--- summary: "Context windows, thinking control, costs, embeddings, and streaming for Ollama" read_when: - You are tuning context windows, num_ctx, or thinking control - You need Ollama memory embeddings or streaming configuration - You are using the legacy OpenAI-compatible mode title: "Ollama advanced configuration" sidebarTitle: "Advanced" --- ## Advanced configuration **Tool calling is not reliable in this mode.** Use it only when a proxy needs OpenAI format and you do not depend on native tool calling. Set `api: "openai-completions"` explicitly for a proxy behind `/v1/chat/completions`: ```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 `params: { streaming: false }` on the model. OpenClaw injects `options.num_ctx` by default in this mode so Ollama does not silently fall back to a 4096-token context. If your proxy rejects unknown `options` fields, disable it: ```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 `/api/show` reports, including larger `PARAMETER num_ctx` values from custom Modelfiles; otherwise it falls back to OpenClaw's default Ollama context window. Per-model `contextWindow` declares native window metadata, and per-model `contextTokens` caps active input. Provider-level `maxTokens` remains an output-token default; a model entry can override it. Native `/api/chat` requests set `options.num_ctx` from a positive `params.num_ctx` first, then from the effective model `contextTokens` when present. Local discovery normally caps `contextTokens` at 32,768 (or the model's smaller native window), so OpenClaw can override a smaller Modelfile context even without an explicit `params.num_ctx`. Invalid, zero, negative, or non-finite `params.num_ctx` values are ignored. Only when neither value is available does Ollama choose its own model, Modelfile, `OLLAMA_CONTEXT_LENGTH`, or VRAM-based default; the native adapter does not fall back directly to the advertised `contextWindow`. After upgrading an older configuration, run `openclaw doctor --fix`. Doctor preserves current `contextTokens` caps without creating a stronger model or provider `num_ctx` pin; uncapped legacy native entries still migrate their older context budgets. Existing explicit `params.num_ctx` values remain authoritative, including pins an older Doctor already wrote. Review or remove an oversized existing pin to let `contextTokens` drive the request again. Use `params.num_ctx` to override the native request context explicitly. The OpenAI-compatible adapter still injects `options.num_ctx` by default from `params.num_ctx`, then the matching model entry's `contextTokens` or `contextWindow`; disable with `injectNumCtxForOpenAICompat: false` if the upstream rejects `options`. Native model entries also accept common Ollama runtime options under `params`, forwarded as native `/api/chat` `options`: `num_keep`, `seed`, `num_predict`, `top_k`, `top_p`, `min_p`, `typical_p`, `repeat_last_n`, `temperature`, `repeat_penalty`, `presence_penalty`, `frequency_penalty`, `stop`, `num_batch`, `num_gpu`, `main_gpu`, `use_mmap`, and `num_thread`. Runtime sampling controls (`temperature`, `topP`, `frequencyPenalty`, `presencePenalty`, and `seed`) override the matching model defaults, including explicit zero values. The Gateway's Chat Completions API maps `top_p`, `frequency_penalty`, and `presence_penalty` to these controls. With `temperature: 0`, OpenClaw still normalizes `top_p` to `1` for greedy sampling after applying overrides. A few keys (`format`, `keep_alive`, `truncate`, `shift`) are forwarded as top-level request fields instead of nested `options`. Local native chat requests default to `truncate: false` and `shift: false`, so supporting servers reject overflowing input instead of silently dropping history. OpenClaw then attempts compaction and retries, or reports the failure. Generation that fills the window can still produce a labeled partial reply. This behavior is verified with Ollama 0.33.3; older servers may ignore the fields. Explicit per-model values override these defaults. Hosted models and the OpenAI-compatible endpoint keep their existing behavior. OpenClaw only forwards these Ollama request keys, so runtime-only params such as `streaming` are never sent to Ollama. Use `params.think` (or `params.thinking`) to set top-level `think`; `false` disables API-level thinking for Qwen-style thinking models. ```json5 { models: { providers: { ollama: { models: [ { id: "llama3.3", contextWindow: 131072, contextTokens: 32768, maxTokens: 65536, params: { num_ctx: 