features/actor_v3_schema.feature

@@ -211,3 +211,24 @@ Feature: v3 Actor YAML schema support in CLI and registry
     Given a v3 graph actor config dict with skills and lsp as dict
     When I parse the v3 config through ReactiveConfigParser
     Then each graph node agent should have lsp dict propagated
+
+  # M5: options block forwarded to agent_config in _build_from_v3 (#11223).
+  @tdd_issue @tdd_issue_11223
+  Scenario: ReactiveConfigParser propagates options block to agent config
+    Given a v3 LLM actor config dict with options block
+    When I parse the v3 config through ReactiveConfigParser
+    Then the agent config should contain the options block
+
+  # M5: actor without options block is unaffected (#11223).
+  @tdd_issue @tdd_issue_11223
+  Scenario: ReactiveConfigParser handles v3 actor without options block
+    Given a v3 LLM actor config dict without options block
+    When I parse the v3 config through ReactiveConfigParser
+    Then the agent config should not contain an options key
+
+  # M5: empty options dict is preserved (#11223).
+  @tdd_issue @tdd_issue_11223
+  Scenario: ReactiveConfigParser preserves empty options block
+    Given a v3 LLM actor config dict with empty options block
+    When I parse the v3 config through ReactiveConfigParser
+    Then the agent config should contain an empty options block

features/consolidated_routing.feature

@@ -1379,3 +1379,24 @@ Feature: Consolidated Routing
     When the stream router tool agent processes "keep_me" through operation
     Then the stream router tool agent operation result should be "keep_me"
 
+  # M5: SimpleLLMAgent._resolve_llm forwards options to registry.create_llm (#11223).
+  @tdd_issue @tdd_issue_11223
+  Scenario: SimpleLLMAgent forwards options block to LLM constructor
+    Given a stream router llm agent with options block containing custom base url
+    When the stream router llm agent resolves the llm
+    Then the llm constructor should have received the custom base url
+
+  # M5: actor without options block is unaffected (#11223).
+  @tdd_issue @tdd_issue_11223
+  Scenario: SimpleLLMAgent without options block calls LLM constructor without extra kwargs
+    Given a stream router llm agent without options block
+    When the stream router llm agent resolves the llm
+    Then the llm constructor should not have received extra kwargs
+
+  # M5: top-level keys take precedence over duplicates in options (#11223).
+  @tdd_issue @tdd_issue_11223
+  Scenario: SimpleLLMAgent top-level key takes precedence over options duplicate
+    Given a stream router llm agent with top-level temperature and options block
+    When the stream router llm agent resolves the llm
+    Then the llm constructor should have received the top-level temperature
+

features/steps/actor_v3_schema_extended_steps.py

@@ -14,6 +14,7 @@ from unittest.mock import patch
 
 import yaml
 from behave import given, then, when
+from behave.runner import Context
 
 from cleveragents.actor.compiler import ActorCompilationError
 from cleveragents.actor.config import ActorConfiguration
@@ -781,3 +782,77 @@ def step_graph_nodes_have_lsp_dict(context: Any) -> None:
         assert lsp.get("auto") is True, (
             f"Node '{node_id}' expected lsp.auto=True, got {lsp}"
         )
+
+
+# ── M5: options block forwarding (#11223) ─────────────────────────────────
+
+
+@given("a v3 LLM actor config dict with options block")
+def step_v3_llm_config_with_options(context: Context) -> None:
+    context.v3_config = {
+        "name": "local/my-llama-actor",
+        "type": "llm",
+        "description": "Local llama.cpp actor",
+        "provider": "openai",
+        "model": "my-local-model",
+        "options": {
+            "openai_api_base": "http://localhost:8080/v1",
+            "openai_api_key": "none",
+            "temperature": 1.0,
+        },
+        "system_prompt": "You are a helpful assistant.",
+    }
+
+
+@given("a v3 LLM actor config dict without options block")
+def step_v3_llm_config_without_options(context: Context) -> None:
+    context.v3_config = {
+        "name": "local/standard-actor",
+        "type": "llm",
+        "description": "Standard actor without options",
+        "provider": "openai",
+        "model": "gpt-4",
+        "system_prompt": "You are a helpful assistant.",
+    }
+
+
+@then("the agent config should contain the options block")
+def step_agent_config_has_options(context: Context) -> None:
+    agents = context.reactive_config.agents
+    agent = next(iter(agents.values()))
+    options = agent.config.get("options")
+    assert options == {
+        "openai_api_base": "http://localhost:8080/v1",
+        "openai_api_key": "none",
+        "temperature": 1.0,
+    }, f"Expected exact options dict, got {options}"
+
+
+@then("the agent config should not contain an options key")
+def step_agent_config_no_options(context: Context) -> None:
+    agents = context.reactive_config.agents
+    agent = next(iter(agents.values()))
+    assert "options" not in agent.config, (
+        f"Expected no 'options' key in agent config, got {agent.config}"
+    )
+
+
+@given("a v3 LLM actor config dict with empty options block")
+def step_v3_llm_config_with_empty_options(context: Context) -> None:
+    context.v3_config = {
+        "name": "local/empty-options-actor",
+        "type": "llm",
+        "description": "Actor with empty options",
+        "provider": "openai",
+        "model": "gpt-4",
+        "options": {},
+        "system_prompt": "You are a helpful assistant.",
+    }
+
+
+@then("the agent config should contain an empty options block")
+def step_agent_config_has_empty_options(context: Context) -> None:
+    agents = context.reactive_config.agents
+    agent = next(iter(agents.values()))
+    options = agent.config.get("options")
+    assert options == {}, f"Expected empty options dict, got {options}"

