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* refactor(config): consolidate media model lists * refactor(config): unify memory configuration * refactor(config): consolidate TTS ownership * refactor(config): move typing policy to agents * refactor(config): retire product-level config surfaces * refactor(config): share scoped tool policy type * chore(config): refresh generated baselines * fix(config): honor agent typing overrides * fix(config): migrate sibling config consumers * refactor(infra): keep base64url decoder private * fix(config): strip invalid legacy TTS values * chore(config): refresh rebased baseline hash * fix(doctor): route legacy messages.tts.realtime voice to talk during tts move * refactor(config): polish final layout names * refactor(config): freeze retired tuning defaults * feat(config): add fast mode default symmetry * refactor(config): key agent entries by id * docs(config): update final layout reference * test(config): cover final layout migrations * chore(config): refresh final layout baselines * fix(config): align final layout runtime readers * fix(config): align remaining readers * fix(config): stabilize final layout migrations * fix(config): finalize config projection proof * fix(config): address final layout review * docs(release): preserve historical config names * fix(config): complete keyed agent migration * fix(config): close final migration gaps * fix(config): finish full-branch review * fix(config): complete runtime secret detection * fix(config): close final review findings * fix(config): finish canonical docs and heartbeat migration * fix(config): integrate latest main after rebase * refactor(env): isolate test-only controls * refactor(env): isolate build and development controls * refactor(env): collapse process identity indirection * refactor(env): remove duplicate config and temp aliases * docs(env): define the operator-facing allowlist * ci(env): ratchet production variable count * fix(env): remove stale provider helper import * fix(env): make ratchet sorting explicit * test(env): keep test seam in dead-code audit * test(env): cover ratchet growth and boundary; document surface budgets * docs(config): document tier-eval consolidations * docs(config): clarify speech preference ownership * test(memory): align retired tuning fixtures * refactor(memory): freeze engine heuristics * refactor(config): apply tier-eval tranche * refactor(tts): move persona shaping to providers * refactor(compaction): move prompt policy to providers * test(config): align hookified prompt fixtures * chore(deadcode): classify test-only exports * chore(github): remove unused spawn helper * chore(deadcode): classify queue diagnostics * chore(deadcode): remove unused lane snapshot export * chore(plugin-sdk): ratchet consolidated surface * fix(config): integrate latest main after rebase
205 lines
5.5 KiB
Markdown
205 lines
5.5 KiB
Markdown
---
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summary: "Run OpenClaw through LiteLLM Proxy for unified model access and cost tracking"
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title: "LiteLLM"
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read_when:
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- You want to route OpenClaw through a LiteLLM proxy
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- You need cost tracking, logging, or model routing through LiteLLM
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---
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[LiteLLM](https://litellm.ai) is an open-source LLM gateway with a unified API to 100+ model
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providers. Route OpenClaw through LiteLLM for centralized cost tracking, logging, virtual keys with
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spend limits, and backend failover without changing OpenClaw config.
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## Quick start
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<Tabs>
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<Tab title="Onboarding (recommended)">
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```bash
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openclaw onboard --auth-choice litellm-api-key
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```
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For non-interactive setup against a remote proxy, pass the proxy URL explicitly:
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```bash
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openclaw onboard --non-interactive --accept-risk --auth-choice litellm-api-key \
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--litellm-api-key "$LITELLM_API_KEY" --custom-base-url "https://litellm.example/v1"
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```
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</Tab>
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<Tab title="Manual setup">
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<Steps>
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<Step title="Start LiteLLM Proxy">
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```bash
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pip install 'litellm[proxy]'
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litellm --model claude-opus-4-6
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```
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</Step>
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<Step title="Point OpenClaw to LiteLLM">
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```bash
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export LITELLM_API_KEY="your-litellm-key"
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openclaw
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```
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</Step>
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</Steps>
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</Tab>
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</Tabs>
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## Configuration
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```json5
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{
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models: {
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providers: {
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litellm: {
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baseUrl: "http://localhost:4000",
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apiKey: "${LITELLM_API_KEY}",
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api: "openai-completions",
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models: [
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{
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id: "claude-opus-4-6",
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name: "Claude Opus 4.6",
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reasoning: true,
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input: ["text", "image"],
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contextWindow: 200000,
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maxTokens: 64000,
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},
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{
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id: "gpt-4o",
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name: "GPT-4o",
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reasoning: false,
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input: ["text", "image"],
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contextWindow: 128000,
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maxTokens: 8192,
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},
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],
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},
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},
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},
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agents: {
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defaults: {
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model: { primary: "litellm/claude-opus-4-6" },
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},
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},
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}
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```
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The default model onboarding writes is `litellm/claude-opus-4-6`.
