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Documentation Index

Fetch the complete documentation index at: https://docs.openclaw.ai/llms.txt

Use this file to discover all available pages before exploring further.

Local is doable, but OpenClaw expects large context + strong defenses against prompt injection. Small cards truncate context and leak safety. Aim high: ≥2 maxed-out Mac Studios or equivalent GPU rig (~$30k+). A single 24 GB GPU works only for lighter prompts with higher latency. Use the largest / full-size model variant you can run; aggressively quantized or “small” checkpoints raise prompt-injection risk (see Security). If you want the lowest-friction local setup, start with LM Studio or Ollama and openclaw onboard. This page is the opinionated guide for higher-end local stacks and custom OpenAI-compatible local servers.
WSL2 + Ollama + NVIDIA/CUDA users: 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. If your WSL2 VM repeatedly restarts after enabling Ollama, see WSL2 crash loop.
Best current local stack. Load a large model in LM Studio (for example, a full-size Qwen, DeepSeek, or Llama build), enable the local server (default http://127.0.0.1:1234), and use 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
  • In LM Studio, download the largest model build available (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 Responses API so only final text is sent.
Keep hosted models configured even when running local; use models.mode: "merge" so fallbacks stay available.

​
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,
          },
        ],
      },
    },
  },
}

​
Local-first with hosted safety net

Swap the primary and fallback order; keep the same providers block and models.mode: "merge" so you can fall back to Sonnet or Opus when the local box is down.

​
Regional hosting / data routing

  • Hosted MiniMax/Kimi/GLM variants also exist on OpenRouter with region-pinned endpoints (e.g., US-hosted). Pick the regional variant there to keep traffic in your chosen jurisdiction while still using models.mode: "merge" for Anthropic/OpenAI fallbacks.
  • Local-only remains 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 custom gateways work if they expose an OpenAI-style /v1/chat/completions endpoint. Use the Chat Completions adapter unless the backend explicitly documents /v1/responses support. Replace the provider block above with your endpoint and model ID: 
{
  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,
          },
        ],
      },
    },
  },
}
If api is omitted on a custom provider with a baseUrl, OpenClaw defaults to openai-completions. Loopback endpoints such as 127.0.0.1 are trusted automatically; LAN, tailnet, and private DNS endpoints still need request.allowPrivateNetwork: true. The models.providers.<id>.models[].id value is provider-local. Do not include the provider prefix there. For example, an MLX server started with mlx_lm.server --model mlx-community/Qwen3-30B-A3B-6bit should use this catalog id and model ref:
  • 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 are injected into agent turns. Interactive custom-provider onboarding infers common vision model IDs and asks only for unknown names. Non-interactive onboarding uses the same inference; use --custom-image-input for unknown vision IDs or --custom-text-input when a known-looking model is text-only behind your endpoint. Keep models.mode: "merge" so hosted models stay available as fallbacks. Use models.providers.<id>.timeoutSeconds for slow local or remote model servers before raising agents.defaults.timeoutSeconds. The provider timeout applies only to model HTTP requests, including connect, headers, body streaming, and the total guarded-fetch abort.
For custom OpenAI-compatible providers, persisting 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 note 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 here: no service_tier, no Responses store, no OpenAI reasoning-compat payload shaping, and no prompt-cache hints
  • hidden OpenClaw attribution headers (originator, version, User-Agent) are not injected on these custom proxy URLs
Compatibility notes for stricter OpenAI-compatible backends:
  • Some servers accept only string messages[].content on Chat Completions, not structured content-part arrays. Set models.providers.<provider>.models[].compat.requiresStringContent: true for those endpoints.
  • Some local models emit standalone bracketed tool requests as text, such as [tool_name] followed by JSON and [END_TOOL_REQUEST]. OpenClaw promotes those into real tool calls only when the name exactly matches a registered tool for the turn; otherwise the block is treated as unsupported text and is hidden from user-visible replies.
  • If a model emits JSON, XML, or ReAct-style text that looks like a tool call but the provider did not emit 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. Treat that as provider/model tool-call incompatibility, not a completed tool run.
  • If tools appear as assistant text instead of running, for example raw JSON, XML, ReAct syntax, or an empty tool_calls array in the provider response, first verify the server is using a tool-call-capable chat template/parser. For OpenAI-compatible Chat Completions backends whose parser works only when tool use is forced, set a per-model request override instead of relying on text parsing: 
    {
  agents: {
    defaults: {
      models: {
        "local/my-local-model": {
          params: {
            extra_body: {
              tool_choice: "required",
            },
          },
        },
      },
    },
  },
}
    Use this only for models/sessions where every normal turn should call a tool. It overrides OpenClaw’s default proxy value of tool_choice: "auto". Replace local/my-local-model with the exact provider/model ref shown by openclaw models list. 
    openclaw config set agents.defaults.models '{"local/my-local-model":{"params":{"extra_body":{"tool_choice":"required"}}}}' --strict-json --merge
  • Some smaller or stricter local backends are unstable with OpenClaw’s full agent-runtime prompt shape, especially when tool schemas are included. First verify the provider path with the lean local probe: 
    openclaw infer model run --local --model <provider/model> --prompt "Reply with exactly: pong" --json
    To verify the Gateway route without the full agent prompt shape, use the Gateway model probe instead: 
    openclaw infer model run --gateway --model <provider/model> --prompt "Reply with exactly: pong" --json
    Both local and Gateway model probes send only the supplied prompt. The Gateway probe still validates Gateway routing, auth, and provider selection, but it intentionally skips prior session transcript, AGENTS/bootstrap context, context-engine assembly, tools, and bundled MCP servers. If that succeeds but normal OpenClaw agent turns fail, first try agents.defaults.experimental.localModelLean: true to drop heavyweight default tools like browser, cron, and message; this is an experimental flag, not a stable default-mode setting. See Experimental Features. If that still fails, try models.providers.<provider>.models[].compat.supportsTools: false.
  • If the backend still fails only on larger OpenClaw runs, the remaining issue is usually upstream model/server capacity or a backend bug, not OpenClaw’s transport layer.

​
Troubleshooting

  • Gateway can 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 macOS crash / jetsam log to confirm whether the model server was killed.
  • OpenClaw warns when the detected context window is below 32k and blocks below 16k. If you hit that preflight, raise the server/model context limit or choose a larger model.
  • Context errors? Lower contextWindow or raise your server limit.
  • OpenAI-compatible server returns messages[].content ... expected a string? Add compat.requiresStringContent: true on that model entry.
  • Direct tiny /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; local model run does not include agent tools. If local model run succeeds but larger agent turns fail, reduce the agent 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 chat template/parser first. If the model only works when tool use is forced, add the per-model params.extra_body.tool_choice: "required" override above and use that model entry only for sessions where a tool call is expected on every turn.
  • Safety: local models skip provider-side filters; keep agents narrow and compaction on to limit prompt injection blast radius.