features/steps/stream_router_unsafe_and_llm_coverage_steps.py

@@ -6,7 +6,8 @@
 
 from __future__ import annotations
 
-from unittest.mock import MagicMock
+from typing import Any
+from unittest.mock import MagicMock, patch
 
 from behave import given, then, when
 from behave.runner import Context
@@ -160,3 +161,115 @@ def step_then_tool_agent_operation_result(context: Context, expected: str) -> No
     assert context.op_result == expected, (
         f"Expected '{expected}', got '{context.op_result}'"
     )
+
+
+# ---------------------------------------------------------------------------
+# M5: SimpleLLMAgent._resolve_llm forwards options to create_llm (#11223).
+# ---------------------------------------------------------------------------
+
+
+@given("a stream router llm agent with options block containing custom base url")
+def step_given_llm_agent_with_options(context: Context) -> None:
+    context.llm_options_agent = SimpleLLMAgent(
+        "test_options",
+        {
+            "provider": "openai",
+            "model": "my-local-model",
+            "options": {
+                "openai_api_base": "http://localhost:8080/v1",
+                "openai_api_key": "none",
+            },
+        },
+    )
+    context.create_llm_kwargs: dict[str, Any] = {}
+
+
+@given("a stream router llm agent without options block")
+def step_given_llm_agent_without_options(context: Context) -> None:
+    context.llm_options_agent = SimpleLLMAgent(
+        "test_no_options",
+        {
+            "provider": "openai",
+            "model": "gpt-4",
+        },
+    )
+    context.create_llm_kwargs = {}
+
+
+@when("the stream router llm agent resolves the llm")
+def step_when_llm_agent_resolves(context: Context) -> None:
+    mock_llm = MagicMock()
+    captured: dict[str, Any] = {}
+
+    def fake_create_llm(
+        provider_type: Any = None, model_id: Any = None, **kwargs: Any
+    ) -> Any:
+        captured["provider_type"] = provider_type
+        captured["model_id"] = model_id
+        captured["kwargs"] = kwargs
+        return mock_llm
+
+    mock_registry = MagicMock()
+    mock_registry.create_llm.side_effect = fake_create_llm
+
+    with patch(
+        "cleveragents.reactive.stream_router.get_provider_registry",
+        return_value=mock_registry,
+    ):
+        context.llm_options_agent._resolve_llm()
+
+    context.create_llm_kwargs = captured
+
+
+@then("the llm constructor should have received the custom base url")
+def step_then_llm_received_custom_base_url(context: Context) -> None:
+    kwargs = context.create_llm_kwargs.get("kwargs", {})
+    assert "openai_api_base" in kwargs, (
+        f"Expected 'openai_api_base' in create_llm kwargs, got {kwargs}"
+    )
+    assert kwargs["openai_api_base"] == "http://localhost:8080/v1", (
+        f"Expected openai_api_base='http://localhost:8080/v1', got {kwargs}"
+    )
+    # openai_api_key is routed through __api_key_sentinel so the
+    # registry can distinguish explicit keys from environment defaults.
+    assert kwargs.get("__api_key_sentinel") == "none", (
+        f"Expected __api_key_sentinel='none' (from options.openai_api_key), "
+        f"got {kwargs}"
+    )
+
+
+@then("the llm constructor should not have received extra kwargs")
+def step_then_llm_no_extra_kwargs(context: Context) -> None:
+    kwargs = context.create_llm_kwargs.get("kwargs", {})
+    # When neither options nor top-level params are present, nothing
+    # should be forwarded to the LLM constructor.
+    assert kwargs == {}, f"Expected empty kwargs in create_llm call, got {kwargs}"
+
+
+@given("a stream router llm agent with top-level temperature and options block")
+def step_given_llm_agent_with_precedence(context: Context) -> None:
+    context.llm_options_agent = SimpleLLMAgent(
+        "test_precedence",
+        {
+            "provider": "openai",
+            "model": "gpt-4",
+            "temperature": 0.5,
+            "options": {
+                "temperature": 1.0,
+                "max_tokens": 4096,
+            },
+        },
+    )
+    context.create_llm_kwargs: dict[str, Any] = {}
+
+
+@then("the llm constructor should have received the top-level temperature")
+def step_then_llm_received_top_level_temperature(context: Context) -> None:
+    kwargs = context.create_llm_kwargs.get("kwargs", {})
+    assert kwargs.get("temperature") == 0.5, (
+        f"Expected temperature=0.5 (top-level precedence over options), got {kwargs}"
+    )
+    # max_tokens from options should still be forwarded.
+    assert kwargs.get("max_tokens") == 4096, (
+        f"Expected max_tokens=4096 from options, got {kwargs}"
+    )

