* 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
5.5 KiB
summary, title, read_when
| summary | title | read_when | ||
|---|---|---|---|---|
| Run OpenClaw through LiteLLM Proxy for unified model access and cost tracking | LiteLLM |
|
LiteLLM is an open-source LLM gateway with a unified API to 100+ model providers. Route OpenClaw through LiteLLM for centralized cost tracking, logging, virtual keys with spend limits, and backend failover without changing OpenClaw config.
Quick start
```bash openclaw onboard --auth-choice litellm-api-key ```For non-interactive setup against a remote proxy, pass the proxy URL explicitly:
```bash
openclaw onboard --non-interactive --accept-risk --auth-choice litellm-api-key \
--litellm-api-key "$LITELLM_API_KEY" --custom-base-url "https://litellm.example/v1"
```
```bash
pip install 'litellm[proxy]'
litellm --model claude-opus-4-6
```
```bash
export LITELLM_API_KEY="your-litellm-key"
openclaw
```
Configuration
{
models: {
providers: {
litellm: {
baseUrl: "http://localhost:4000",
apiKey: "${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" },
},
},
}
The default model onboarding writes is litellm/claude-opus-4-6.
Image generation
LiteLLM can back the image_generate tool through OpenAI-compatible /images/generations and
/images/edits routes. Default image model is gpt-image-2; configure a different one under
agents.defaults.mediaModels.image:
{
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 (http://localhost:4000, 127.0.0.1, ::1, host.docker.internal) work
without a global private-network override. For a LAN-hosted proxy, set
models.providers.litellm.request.allowPrivateNetwork: true because the API key is sent to that host.
Advanced
Create a dedicated key for OpenClaw with spend limits:```bash
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`:
```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.
```bash
# 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 a configured LiteLLM base URL:
no `service_tier`, no Responses `store`, no prompt-cache hints, no OpenAI reasoning-effort
payload shaping.
- Hidden OpenClaw attribution headers (`originator`, `version`, `User-Agent`) are only sent to
verified native OpenAI endpoints, so they are not injected on a custom LiteLLM base URL.
For general provider configuration and failover behavior, see [Model Providers](/concepts/model-providers).