,
models: [
{ id: "gemma4", name: "gemma4", input: ["text"] },
{ id: "kimi-k2.5:cloud", name: "kimi-k2.5:cloud", input: ["text", "image"] },
],
},
},
},
agents: {
defaults: {
model: {
primary: "ollama/gemma4",
fallbacks: ["ollama/kimi-k2.5:cloud"],
},
},
},
}
```
Use custom provider IDs when you have more than one Ollama server. Each provider gets its own host, models, auth, timeout, and model refs.
```json5
{
models: {
providers: {
"ollama-fast": {
baseUrl: "http://mini.local:11434",
apiKey: "ollama-local",
api: "ollama",
contextWindow: 32768,
models: [{ id: "gemma4", name: "gemma4", input: ["text"] }],
},
"ollama-large": {
baseUrl: "http://gpu-box.local:11434",
apiKey: "ollama-local",
api: "ollama",
timeoutSeconds: 420,
contextWindow: 131072,
maxTokens: 16384,
models: [{ id: "qwen3.5:27b", name: "qwen3.5:27b", input: ["text"] }],
},
},
},
agents: {
defaults: {
model: {
primary: "ollama-fast/gemma4",
fallbacks: ["ollama-large/qwen3.5:27b"],
},
},
},
}
```
When OpenClaw sends the request, the active provider prefix is stripped so `ollama-large/qwen3.5:27b` reaches Ollama as `qwen3.5:27b`.
Some local models can answer simple prompts but struggle with the full agent tool surface. Start by limiting tools and context before changing global runtime settings.
```json5
{
agents: {
defaults: {
experimental: {
localModelLean: true,
},
model: { primary: "ollama/gemma4" },
},
},
models: {
providers: {
ollama: {
baseUrl: "http://127.0.0.1:11434",
apiKey: "ollama-local",
api: "ollama",
contextWindow: 32768,
models: [
{
id: "gemma4",
name: "gemma4",
input: ["text"],
params: { num_ctx: 32768 },
compat: { supportsTools: false },
},
],
},
},
},
}
```
Use `compat.supportsTools: false` only when the model or server reliably fails on tool schemas. It trades agent capability for stability.
`localModelLean` removes the browser, cron, and message tools from the agent surface, but it does not change Ollama's runtime context or thinking mode. Pair it with explicit `params.num_ctx` and `params.thinking: false` for small Qwen-style thinking models that loop or spend their response budget on hidden reasoning.
### Model selection
Once configured, all your Ollama models are available:
```json5
{
agents: {
defaults: {
model: {
primary: "ollama/gpt-oss:20b",
fallbacks: ["ollama/llama3.3", "ollama/qwen2.5-coder:32b"],
},
},
},
}
```
Custom Ollama provider ids are also supported. When a model ref uses the active
provider prefix, such as `ollama-spark/qwen3:32b`, OpenClaw strips only that
prefix before calling Ollama so the server receives `qwen3:32b`.
For slow local models, prefer provider-scoped request tuning before raising the
whole agent runtime timeout:
```json5
{
models: {
providers: {
ollama: {
timeoutSeconds: 300,
models: [
{
id: "gemma4:26b",
name: "gemma4:26b",
params: { keep_alive: "15m" },
},
],
},
},
},
}
```
`timeoutSeconds` applies to the model HTTP request, including connection setup,
headers, body streaming, and the total guarded-fetch abort. `params.keep_alive`
is forwarded to Ollama as top-level `keep_alive` on native `/api/chat` requests;
set it per model when first-turn load time is the bottleneck.
### Quick verification
```bash
# Ollama daemon visible to this machine
curl http://127.0.0.1:11434/api/tags
# OpenClaw catalog and selected model
openclaw models list --provider ollama
openclaw models status
# Direct model smoke
openclaw infer model run \
--model ollama/gemma4 \
--prompt "Reply with exactly: ok"
```
For remote hosts, replace `127.0.0.1` with the host used in `baseUrl`. If `curl` works but OpenClaw does not, check whether the Gateway runs on a different machine, container, or service account.
