running-the-proxy

<!-- AI-RULEZ :: GENERATED FILE — DO NOT EDIT Content-Hash: blake3:c116c87cc47e287a604e874041bfbf9fd6c4da4206035b9f9943563df1a3ce4b Source-Hash: blake3:994b83e19a30ce07bfdfeb4caf56e995dc024de32e00f13b45cd995d0e441086 Schema-Version: v1 --> # Running the Proxy `liter-llm api` is a drop-in OpenAI-compatible gateway: 22 REST endpoints that route to 143 providers, with multi-tenant virtual keys, rate limits, budgets, and cost tracking. ## Start it ```bash liter-llm api --config liter-llm-proxy.toml ``` The proxy auto-discovers `liter-llm-proxy.toml` in the current directory. ## Configuration ```toml [server] host = "0.0.0.0" port = 4000 [general] master_key = "${LITER_LLM_MASTER_KEY}" # Each [[models]] entry maps a routable name to a provider/model and its key. [[models]] name = "gpt-4o" provider_model = "openai/gpt-4o" api_key = "${OPENAI_API_KEY}" [[models]] name = "claude-sonnet" provider_model = "anthropic/claude-sonnet-4-20250514" api_key = "${ANTHROPIC_API_KEY}" # Virtual keys scope which configured model names a caller may use. [[keys]] key = "sk-team-frontend" models = ["gpt-4o", "claude-sonnet"] rpm = 60 tpm = 100000 budget_limit = 50.0 ``` `${ENV_VAR}` interpolation keeps secrets out of the file. ## Call it like OpenAI ```bash curl http://localhost:4000/v1/chat/completions \ -H "Authorization: Bearer sk-team-frontend" \ -d '{"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello"}]}' ``` ## Notes - Virtual keys scope which models a caller may use and carry their own RPM/TPM and budget limits; the `master_key` administers them. - The OpenAPI 3.1 spec is served at `/openapi.json`. - Endpoints cover chat, embeddings, images, audio, moderations, files, batches, responses, and model listing. - Docker: `docker run -p 4000:4000 -e LITER_LLM_MASTER_KEY=sk-key ghcr.io/xberg-io/liter-llm`.