openclaw-openclaw/docs/gateway/local-models.md

summary, read_when, title

summary	read_when	title
Run OpenClaw on local LLMs (LM Studio, vLLM, LiteLLM, custom OpenAI endpoints)	You want to serve models from your own GPU box You are wiring LM Studio or an OpenAI-compatible proxy You need the safest local model guidance	Local models

Local models work, but they raise the bar on hardware, context size, and prompt-injection defense: small or aggressively quantized models truncate context and skip provider-side safety filters. This page covers higher-end local stacks and custom OpenAI-compatible servers. For the lowest-friction path, start with LM Studio or Ollama and openclaw onboard.

For local servers that should start only when a selected model needs them, see Local model services.

Hardware floor

Aim for 2+ maxed-out Mac Studios or an equivalent GPU rig (~$30k+) for a comfortable agent loop. A single 24 GB GPU only handles lighter prompts at higher latency. Always run the largest / full-size variant you can host - small or heavily quantized checkpoints raise prompt-injection risk (see Security).

Pick a backend

Backend	Use when
ds4	Local DeepSeek V4 Flash on macOS Metal with OpenAI-compatible tool calls
LM Studio	First-time local setup, GUI loader, native Responses API
LiteLLM / OAI-proxy / custom OpenAI-compatible proxy	You front another model API and need OpenClaw to treat it as OpenAI
MLX / vLLM / SGLang	High-throughput self-hosted serving with an OpenAI-compatible HTTP endpoint
Ollama	CLI workflow, model library, hands-off systemd service

Use api: "openai-responses" when the backend supports it (LM Studio does). Otherwise use api: "openai-completions". If api is omitted on a custom provider with a baseUrl, OpenClaw defaults to openai-completions.

**WSL2 + Ollama + NVIDIA/CUDA:** the official Ollama Linux installer enables a systemd service with `Restart=always`. On WSL2 GPU setups, autostart can reload the last model during boot and pin host memory, causing repeated VM restarts. See [WSL2 crash loop](/providers/ollama#troubleshooting).

LM Studio + large local model (Responses API)

This is the best current local stack. Load a large model in LM Studio (a full-size Qwen, DeepSeek, or Llama build), enable the local server (default http://127.0.0.1:1234), and use the Responses API to keep reasoning separate from final text.

{
  agents: {
    defaults: {
      model: { primary: "lmstudio/my-local-model" },
      models: {
        "anthropic/claude-opus-4-6": { alias: "Opus" },
        "lmstudio/my-local-model": { alias: "Local" },
      },
    },
  },
  models: {
    mode: "merge",
    providers: {
      lmstudio: {
        baseUrl: "http://127.0.0.1:1234/v1",
        apiKey: "lmstudio",
        api: "openai-responses",
        models: [
          {
            id: "my-local-model",
            name: "Local Model",
            reasoning: false,
            input: ["text"],
            cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
            contextWindow: 196608,
            maxTokens: 8192,
          },
        ],
      },
    },
  },
}

Setup checklist:

• Install LM Studio: https://lmstudio.ai
• Download the largest available model build (avoid "small"/heavily quantized variants), start the server, confirm http://127.0.0.1:1234/v1/models lists it.
• Replace my-local-model with the actual model ID shown in LM Studio.
• Keep the model loaded; cold-load adds startup latency.
• Adjust contextWindow/maxTokens if your LM Studio build differs.
• For WhatsApp, stick to the Responses API so only final text is sent.
• Keep models.mode: "merge" so hosted models stay available as fallbacks.

Hybrid config: hosted primary, local fallback

{
  agents: {
    defaults: {
      model: {
        primary: "anthropic/claude-sonnet-4-6",
        fallbacks: ["lmstudio/my-local-model", "anthropic/claude-opus-4-6"],
      },
      models: {
        "anthropic/claude-sonnet-4-6": { alias: "Sonnet" },
        "lmstudio/my-local-model": { alias: "Local" },
        "anthropic/claude-opus-4-6": { alias: "Opus" },
      },
    },
  },
  models: {
    mode: "merge",
    providers: {
      lmstudio: {
        baseUrl: "http://127.0.0.1:1234/v1",
        apiKey: "lmstudio",
        api: "openai-responses",
        models: [
          {
            id: "my-local-model",
            name: "Local Model",
            reasoning: false,
            input: ["text"],
            cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
            contextWindow: 196608,
            maxTokens: 8192,
          },
        ],
      },
    },
  },
}

For local-first with a hosted safety net, swap primary/fallbacks order and keep the same providers block and models.mode: "merge".