## Ollama Web Search
OpenClaw supports **Ollama Web Search** as a bundled `web_search` provider.
| Property | Detail |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Host | Uses your configured Ollama host (`models.providers.ollama.baseUrl` when set, otherwise `http://127.0.0.1:11434`); `https://ollama.com` uses the hosted API directly |
| Auth | Key-free for signed-in local Ollama hosts; `OLLAMA_API_KEY` or configured provider auth for direct `https://ollama.com` search or auth-protected hosts |
| Requirement | Local/self-hosted hosts must be running and signed in with `ollama signin`; direct hosted search requires `baseUrl: "https://ollama.com"` plus a real Ollama API key |
Choose **Ollama Web Search** during `openclaw onboard` or `openclaw configure --section web`, or set:
```json5
{
tools: {
web: {
search: {
provider: "ollama",
},
},
},
}
```
For direct hosted search through Ollama Cloud:
```json5
{
models: {
providers: {
ollama: {
baseUrl: "https://ollama.com",
apiKey: "OLLAMA_API_KEY",
api: "ollama",
models: [{ id: "kimi-k2.5:cloud", name: "kimi-k2.5:cloud", input: ["text"] }],
},
},
},
tools: {
web: {
search: { provider: "ollama" },
},
},
}
```
For a signed-in local daemon, OpenClaw uses the daemon's `/api/experimental/web_search` proxy. For `https://ollama.com`, it calls the hosted `/api/web_search` endpoint directly.
For the full setup and behavior details, see [Ollama Web Search](/tools/ollama-search).
## Advanced configuration
**Tool calling is not reliable in OpenAI-compatible mode.** Use this mode only if you need OpenAI format for a proxy and do not depend on native tool calling behavior.
If you need to use the OpenAI-compatible endpoint instead (for example, behind a proxy that only supports OpenAI format), set `api: "openai-completions"` explicitly:
```json5
{
models: {
providers: {
ollama: {
baseUrl: "http://ollama-host:11434/v1",
api: "openai-completions",
injectNumCtxForOpenAICompat: true, // default: true
apiKey: "ollama-local",
models: [...]
}
}
}
}
```
This mode may not support streaming and tool calling simultaneously. You may need to disable streaming with `params: { streaming: false }` in model config.
When `api: "openai-completions"` is used with Ollama, OpenClaw injects `options.num_ctx` by default so Ollama does not silently fall back to a 4096 context window. If your proxy/upstream rejects unknown `options` fields, disable this behavior:
```json5
{
models: {
providers: {
ollama: {
baseUrl: "http://ollama-host:11434/v1",
api: "openai-completions",
injectNumCtxForOpenAICompat: false,
apiKey: "ollama-local",
models: [...]
}
}
}
}
```
For auto-discovered models, OpenClaw uses the context window reported by Ollama when available, including larger `PARAMETER num_ctx` values from custom Modelfiles. Otherwise it falls back to the default Ollama context window used by OpenClaw.
You can set provider-level `contextWindow`, `contextTokens`, and `maxTokens` defaults for every model under that Ollama provider, then override them per model when needed. `contextWindow` is OpenClaw's prompt and compaction budget. Native Ollama requests leave `options.num_ctx` unset unless you explicitly configure `params.num_ctx`, so Ollama can apply its own model, `OLLAMA_CONTEXT_LENGTH`, or VRAM-based default. To cap or force Ollama's per-request runtime context without rebuilding a Modelfile, set `params.num_ctx`; invalid, zero, negative, and non-finite values are ignored. The OpenAI-compatible Ollama adapter still injects `options.num_ctx` by default from the configured `params.num_ctx` or `contextWindow`; disable that with `injectNumCtxForOpenAICompat: false` if your upstream rejects `options`.
Native Ollama model entries also accept the common Ollama runtime options under `params`, including `temperature`, `top_p`, `top_k`, `min_p`, `num_predict`, `stop`, `repeat_penalty`, `num_batch`, `num_thread`, and `use_mmap`. OpenClaw forwards only Ollama request keys, so OpenClaw runtime params such as `streaming` are not leaked to Ollama. Use `params.think` or `params.thinking` to send top-level Ollama `think`; `false` disables API-level thinking for Qwen-style thinking models.
```json5
{
models: {
providers: {
ollama: {
contextWindow: 32768,
models: [
{
id: "llama3.3",
contextWindow: 131072,
maxTokens: 65536,
params: {
num_ctx: 32768,
temperature: 0.7,
top_p: 0.9,
thinking: false,
},
}
]
}
}
}
}
```
Per-model `agents.defaults.models["ollama/"].params.num_ctx` works too. If both are configured, the explicit provider model entry wins over the agent default.
For native Ollama models, OpenClaw forwards thinking control as Ollama expects it: top-level `think`, not `options.think`.
```bash
openclaw agent --model ollama/gemma4 --thinking off
openclaw agent --model ollama/gemma4 --thinking low
```
You can also set a model default:
```json5
{
agents: {
defaults: {
models: {
"ollama/gemma4": {
thinking: "low",
},
},
},
},
}
```
Per-model `params.think` or `params.thinking` can disable or force Ollama API thinking for a specific configured model. Runtime commands such as `/think off` still apply to the active run.
OpenClaw treats models with names such as `deepseek-r1`, `reasoning`, or `think` as reasoning-capable by default.