src/cleveragents/reactive/config_parser.py

@@ -286,9 +286,9 @@ class ReactiveConfigParser:
         are propagated via the agent config so the runtime can attach them.
 
         All v3 fields — ``context_view``, ``memory``, ``context``,
-        ``env_vars``, ``response_format``, ``lsp_capabilities``, and
-        ``lsp_context_enrichment`` — are propagated into the agent config
-        so the runtime can consume them.
+        ``env_vars``, ``response_format``, ``lsp_capabilities``,
+        ``lsp_context_enrichment``, and ``options`` — are propagated
+        into the agent config so the runtime can consume them.
         """
         # m4: guard against None-to-string coercion for all three fields.
         actor_type = str(data.get("type") or "llm").lower()
@@ -375,6 +375,11 @@ class ReactiveConfigParser:
         if isinstance(response_format, dict):
             agent_config["response_format"] = response_format
 
+        # M5: propagate actor options block for custom backend support (#11223).
+        options_raw = data.get("options")
+        if isinstance(options_raw, dict):
+            agent_config["options"] = dict(options_raw)
+
         if actor_type in ("llm", "tool"):
             # Single-agent synthesis.
             rc.agents[actor_name] = AgentConfig(
@@ -424,6 +429,9 @@ class ReactiveConfigParser:
                         else dict(lsp_bindings)
                     )
                     node_config.setdefault("lsp", lsp_copy)
+                # M5: propagate actor-level options to graph nodes (#11223).
+                if isinstance(options_raw, dict) and options_raw:
+                    node_config.setdefault("options", dict(options_raw))
                 nodes_map[node_id] = node_config
 
                 # Create a reactive agent for each graph node.

src/cleveragents/reactive/stream_router.py

@@ -226,6 +226,55 @@ class SimpleLLMAgent:
             llm_kwargs["max_tokens"] = max_tokens
         if max_retries is not None:
             llm_kwargs["max_retries"] = max_retries
+        # M5: merge options block so custom LLM backend kwargs
+        # (e.g. openai_api_base, openai_api_key) are forwarded to the
+        # LLM constructor.  Options are applied after the fixed keys so
+        # that explicit top-level keys (temperature, max_tokens, etc.)
+        # take precedence over duplicates in options.
+        #
+        # openai_api_key is handled specially: the registry uses a
+        # __api_key_sentinel mechanism to distinguish explicitly
+        # provided keys from environment-sourced ones.  If the user
+        # supplies openai_api_key in options, we extract it and inject
+        # it via the sentinel so it overrides the registry's default.
+        options = dict(self.config.get("options") or {})
+        if "openai_api_key" in options:
+            llm_kwargs["__api_key_sentinel"] = options.pop("openai_api_key")
+        # Ensure sensitive/reserved ChatOpenAI constructor params cannot
+        # be injected via a crafted actor YAML.
+        _ALLOWED_OPTIONS: frozenset[str] = frozenset(
+            {
+                "openai_api_base",
+                "temperature",
+                "max_tokens",
+                "timeout",
+                "top_p",
+                "frequency_penalty",
+                "presence_penalty",
+            }
+        )
+        _RESERVED: frozenset[str] = frozenset({"provider_type", "model_id"})
+        for key, value in options.items():
+            if key in _RESERVED:
+                logger_sr.warning(
+                    "Actor '%s' options block contains reserved key '%s' "
+                    "that conflicts with positional arguments; ignoring.",
+                    self.name,
+                    key,
+                )
+                continue
+            if key not in llm_kwargs:
+                if key in _ALLOWED_OPTIONS:
+                    llm_kwargs[key] = value
+                else:
+                    logger_sr.warning(
+                        "Actor '%s' options block contains unrecognized "
+                        "key '%s'; ignoring for security.  Allowed keys: "
+                        "%s.",
+                        self.name,
+                        key,
+                        sorted(_ALLOWED_OPTIONS),
+                    )
         registry = get_provider_registry()
         self._llm = registry.create_llm(
             provider_type=provider, model_id=model, **llm_kwargs