vLLM

vLLM is treated as a proxy-style OpenAI-compatible /v1 backend, not a native OpenAI endpoint:

For Qwen models, set compat.thinkingFormat: "qwen-chat-template" on the model row when the server expects Qwen chat-template kwargs. These models expose a binary /think profile (off, on) because Qwen chat-template thinking is an on/off flag, not an OpenAI-style effort ladder.

{  models: {    providers: {      vllm: {        models: [          {            id: "Qwen/Qwen3-8B",            name: "Qwen3 8B",            reasoning: true,            compat: { thinkingFormat: "qwen-chat-template" },          },        ],      },    },  },}

OpenClaw maps /think off to:

{  "chat_template_kwargs": {    "enable_thinking": false,    "preserve_thinking": true  }}

Non-off thinking levels send enable_thinking: true. If your endpoint expects DashScope-style top-level flags instead, use compat.thinkingFormat: "qwen" to send enable_thinking at the request root.

For vllm/nemotron-3-* models with thinking off, the bundled plugin sends:

{  "chat_template_kwargs": {    "enable_thinking": false,    "force_nonempty_content": true  }}

To customize these values, set chat_template_kwargs under the model params. If you also set params.extra_body.chat_template_kwargs, that value wins because extra_body is the last request-body override.

{  agents: {    defaults: {      models: {        "vllm/nemotron-3-super": {          params: {            chat_template_kwargs: {              enable_thinking: false,              force_nonempty_content: true,            },          },        },      },    },  },}

First confirm vLLM was started with the right tool-call parser and chat template for the model. vLLM documents hermes for Qwen2.5 models and qwen3_xml for Qwen3-Coder models.

Symptoms: skills/tools never run, the assistant prints raw JSON/XML such as {"name":"read","arguments":...}, or vLLM returns an empty tool_calls array when OpenClaw sends tool_choice: "auto".

Some Qwen/vLLM combinations return structured tool calls only when the request uses tool_choice: "required". Force it per model with params.extra_body:

{  agents: {    defaults: {      models: {        "vllm/Qwen-Qwen2.5-Coder-32B-Instruct": {          params: {            extra_body: {              tool_choice: "required",            },          },        },      },    },  },}

Replace the model id with the exact id from openclaw models list --provider vllm, or apply the same override from the CLI:

openclaw config set agents.defaults.models '{"vllm/Qwen-Qwen2.5-Coder-32B-Instruct":{"params":{"extra_body":{"tool_choice":"required"}}}}' --strict-json --merge

This is an opt-in workaround: it forces every turn with tools to make a tool call, so use it only for a dedicated model entry where that is acceptable. Do not set it as a global default for all vLLM models, and do not pair it with a proxy that converts arbitrary assistant text into executable tool calls.

If your vLLM server runs on a non-default host or port, set baseUrl in the explicit provider config:

{  models: {    providers: {      vllm: {        baseUrl: "http://192.168.1.50:9000/v1",        apiKey: "${VLLM_API_KEY}",        api: "openai-completions",        timeoutSeconds: 300,        models: [          {            id: "my-custom-model",            name: "Remote vLLM Model",            reasoning: false,            input: ["text"],            contextWindow: 64000,            maxTokens: 4096,          },        ],      },    },  },}

For large local models, remote LAN hosts, or tailnet links, set a provider-scoped request timeout:

{  models: {    providers: {      vllm: {        baseUrl: "http://192.168.1.50:8000/v1",        apiKey: "${VLLM_API_KEY}",        api: "openai-completions",        timeoutSeconds: 300,        models: [{ id: "your-model-id", name: "Local vLLM Model" }],      },    },  },}

timeoutSeconds applies to vLLM model HTTP requests only: connection setup, response headers, body streaming, and the total guarded-fetch abort. It also raises the LLM idle/stream watchdog ceiling above the implicit ~120s default for this provider. Prefer this over increasing agents.defaults.timeoutSeconds, which controls the whole agent run.

Check that the vLLM server is running and accessible:

curl http://127.0.0.1:8000/v1/models

If you see a connection error, verify the host, port, and that vLLM started in OpenAI-compatible server mode. OpenClaw trusts the exact configured models.providers.vllm.baseUrl origin for guarded model requests on loopback, LAN, and Tailscale endpoints. Metadata/link-local origins remain blocked without explicit opt-in. Set models.providers.vllm.request.allowPrivateNetwork: true only when vLLM requests must reach another private origin, or false to opt out of exact-origin trust.

If requests fail with auth errors, set a real VLLM_API_KEY that matches your server configuration, or configure the provider explicitly under models.providers.vllm.

Auto-discovery requires VLLM_API_KEY to be set. If you have defined models.providers.vllm, OpenClaw uses only your declared models unless agents.defaults.models includes "vllm/*": {}.

If a Qwen model prints JSON/XML tool syntax instead of executing a skill:

• Start vLLM with the correct parser/template for that model.
• Confirm the exact model id with openclaw models list --provider vllm.
• Add a dedicated per-model params.extra_body.tool_choice: "required" override only if tool_choice: "auto" still returns empty or text-only tool calls.