```bash
ollama pull deepseek-r1:32b
```
No additional configuration is needed. OpenClaw marks them automatically.
Ollama is free and runs locally, so all model costs are set to $0. This applies to both auto-discovered and manually defined models.
The bundled Ollama plugin registers a memory embedding provider for
[memory search](/concepts/memory). It uses the configured Ollama base URL
and API key, calls Ollama's current `/api/embed` endpoint, and batches
multiple memory chunks into one `input` request when possible.
| Property | Value |
| ------------- | ------------------- |
| Default model | `nomic-embed-text` |
| Auto-pull | Yes — the embedding model is pulled automatically if not present locally |
Query-time embeddings use retrieval prefixes for models that require or recommend them, including `nomic-embed-text`, `qwen3-embedding`, and `mxbai-embed-large`. Memory document batches stay raw so existing indexes do not need a format migration.
To select Ollama as the memory search embedding provider:
```json5
{
agents: {
defaults: {
memorySearch: { provider: "ollama" },
},
},
}
```
For a remote embedding host, keep auth scoped to that host:
```json5
{
agents: {
defaults: {
memorySearch: {
provider: "ollama",
remote: {
baseUrl: "http://gpu-box.local:11434",
model: "nomic-embed-text",
apiKey: "ollama-local",
},
},
},
},
}
```
OpenClaw's Ollama integration uses the **native Ollama API** (`/api/chat`) by default, which fully supports streaming and tool calling simultaneously. No special configuration is needed.
For native `/api/chat` requests, OpenClaw also forwards thinking control directly to Ollama: `/think off` and `openclaw agent --thinking off` send top-level `think: false`, while `/think low|medium|high` send the matching top-level `think` effort string. `/think max` maps to Ollama's highest native effort, `think: "high"`.
If you need to use the OpenAI-compatible endpoint, see the "Legacy OpenAI-compatible mode" section above. Streaming and tool calling may not work simultaneously in that mode.
## Troubleshooting
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 the model loads. Hyper-V memory reclaim cannot always reclaim those pinned pages, so Windows can terminate the WSL2 VM, systemd starts Ollama again, and the loop repeats.
Common evidence:
- repeated WSL2 reboots or terminations from the Windows side
- high CPU in `app.slice` or `ollama.service` shortly after WSL2 startup
- SIGTERM from systemd rather than a Linux OOM-killer event
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
```
Add this to `%USERPROFILE%\.wslconfig` on the Windows side, then run `wsl --shutdown`:
```ini
[experimental]
autoMemoryReclaim=disabled
```
Set a shorter keep-alive in the Ollama service environment, or start Ollama manually only when you need it:
```bash
export OLLAMA_KEEP_ALIVE=5m
ollama serve
```
See [ollama/ollama#11317](https://github.com/ollama/ollama/issues/11317).
Make sure Ollama is running and that you set `OLLAMA_API_KEY` (or an auth profile), and that you did **not** define an explicit `models.providers.ollama` entry:
```bash
ollama serve
```
Verify that the API is accessible:
```bash
curl http://localhost:11434/api/tags
```
If your model is not listed, either 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
```
Check that Ollama is running on the correct port:
```bash
# Check if Ollama is running
ps aux | grep ollama
# Or restart Ollama
ollama serve
```
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`, which selects OpenAI-compatible behavior instead of native Ollama.
- The remote host needs firewall or LAN binding changes on the Ollama side.
- The model is present on your laptop's daemon but not on the remote daemon.
This usually means the provider is using OpenAI-compatible mode or the model cannot handle tool schemas.
Prefer native Ollama 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.
Large local models can need a long first load before streaming begins. Keep the timeout scoped to the Ollama provider, and optionally ask Ollama to 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 Undici connect timeout for this provider.
Many Ollama models advertise contexts that are larger than your hardware can run comfortably. Native Ollama uses Ollama's own runtime context default unless you set `params.num_ctx`. Cap both OpenClaw's budget and Ollama's request context when you want predictable first-token latency:
```json5
{
models: {
providers: {
ollama: {
contextWindow: 32768,
maxTokens: 8192,
models: [
{
id: "qwen3.5:9b",
name: "qwen3.5:9b",
params: { num_ctx: 32768, thinking: false },
},
],
},
},
},
}
```
Lower `contextWindow` first if OpenClaw is sending too much prompt. Lower `params.num_ctx` if Ollama is loading a runtime context that is too large for the machine. Lower `maxTokens` if generation runs too long.
More help: [Troubleshooting](/help/troubleshooting) and [FAQ](/help/faq).
## Related
Overview of all providers, model refs, and failover behavior.
How to choose and configure models.
Full setup and behavior details for Ollama-powered web search.
Full config reference.