Regional hosting / data routing

Hosted MiniMax/Kimi/GLM variants also exist on OpenRouter with region-pinned endpoints (for example, US-hosted). Pick the regional variant to keep traffic in your chosen jurisdiction while keeping models.mode: "merge" for Anthropic/OpenAI fallbacks. Local-only is still the strongest privacy path; hosted regional routing is the middle ground when you need provider features but want control over data flow.

Other OpenAI-compatible local proxies

MLX (mlx_lm.server), vLLM, SGLang, LiteLLM, OAI-proxy, or any custom gateway works if it exposes an OpenAI-style /v1/chat/completions endpoint. Use openai-completions unless the backend explicitly documents /v1/responses support.

{
  agents: {
    defaults: {
      model: { primary: "local/my-local-model" },
    },
  },
  models: {
    mode: "merge",
    providers: {
      local: {
        baseUrl: "http://127.0.0.1:8000/v1",
        apiKey: "sk-local",
        api: "openai-completions",
        timeoutSeconds: 300,
        models: [
          {
            id: "my-local-model",
            name: "Local Model",
            reasoning: false,
            input: ["text"],
            cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
            contextWindow: 120000,
            maxTokens: 8192,
          },
        ],
      },
    },
  },
}

Custom/local provider entries trust their exact configured baseUrl origin for guarded model requests, including loopback, LAN, tailnet, and private DNS hosts. Metadata/link-local origins are always blocked regardless. Requests to other private origins still need models.providers.<id>.request.allowPrivateNetwork: true; set the trust flag to false to opt out of exact-origin trust.

models.providers.<id>.models[].id is provider-local - do not include the provider prefix. For an MLX server started with mlx_lm.server --model mlx-community/Qwen3-30B-A3B-6bit:

• models.providers.mlx.models[].id: "mlx-community/Qwen3-30B-A3B-6bit"
• agents.defaults.model.primary: "mlx/mlx-community/Qwen3-30B-A3B-6bit"

Set input: ["text", "image"] on local or proxied vision models so image attachments get injected into agent turns. Interactive custom-provider onboarding infers common vision model IDs and only asks about unknown names; non-interactive onboarding uses the same inference, with --custom-image-input / --custom-text-input to override it.

Use models.providers.<id>.timeoutSeconds for slow local/remote model servers before raising agents.defaults.timeoutSeconds. The provider timeout covers connect, headers, body streaming, and the total guarded-fetch abort for model HTTP requests only - if the agent/run timeout is lower, raise that too, since the provider timeout cannot extend the whole run.

For custom OpenAI-compatible providers, a non-secret local marker such as `apiKey: "ollama-local"` is accepted when `baseUrl` resolves to loopback, a private LAN, `.local`, or a bare hostname - OpenClaw treats it as a valid local credential instead of reporting a missing key. Use a real value for any provider that accepts a public hostname.

Behavior notes for local/proxied /v1 backends:

• OpenClaw treats these as proxy-style OpenAI-compatible routes, not native OpenAI endpoints.
• Native-OpenAI-only request shaping does not apply: no service_tier, no Responses store, no OpenAI reasoning-compat payload shaping, no prompt-cache hints.
• Hidden OpenClaw attribution headers (originator, version, User-Agent) are not injected on custom proxy URLs.

Compat overrides for stricter OpenAI-compatible backends:

• String-only content: some servers accept only string messages[].content, not structured content-part arrays. Set models.providers.<provider>.models[].compat.requiresStringContent: true.

• Strict message keys: if the server rejects message entries with more than role/content, set compat.strictMessageKeys: true.

• Bracketed tool text: some local models emit standalone bracketed tool requests as text, like [tool_name] followed by JSON and [END_TOOL_REQUEST]. OpenClaw promotes those to real tool calls only when the name exactly matches a registered tool for the turn; otherwise it stays as hidden, unsupported text.

• Unstructured tool-call-looking text: if a model emits JSON/XML/ReAct-style text that looks like a tool call but wasn't a structured invocation, OpenClaw leaves it as text and logs a warning with the run id, provider/model, detected pattern, and tool name when available. That is provider/model incompatibility, not a completed tool run.

