Pass instruction depth config to llm-connect

src/activity_core/llm_client.py

@@ -17,7 +17,12 @@ import httpx
 class DisabledLLMClient:
     """LLM client used when no llm-connect endpoint is configured."""
 
-    def complete(self, prompt: str, model: str = "") -> str:  # noqa: ARG002
+    def complete(
+        self,
+        prompt: str,
+        model: str = "",
+        config: dict[str, Any] | None = None,
+    ) -> str:  # noqa: ARG002
         raise RuntimeError("LLM_CONNECT_URL is not configured")
 
 
@@ -28,13 +33,19 @@ class LLMConnectClient:
         self.base_url = base_url.rstrip("/")
         self.timeout_seconds = timeout_seconds
 
-    def complete(self, prompt: str, model: str = "") -> str:
+    def complete(
+        self,
+        prompt: str,
+        model: str = "",
+        config: dict[str, Any] | None = None,
+    ) -> str:
+        run_config = dict(config or {})
+        if model and "model_name" not in run_config:
+            run_config["model_name"] = model
+        run_config.setdefault("timeout_seconds", int(self.timeout_seconds))
         payload: dict[str, Any] = {
             "prompt": prompt,
-            "config": {
-                "model_name": model,
-                "timeout_seconds": int(self.timeout_seconds),
-            },
+            "config": run_config,
         }
         resp = httpx.post(
             f"{self.base_url}/execute",

src/activity_core/models.py

@@ -109,6 +109,10 @@ class InstructionDef(BaseModel):
         description="Allowlist of event/context fields that may appear in the prompt template.",
     )
     model: str = Field(description="LLM model identifier, e.g. 'claude-sonnet-4-6'.")
+    temperature: float | None = Field(default=None)
+    max_tokens: int | None = Field(default=None)
+    max_depth: int | None = Field(default=None)
+    model_params: dict[str, Any] = Field(default_factory=dict)
     prompt: str = Field(description="Prompt template with {field.path} placeholders.")
     output_schema: str = Field(description="Path to JSON Schema file for output validation.")
     review_required: bool = Field(default=False)

src/activity_core/rules/executor.py

@@ -144,15 +144,16 @@ def _execute(
     # Step 2 — render prompt (raises UntrustedFieldError on policy violation)
     rendered = _render_prompt(instr.prompt, instr.trusted_fields, event, context)
     prompt_hash = hashlib.sha256(rendered.encode()).hexdigest()
+    llm_config = _llm_run_config(instr)
 
     # Step 3 — call LLM
-    raw_output = llm_client.complete(rendered, model=instr.model)
+    raw_output = llm_client.complete(rendered, model=instr.model, config=llm_config)
 
     # Step 4 — validate and optionally retry
     task_specs, report, error = _validate_output(raw_output, instr)
     if error:
         retry_prompt = rendered + f"\n\nPrevious output was invalid: {error}\nPlease fix."
-        raw_output = llm_client.complete(retry_prompt, model=instr.model)
+        raw_output = llm_client.complete(retry_prompt, model=instr.model, config=llm_config)
         task_specs, report, error = _validate_output(raw_output, instr)
         if error:
             logger.warning(
@@ -172,6 +173,19 @@ def _execute(
     )
 
 
+def _llm_run_config(instr: Any) -> dict[str, Any]:
+    """Build the llm-connect RunConfig payload from instruction metadata."""
+    config: dict[str, Any] = {"model_name": instr.model}
+    for field in ("temperature", "max_tokens", "max_depth"):
+        value = getattr(instr, field, None)
+        if value is not None:
+            config[field] = value
+    model_params = getattr(instr, "model_params", None)
+    if model_params:
+        config["model_params"] = model_params
+    return config
+
+
 def _empty_result(instr: Any, prompt_hash: str | None = None) -> InstructionResult:
     return InstructionResult(
         tasks=[],