LLM Task

llm-task is an optional plugin tool that runs a JSON-only LLM task and returns structured output (optionally validated against JSON Schema).This is ideal for workflow engines like Lobster: you can add a single LLM step without writing custom Moltbot code for each workflow.

Enable the plugin

Enable the plugin:


{
  "plugins": {
    "entries": {
      "llm-task": { "enabled": true }
    }
  }
}

Allowlist the tool (it is registered with optional: true):


{
  "agents": {
    "list": [\
      {\
        "id": "main",\
        "tools": { "allow": ["llm-task"] }\
      }\
    ]
  }
}

Config (optional)


{
  "plugins": {
    "entries": {
      "llm-task": {
        "enabled": true,
        "config": {
          "defaultProvider": "openai-codex",
          "defaultModel": "gpt-5.2",
          "defaultAuthProfileId": "main",
          "allowedModels": ["openai-codex/gpt-5.2"],
          "maxTokens": 800,
          "timeoutMs": 30000
        }
      }
    }
  }
}

allowedModels is an allowlist of provider/model strings. If set, any request outside the list is rejected.

Tool parameters

prompt (string, required)
input (any, optional)
schema (object, optional JSON Schema)
provider (string, optional)
model (string, optional)
authProfileId (string, optional)
temperature (number, optional)
maxTokens (number, optional)
timeoutMs (number, optional)

Output

Returns details.json containing the parsed JSON (and validates against schema when provided).

Example: Lobster workflow step


clawd.invoke --tool llm-task --action json --args-json '{
  "prompt": "Given the input email, return intent and draft.",
  "input": {
    "subject": "Hello",
    "body": "Can you help?"
  },
  "schema": {
    "type": "object",
    "properties": {
      "intent": { "type": "string" },
      "draft": { "type": "string" }
    },
    "required": ["intent", "draft"],
    "additionalProperties": false
  }
}'

Safety notes

The tool is JSON-only and instructs the model to output only JSON (no code fences, no commentary).
No tools are exposed to the model for this run.
Treat output as untrusted unless you validate with schema.
Put approvals before any side-effecting step (send, post, exec).