32768, temperature: 0.7, top_p: 0.9, thinking: false, }, } ] } } } } ``` Per-model `agents.defaults.models["ollama/"].params.num_ctx` also works; the explicit provider model entry wins if both are set. Native local Ollama compaction summaries default to thinking off. This keeps summarization from using its default three-minute request window for reasoning; Qwen3.5 treats `low` as thinking enabled rather than a reduced thinking budget. An explicit `agents.defaults.compaction.thinkingLevel` overrides this preference. Existing per-model `params.think`/`params.thinking` settings keep their normal precedence. Hosted routes keep their compaction defaults. OpenClaw forwards thinking as Ollama expects it: top-level `think`, not `options.think`. Auto-discovered models whose `/api/show` reports a `thinking` capability expose `/think low`, `/think medium`, `/think high`, and `/think max`; non-thinking models expose only `/think off`. When replaying an assistant message, native requests retain its available reasoning in Ollama's separate `thinking` field alongside text and tool calls. This lets tool follow-ups reuse reasoning retained by the session's history policy without mixing it into visible answer text. ```bash openclaw agent --model ollama/gemma4 --thinking off openclaw agent --model ollama/gemma4 --thinking low ``` Or set a model default: ```json5 { agents: { defaults: { models: { "ollama/gemma4": { params: { thinking: "low" }, }, }, }, }, } ``` Per-model `params.think`/`params.thinking` can disable or force API thinking for a specific model. OpenClaw preserves that explicit config when the active run only has the implicit `off` default; a non-off runtime command such as `/think medium` still overrides it. A truthy thinking request is never sent to a model explicitly marked `reasoning: false`; a `think: false` request is always sent regardless. Models named `deepseek-r1`, `reasoning`, `reason`, or `think` are treated as reasoning-capable by default — no extra config needed: ```bash ollama pull deepseek-r1:32b ``` Ollama runs locally and is free, so all model costs are `0` for 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 `/api/embed`, and batches multiple memory chunks into one `input` request when possible. When `proxy.enabled=true`, embedding requests to the exact host-local loopback origin derived from the configured `baseUrl` use OpenClaw's guarded direct path instead of the managed forward proxy. The configured hostname must itself be `localhost` or a loopback IP literal — DNS names that merely resolve to loopback still use the managed proxy path. LAN, tailnet, private-network, and public Ollama hosts always stay on the managed proxy path, and redirects to another host/port do not inherit trust. `proxy.loopbackMode: "proxy"` routes loopback traffic through the proxy anyway; `proxy.loopbackMode: "block"` denies it before connecting — see [Managed proxy](/security/network-proxy#gateway-loopback-mode). | Property | Value | | --- | --- | | Default model | `nomic-embed-text` | | Auto-pull | Yes, if not present locally | | Embedding concurrency | Provider-owned; no memory-search tuning key is required | Query-time embeddings use retrieval prefixes for models that require or recommend them: `nomic-embed-text`, `qwen3-embedding`, and `mxbai-embed-large`. Document batches stay raw, so existing indexes need no format migration. Embedding concurrency and batching behavior are owned by the Ollama memory provider. For a remote embedding host, use the supported `remote.baseUrl` and `remote.apiKey` fields to keep auth scoped to that host: ```json5 { memory: { search: { provider: "ollama", model: "nomic-embed-text", remote: { baseUrl: "http://gpu-box.local:11434", apiKey: "ollama-local", }, }, }, } ``` Ollama uses the **native API** (`/api/chat`) by default, which supports streaming and tool calling together — no special config needed. For native requests, thinking control is forwarded directly: `/think off` and `openclaw agent --thinking off` send top-level `think: false` unless an explicit `params.think`/`params.thinking` is configured; `/think low|medium|high` send the matching effort string. Verified full-effort Ollama Cloud families such as GLM 5.2 and DeepSeek V4 also send native `think: "max"` for `/think max`; other models and local servers keep the compatible `think: "high"` mapping. For the OpenAI-compatible endpoint instead, see "Legacy OpenAI-compatible mode" above — streaming and tool calling may not work together there.