feat: Update LangChain runners to implement Runner protocol returning RunnerResult

- LangChainModelRunner.run() implements the unified Runner protocol; returns RunnerResult
  with content, metrics (LDAIMetrics), raw, and parsed fields. Structured output is
  supported via the output_type parameter.
- LangChainAgentRunner.run() updated to return RunnerResult; populates tool_calls in
  LDAIMetrics from observed tool_calls in message responses.
- Legacy invoke_model() and invoke_structured_model() retained as deprecated adapters
  that delegate to run() and wrap results into ModelResponse / StructuredResponse for
  backward compatibility.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: jsonbailey

packages/ai-providers/server-ai-langchain/src/ldai_langchain/langchain_agent_runner.py

@@ -1,65 +1,83 @@
-from typing import Any
+from typing import Any, Dict, List, Optional
 
 from ldai import log
-from ldai.providers import AgentResult, AgentRunner
-from ldai.providers.types import LDAIMetrics
+from ldai.providers.types import LDAIMetrics, RunnerResult
 
 from ldai_langchain.langchain_helper import (
     extract_last_message_content,
+    get_tool_calls_from_response,
     sum_token_usage_from_messages,
 )
 
 
-class LangChainAgentRunner(AgentRunner):
+class LangChainAgentRunner:
     """
     CAUTION:
     This feature is experimental and should NOT be considered ready for production use.
     It may change or be removed without notice and is not subject to backwards
     compatibility guarantees.
 
-    AgentRunner implementation for LangChain.
+    Runner implementation for a single LangChain agent.
 
     Wraps a compiled LangChain agent graph (from ``langchain.agents.create_agent``)
     and delegates execution to it. Tool calling and loop management are handled
     internally by the graph.
-    Returned by LangChainRunnerFactory.create_agent(config, tools).
+    Returned by ``LangChainRunnerFactory.create_agent(config, tools)``.
+
+    Implements the unified :class:`~ldai.providers.runner.Runner` protocol.
     """
 
     def __init__(self, agent: Any):
         self._agent = agent
 
-    async def run(self, input: Any) -> AgentResult:
+    async def run(
+        self,
+        input: Any,
+        output_type: Optional[Dict[str, Any]] = None,
+    ) -> RunnerResult:
         """
-        Run the agent with the given input string.
+        Run the agent with the given input.
 
         Delegates to the compiled LangChain agent, which handles
         the tool-calling loop internally.
 
         :param input: The user prompt or input to the agent
-        :return: AgentResult with output, raw response, and aggregated metrics
+        :param output_type: Reserved for future structured output support;
+            currently ignored.
+        :return: :class:`RunnerResult` with ``content``, ``raw`` response, and
+            metrics including aggregated token usage and observed ``tool_calls``.
         """
         try:
             result = await self._agent.ainvoke({
                 "messages": [{"role": "user", "content": str(input)}]
             })
-            messages = result.get("messages", [])
-            output = extract_last_message_content(messages)
-            return AgentResult(
-                output=output,
-                raw=result,
+            messages: List[Any] = result.get("messages", [])
+            content = extract_last_message_content(messages)
+            tool_calls = self._extract_tool_calls(messages)
+            return RunnerResult(
+                content=content,
                 metrics=LDAIMetrics(
                     success=True,
                     usage=sum_token_usage_from_messages(messages),
+                    tool_calls=tool_calls if tool_calls else None,
                 ),
+                raw=result,
             )
         except Exception as error:
             log.warning(f"LangChain agent run failed: {error}")
-            return AgentResult(
-                output="",
-                raw=None,
+            return RunnerResult(
+                content="",
                 metrics=LDAIMetrics(success=False, usage=None),
             )
 
+    @staticmethod
+    def _extract_tool_calls(messages: List[Any]) -> List[str]:
+        """Collect tool call names from all messages in the agent output."""
+        names: List[str] = []
+        for msg in messages:
+            names.extend(get_tool_calls_from_response(msg))
+        return names
+
     def get_agent(self) -> Any:
         """Return the underlying compiled LangChain agent."""
         return self._agent

packages/ai-providers/server-ai-langchain/src/ldai_langchain/langchain_model_runner.py

@@ -1,10 +1,10 @@
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional
 
 from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.messages import BaseMessage
 from ldai import LDMessage, log
 from ldai.providers.model_runner import ModelRunner
-from ldai.providers.types import LDAIMetrics, ModelResponse, StructuredResponse
+from ldai.providers.types import LDAIMetrics, ModelResponse, RunnerResult, StructuredResponse
 
 from ldai_langchain.langchain_helper import (
     convert_messages_to_langchain,
@@ -15,10 +15,14 @@
 
 class LangChainModelRunner(ModelRunner):
     """
-    ModelRunner implementation for LangChain.
+    Runner implementation for LangChain chat models.
 