• Forcing tool use: if tools show up as assistant text (raw JSON/XML/ReAct, or an empty tool_calls array), first confirm the server's chat template/parser supports tool calls. If the parser only works when tool use is forced, override the default proxy value of tool_choice: "auto" per model:

  {
    agents: {
      defaults: {
        models: {
          "local/my-local-model": {
            params: {
              extra_body: {
                tool_choice: "required",
              },
            },
          },
        },
      },
    },
  }
  

  Use this only where every normal turn should call a tool. Replace local/my-local-model with the exact ref from openclaw models list, or set it via CLI:

  openclaw config set agents.defaults.models '{"local/my-local-model":{"params":{"extra_body":{"tool_choice":"required"}}}}' --strict-json --merge
  

• Extra reasoning efforts: if a custom OpenAI-compatible model accepts OpenAI reasoning efforts beyond the built-in profile, declare them in the model's compat block. Adding "xhigh" exposes it for that model ref in /think xhigh, session pickers, Gateway validation, and llm-task validation:

  {
    models: {
      providers: {
        local: {
          baseUrl: "http://127.0.0.1:8000/v1",
          apiKey: "sk-local",
          api: "openai-responses",
          models: [
            {
              id: "gpt-5.4",
              name: "GPT 5.4 via local proxy",
              reasoning: true,
              input: ["text"],
              cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
              contextWindow: 196608,
              maxTokens: 8192,
              compat: {
                supportedReasoningEfforts: ["low", "medium", "high", "xhigh"],
                reasoningEffortMap: { xhigh: "xhigh" },
              },
            },
          ],
        },
      },
    },
  }
  

Smaller or stricter backends

If the model loads cleanly but full agent turns misbehave, work top-down: confirm transport first, then narrow the surface.

1. Confirm the local model responds - no tools, no agent context:

   openclaw infer model run --local --model <provider/model> --prompt "Reply with exactly: pong" --json
   

2. Confirm Gateway routing - sends only the prompt, skipping transcript, AGENTS bootstrap, context-engine assembly, tools, and bundled MCP servers, but still exercises Gateway routing, auth, and provider selection:

   openclaw infer model run --gateway --model <provider/model> --prompt "Reply with exactly: pong" --json
   

3. Try lean mode if both probes pass but real agent turns fail with malformed tool calls or oversized prompts: set agents.defaults.experimental.localModelLean: true. It drops heavyweight browser, cron, message, media-generation, voice, and PDF tools unless explicitly required, and defaults larger tool catalogs behind structured Tool Search controls while keeping exec directly visible. See Experimental Features -> Local model lean mode for details and how to confirm it's on.

4. Disable tools entirely as a last resort by setting models.providers.<provider>.models[].compat.supportsTools: false for that model - the agent then runs without tool calls.

5. Past that, the bottleneck is upstream. If the backend still fails only on larger OpenClaw runs after lean mode and supportsTools: false, the remaining issue is usually the model or server itself - context window, GPU memory, kv-cache eviction, or a backend bug - not OpenClaw's transport layer.

Troubleshooting

• Gateway can't reach the proxy? curl http://127.0.0.1:1234/v1/models.
• LM Studio model unloaded? Reload; cold start is a common "hanging" cause.
• Local server says terminated, ECONNRESET, or closes the stream mid-turn? OpenClaw records a low-cardinality model.call.error.failureKind plus the OpenClaw process RSS/heap snapshot in diagnostics. For LM Studio/Ollama memory pressure, match that timestamp against the server log or a macOS crash/jetsam log to confirm whether the model server was killed.
• Context errors? OpenClaw derives context-window preflight thresholds from the detected model window (or the capped window when agents.defaults.contextTokens lowers it), warning below 20% with an 8k floor and hard-blocking below 10% with a 4k floor (capped to the effective context window so oversized model metadata can't reject a valid user cap). Lower contextWindow or raise the server/model context limit.
• messages[].content ... expected a string? Add compat.requiresStringContent: true on that model entry.
• validation.keys, or "message entries only allow role and content"? Add compat.strictMessageKeys: true on that model entry.
• Direct /v1/chat/completions calls work, but openclaw infer model run --local fails on Gemma or another local model? Check the provider URL, model ref, auth marker, and server logs first - model run skips agent tools entirely. If model run succeeds but larger agent turns fail, reduce the tool surface with localModelLean or compat.supportsTools: false.
• Tool calls show up as raw JSON/XML/ReAct text, or the provider returns an empty tool_calls array? Do not add a proxy that blindly converts assistant text into tool execution - fix the server's chat template/parser first. If the model only works when tool use is forced, add the params.extra_body.tool_choice: "required" override above and use that model entry only for sessions where a tool call is expected every turn.
• Safety: local models skip provider-side filters. Keep agents narrow and compaction on to limit prompt-injection blast radius.

Related

• Configuration reference
• Model failover