--- summary: "Run OpenClaw with SGLang (OpenAI-compatible self-hosted server)" read_when: - You want to run OpenClaw against a local SGLang server - You want OpenAI-compatible /v1 endpoints with your own models title: "SGLang" --- SGLang serves open-weight models via an OpenAI-compatible HTTP API. OpenClaw connects to SGLang using the `openai-completions` provider family with auto-discovery of available models. | Property | Value | | ------------------------- | ------------------------------------------------------------ | | Provider id | `sglang` | | Plugin | bundled, `enabledByDefault: true` | | Auth env var | `SGLANG_API_KEY` (any non-empty value if server has no auth) | | Onboarding flag | `--auth-choice sglang` | | API | OpenAI-compatible (`openai-completions`) | | Default base URL | `http://127.0.0.1:30000/v1` | | Default model placeholder | `sglang/Qwen/Qwen3-8B` | | Streaming usage | Yes (`supportsStreamingUsage: true`) | | Pricing | Marked external-free (`modelPricing.external: false`) | OpenClaw also **auto-discovers** available models from SGLang when you opt in with `SGLANG_API_KEY` and you do not define an explicit `models.providers.sglang` entry — see [Model discovery (implicit provider)](#model-discovery-implicit-provider) below. ## Getting started Launch SGLang with an OpenAI-compatible server. Your base URL should expose `/v1` endpoints (for example `/v1/models`, `/v1/chat/completions`). SGLang commonly runs on: - `http://127.0.0.1:30000/v1` Any value works if no auth is configured on your server: ```bash export SGLANG_API_KEY="sglang-local" ``` ```bash openclaw onboard ``` Or configure the model manually: ```json5 { agents: { defaults: { model: { primary: "sglang/your-model-id" }, }, }, } ``` ## Model discovery (implicit provider) When `SGLANG_API_KEY` is set (or an auth profile exists) and you **do not** define `models.providers.sglang`, OpenClaw will query: - `GET http://127.0.0.1:30000/v1/models` and convert the returned IDs into model entries. If you set `models.providers.sglang` explicitly, auto-discovery is skipped and you must define models manually. ## Explicit configuration (manual models) Use explicit config when: - SGLang runs on a different host/port. - You want to pin `contextWindow`/`maxTokens` values. - Your server requires a real API key (or you want to control headers). ```json5 { models: { providers: { sglang: { baseUrl: "http://127.0.0.1:30000/v1", apiKey: "${SGLANG_API_KEY}", api: "openai-completions", models: [ { id: "your-model-id", name: "Local SGLang Model", reasoning: false, input: ["text"], cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 }, contextWindow: 128000, maxTokens: 8192, }, ], }, }, }, } ``` ## Advanced configuration SGLang is treated as a proxy-style OpenAI-compatible `/v1` backend, not a native OpenAI endpoint. | Behavior | SGLang | |----------|--------| | OpenAI-only request shaping | Not applied | | `service_tier`, Responses `store`, prompt-cache hints | Not sent | | Reasoning-compat payload shaping | Not applied | | Hidden attribution headers (`originator`, `version`, `User-Agent`) | Not injected on custom SGLang base URLs | **Server not reachable** Verify the server is running and responding: ```bash curl http://127.0.0.1:30000/v1/models ``` **Auth errors** If requests fail with auth errors, set a real `SGLANG_API_KEY` that matches your server configuration, or configure the provider explicitly under `models.providers.sglang`. If you run SGLang without authentication, any non-empty value for `SGLANG_API_KEY` is sufficient to opt in to model discovery. ## Related Choosing providers, model refs, and failover behavior. Full config schema including provider entries.