     Holds a fully-configured BaseChatModel.
-    Returned by LangChainConnector.create_model(config).
+    Returned by ``LangChainRunnerFactory.create_model(config)``.
+
+    Implements the unified :class:`~ldai.providers.runner.Runner` protocol via
+    :meth:`run`. The legacy :meth:`invoke_model` and :meth:`invoke_structured_model`
+    methods are preserved for backward compatibility.
     """
 
     def __init__(self, llm: BaseChatModel):
@@ -32,13 +36,37 @@ def get_llm(self) -> BaseChatModel:
         """
         return self._llm
 
-    async def invoke_model(self, messages: List[LDMessage]) -> ModelResponse:
+    async def run(
+        self,
+        input: Any,
+        output_type: Optional[Dict[str, Any]] = None,
+    ) -> RunnerResult:
         """
-        Invoke the LangChain model with an array of messages.
-
-        :param messages: Array of LDMessage objects representing the conversation
-        :return: ModelResponse containing the model's response and metrics
+        Run the LangChain model with the given input.
+
+        :param input: A string prompt or a list of :class:`LDMessage` objects
+        :param output_type: Optional JSON schema dict requesting structured output.
+            When provided, ``parsed`` on the returned :class:`RunnerResult` is
+            populated with the structured data.
+        :return: :class:`RunnerResult` containing ``content``, ``metrics``,
+            ``raw`` and (when ``output_type`` is set) ``parsed``.
         """
+        messages = self._coerce_input(input)
+        if output_type is not None:
+            return await self._run_structured(messages, output_type)
+        return await self._run_completion(messages)
+
+    @staticmethod
+    def _coerce_input(input: Any) -> List[LDMessage]:
+        if isinstance(input, str):
+            return [LDMessage(role='user', content=input)]
+        if isinstance(input, list):
+            return input
+        raise TypeError(
+            f"Unsupported input type for LangChainModelRunner.run: {type(input).__name__}"
+        )
+
+    async def _run_completion(self, messages: List[LDMessage]) -> RunnerResult:
         try:
             langchain_messages = convert_messages_to_langchain(messages)
             response: BaseMessage = await self._llm.ainvoke(langchain_messages)
@@ -52,58 +80,91 @@ async def invoke_model(self, messages: List[LDMessage]) -> ModelResponse:
                     f'Multimodal response not supported, expecting a string. '
                     f'Content type: {type(response.content)}, Content: {response.content}'
                 )
-                metrics = LDAIMetrics(success=False, usage=metrics.usage)
+                return RunnerResult(
+                    content='',
+                    metrics=LDAIMetrics(success=False, usage=metrics.usage),
+                    raw=response,
+                )
 
-            return ModelResponse(
-                message=LDMessage(role='assistant', content=content),
-                metrics=metrics,
-            )
+            return RunnerResult(content=content, metrics=metrics, raw=response)
         except Exception as error:
             log.warning(f'LangChain model invocation failed: {error}')
-            return ModelResponse(
-                message=LDMessage(role='assistant', content=''),
+            return RunnerResult(
+                content='',
                 metrics=LDAIMetrics(success=False, usage=None),
             )
 
-    async def invoke_structured_model(
-        self,
-        messages: List[LDMessage],
-        response_structure: Dict[str, Any],
-    ) -> StructuredResponse:
-        """
-        Invoke the LangChain model with structured output support.
-
-        :param messages: Array of LDMessage objects representing the conversation
-        :param response_structure: Dictionary defining the output structure
-        :return: StructuredResponse containing the structured data
-        """
-        structured_response = StructuredResponse(
-            data={},
-            raw_response='',
-            metrics=LDAIMetrics(success=False, usage=None),
-        )
+    async def _run_structured(
+        self, messages: List[LDMessage], response_structure: Dict[str, Any]
+    ) -> RunnerResult:
         try:
             langchain_messages = convert_messages_to_langchain(messages)
             structured_llm = self._llm.with_structured_output(response_structure, include_raw=True)
             response = await structured_llm.ainvoke(langchain_messages)
 
             if not isinstance(response, dict):
                 log.warning(f'Structured output did not return a dict. Got: {type(response)}')
-                return structured_response
+                return RunnerResult(
+                    content='',
+                    metrics=LDAIMetrics(success=False, usage=None),
+                )
 
             raw_response = response.get('raw')
-            if raw_response is not None:
-                if hasattr(raw_response, 'content'):
-                    structured_response.raw_response = raw_response.content
-                structured_response.metrics.usage = get_ai_usage_from_response(raw_response)
+            usage = get_ai_usage_from_response(raw_response) if raw_response is not None else None
+            raw_content = raw_response.content if raw_response is not None and hasattr(raw_response, 'content') else ''
 
             if response.get('parsing_error'):
                 log.warning('LangChain structured model invocation had a parsing error')
-                return structured_response
+                return RunnerResult(
+                    content=raw_content,
+                    metrics=LDAIMetrics(success=False, usage=usage),
+                    raw=raw_response,
+                )
 
