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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.

LiteLLM is an open-source LLM gateway that provides a unified API to 100+ model providers. Route OpenClaw through LiteLLM to get centralized cost tracking, logging, and the flexibility to switch backends without changing your OpenClaw config.
Why use LiteLLM with OpenClaw?
  • Cost tracking — See exactly what OpenClaw spends across all models
  • Model routing — Switch between Claude, GPT-4, Gemini, Bedrock without config changes
  • Virtual keys — Create keys with spend limits for OpenClaw
  • Logging — Full request/response logs for debugging
  • Fallbacks — Automatic failover if your primary provider is down

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Quick start

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Configuration

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Environment variables

export LITELLM_API_KEY="sk-litellm-key"

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Config file

{
  models: {
    providers: {
      litellm: {
        baseUrl: "http://localhost:4000",
        apiKey: "${LITELLM_API_KEY}",
        api: "openai-completions",
        models: [
          {
            id: "claude-opus-4-6",
            name: "Claude Opus 4.6",
            reasoning: true,
            input: ["text", "image"],
            contextWindow: 200000,
            maxTokens: 64000,
          },
          {
            id: "gpt-4o",
            name: "GPT-4o",
            reasoning: false,
            input: ["text", "image"],
            contextWindow: 128000,
            maxTokens: 8192,
          },
        ],
      },
    },
  },
  agents: {
    defaults: {
      model: { primary: "litellm/claude-opus-4-6" },
    },
  },
}

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Advanced configuration

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Image generation

LiteLLM can also back the image_generate tool through OpenAI-compatible /images/generations and /images/edits routes. Configure a LiteLLM image model under agents.defaults.imageGenerationModel: 
{
  models: {
    providers: {
      litellm: {
        baseUrl: "http://localhost:4000",
        apiKey: "${LITELLM_API_KEY}",
      },
    },
  },
  agents: {
    defaults: {
      imageGenerationModel: {
        primary: "litellm/gpt-image-2",
        timeoutMs: 180_000,
      },
    },
  },
}
Loopback LiteLLM URLs such as http://localhost:4000 work without a global private-network override. For a LAN-hosted proxy, set models.providers.litellm.request.allowPrivateNetwork: true because the API key will be sent to the configured proxy host.
Create a dedicated key for OpenClaw with spend limits:
curl -X POST "http://localhost:4000/key/generate" \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "key_alias": "openclaw",
    "max_budget": 50.00,
    "budget_duration": "monthly"
  }'
Use the generated key as LITELLM_API_KEY.
LiteLLM can route model requests to different backends. Configure in your LiteLLM config.yaml:
model_list:
  - model_name: claude-opus-4-6
    litellm_params:
      model: claude-opus-4-6
      api_key: os.environ/ANTHROPIC_API_KEY

  - model_name: gpt-4o
    litellm_params:
      model: gpt-4o
      api_key: os.environ/OPENAI_API_KEY
OpenClaw keeps requesting claude-opus-4-6 — LiteLLM handles the routing.
Check LiteLLM’s dashboard or API:
# Key info
curl "http://localhost:4000/key/info" \
  -H "Authorization: Bearer sk-litellm-key"

# Spend logs
curl "http://localhost:4000/spend/logs" \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY"
  • LiteLLM runs on http://localhost:4000 by default
  • OpenClaw connects through LiteLLM’s proxy-style OpenAI-compatible /v1 endpoint
  • Native OpenAI-only request shaping does not apply through LiteLLM: no service_tier, no Responses store, no prompt-cache hints, and no OpenAI reasoning-compat payload shaping
  • Hidden OpenClaw attribution headers (originator, version, User-Agent) are not injected on custom LiteLLM base URLs
For general provider configuration and failover behavior, see Model Providers.

LiteLLM Docs

Official LiteLLM documentation and API reference.

Model selection

Overview of all providers, model refs, and failover behavior.

Configuration

Full config reference.

Model selection

How to choose and configure models.