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## Image generation
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LiteLLM can back the `image_generate` tool through OpenAI-compatible `/images/generations` and
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`/images/edits` routes. Default image model is `gpt-image-2`; configure a different one under
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`agents.defaults.mediaModels.image`:
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```json5
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{
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models: {
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providers: {
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litellm: {
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baseUrl: "http://localhost:4000",
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apiKey: "${LITELLM_API_KEY}",
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},
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},
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},
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agents: {
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defaults: {
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imageGenerationModel: {
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primary: "litellm/gpt-image-2",
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timeoutMs: 180_000,
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},
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},
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},
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}
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```
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Loopback LiteLLM URLs (`http://localhost:4000`, `127.0.0.1`, `::1`, `host.docker.internal`) work
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without a global private-network override. For a LAN-hosted proxy, set
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`models.providers.litellm.request.allowPrivateNetwork: true` because the API key is sent to that host.
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## Advanced
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<AccordionGroup>
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<Accordion title="Virtual keys">
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Create a dedicated key for OpenClaw with spend limits:
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```bash
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curl -X POST "http://localhost:4000/key/generate" \
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-H "Authorization: Bearer $LITELLM_MASTER_KEY" \
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-H "Content-Type: application/json" \
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-d '{
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"key_alias": "openclaw",
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"max_budget": 50.00,
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"budget_duration": "monthly"
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}'
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```
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Use the generated key as `LITELLM_API_KEY`.
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</Accordion>
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<Accordion title="Model routing">
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LiteLLM can route model requests to different backends. Configure in your LiteLLM `config.yaml`:
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```yaml
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model_list:
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- model_name: claude-opus-4-6
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litellm_params:
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model: claude-opus-4-6
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api_key: os.environ/ANTHROPIC_API_KEY
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- model_name: gpt-4o
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litellm_params:
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model: gpt-4o
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api_key: os.environ/OPENAI_API_KEY
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```
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OpenClaw keeps requesting `claude-opus-4-6`; LiteLLM handles the routing.
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</Accordion>
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<Accordion title="Viewing usage">
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```bash
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# Key info
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curl "http://localhost:4000/key/info" \
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-H "Authorization: Bearer sk-litellm-key"
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# Spend logs
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curl "http://localhost:4000/spend/logs" \
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-H "Authorization: Bearer $LITELLM_MASTER_KEY"
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```
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</Accordion>
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<Accordion title="Proxy behavior notes">
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- LiteLLM runs on `http://localhost:4000` by default.
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- OpenClaw connects through LiteLLM's proxy-style OpenAI-compatible `/v1` endpoint.
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- Native-OpenAI-only request shaping does not apply through a configured LiteLLM base URL:
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no `service_tier`, no Responses `store`, no prompt-cache hints, no OpenAI reasoning-effort
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payload shaping.
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- Hidden OpenClaw attribution headers (`originator`, `version`, `User-Agent`) are only sent to
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verified native OpenAI endpoints, so they are not injected on a custom LiteLLM base URL.
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</Accordion>
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</AccordionGroup>
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<Note>
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For general provider configuration and failover behavior, see [Model Providers](/concepts/model-providers).
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</Note>
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## Related
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<CardGroup cols={2}>
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<Card title="LiteLLM Docs" href="https://docs.litellm.ai" icon="book">
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Official LiteLLM documentation and API reference.
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</Card>
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<Card title="Model selection" href="/concepts/model-providers" icon="layers">
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Overview of all providers, model refs, and failover behavior.
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</Card>
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<Card title="Configuration" href="/gateway/configuration" icon="gear">
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Full config reference.
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</Card>
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<Card title="Models" href="/concepts/models" icon="brain">
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How to choose and configure models.
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</Card>
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</CardGroup>
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