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

For Qwen models served through vLLM, set params.qwenThinkingFormat: "chat-template" on the model entry when the server expects Qwen chat-template kwargs. 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 params.qwenThinkingFormat: "top-level" to send enable_thinking at the request root. Snake-case params.qwen_thinking_format is also accepted.

vLLM/Nemotron 3 can use chat-template kwargs to control whether reasoning is returned as hidden reasoning or visible answer text. When an OpenClaw session uses vllm/nemotron-3-* with thinking off, the bundled vLLM 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 has final precedence 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 make sure vLLM was started with the right tool-call parser and chat template for the model. For example, vLLM documents hermes for Qwen2.5 models and qwen3_xml for Qwen3-Coder models.

Symptoms:

• skills or tools never run
• the assistant prints raw JSON/XML such as {"name":"read","arguments":...}
• 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". For those model entries, force the OpenAI-compatible request field with params.extra_body:

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

Replace Qwen-Qwen2.5-Coder-32B-Instruct with the exact id returned by:

openclaw models list --provider vllm

You can 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 compatibility workaround. It makes every model turn with tools require a tool call, so use it only for a dedicated local model entry where that behavior is acceptable. Do not use it as a global default for all vLLM models, and do not use a proxy that blindly 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",
        request: { allowPrivateNetwork: true },
        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",
        request: { allowPrivateNetwork: true },
        timeoutSeconds: 300,
        models: [{ id: "your-model-id", name: "Local vLLM Model" }],
      },
    },
  },
}

timeoutSeconds applies to vLLM model HTTP requests only, including connection setup, response headers, body streaming, and the total guarded-fetch abort. Prefer this before 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 with the OpenAI-compatible server mode. For explicit loopback, LAN, or Tailscale endpoints, also set models.providers.vllm.request.allowPrivateNetwork: true; provider requests block private-network URLs by default unless the provider is explicitly trusted.

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 and no explicit models.providers.vllm config entry. If you have defined the provider manually, OpenClaw skips discovery and uses only your declared models.

If a Qwen model prints JSON/XML tool syntax instead of executing a skill, check the Qwen guidance in Advanced configuration above. The usual fix is:

• 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