-            structured_response.metrics.success = True
-            structured_response.data = response.get('parsed') or {}
-            return structured_response
+            parsed = response.get('parsed') or {}
+            return RunnerResult(
+                content=raw_content,
+                metrics=LDAIMetrics(success=True, usage=usage),
+                raw=raw_response,
+                parsed=parsed,
+            )
         except Exception as error:
             log.warning(f'LangChain structured model invocation failed: {error}')
-            return structured_response
+            return RunnerResult(
+                content='',
+                metrics=LDAIMetrics(success=False, usage=None),
+            )
+
+    async def invoke_model(self, messages: List[LDMessage]) -> ModelResponse:
+        """
+        Invoke the LangChain model with an array of messages.
+
+        .. deprecated::
+            Use :meth:`run` instead. This method delegates to :meth:`run` and
+            adapts the result to the legacy :class:`ModelResponse` shape.
+        """
+        result = await self._run_completion(messages)
+        return ModelResponse(
+            message=LDMessage(role='assistant', content=result.content),
+            metrics=result.metrics,
+        )
+
+    async def invoke_structured_model(
+        self,
+        messages: List[LDMessage],
+        response_structure: Dict[str, Any],
+    ) -> StructuredResponse:
+        """
+        Invoke the LangChain model with structured output support.
+
+        .. deprecated::
+            Use :meth:`run` with the ``output_type`` argument instead. This
+            method delegates to :meth:`run` and adapts the result to the
+            legacy :class:`StructuredResponse` shape.
+        """
+        result = await self._run_structured(messages, response_structure)
+        return StructuredResponse(
+            data=result.parsed or {},
+            raw_response=result.content,
+            metrics=result.metrics,
+        )

packages/ai-providers/server-ai-langchain/src/ldai_langchain/langgraph_agent_graph_runner.py

@@ -329,8 +329,10 @@ async def run(self, input: Any) -> AgentGraphResult:
             messages = result.get('messages', [])
             output = extract_last_message_content(messages)
 
-            # Flush per-node metrics to LD trackers
-            all_eval_results = await handler.flush(self._graph, pending_eval_tasks)
+            # Flush per-node metrics to LD trackers; eval results are tracked
+            # internally and intentionally not exposed on AgentGraphResult here
+            # — judge dispatch is the managed layer's responsibility.
+            await handler.flush(self._graph, pending_eval_tasks)
 
             tracker.track_path(handler.path)
             tracker.track_duration(duration)
@@ -341,7 +343,6 @@ async def run(self, input: Any) -> AgentGraphResult:
                 output=output,
                 raw=result,
                 metrics=LDAIMetrics(success=True),
-                evaluations=all_eval_results,
             )
 
         except Exception as exc:

packages/ai-providers/server-ai-langchain/tests/test_langchain_provider.py

@@ -464,7 +464,7 @@ class TestLangChainAgentRunner:
 
     @pytest.mark.asyncio
     async def test_runs_agent_and_returns_result(self):
-        """Should return AgentResult with the last message content from the graph."""
+        """Should return RunnerResult with the last message content from the graph."""
         from ldai_langchain import LangChainAgentRunner
 
         final_msg = AIMessage(content="The answer is 42.")
@@ -474,7 +474,7 @@ async def test_runs_agent_and_returns_result(self):
         runner = LangChainAgentRunner(mock_agent)
         result = await runner.run("What is the answer?")
 
-        assert result.output == "The answer is 42."
+        assert result.content == "The answer is 42."
         assert result.metrics.success is True
         mock_agent.ainvoke.assert_called_once_with(
             {"messages": [{"role": "user", "content": "What is the answer?"}]}
@@ -496,7 +496,7 @@ async def test_aggregates_token_usage_across_messages(self):
         runner = LangChainAgentRunner(mock_agent)
         result = await runner.run("Hello")
 
-        assert result.output == "final answer"
+        assert result.content == "final answer"
         assert result.metrics.success is True
         assert result.metrics.usage is not None
         assert result.metrics.usage.total == 30
@@ -505,7 +505,7 @@ async def test_aggregates_token_usage_across_messages(self):
 
     @pytest.mark.asyncio
     async def test_returns_failure_when_exception_thrown(self):
-        """Should return unsuccessful AgentResult when exception is thrown."""
+        """Should return unsuccessful RunnerResult when exception is thrown."""
         from ldai_langchain import LangChainAgentRunner
 
         mock_agent = MagicMock()
@@ -514,7 +514,7 @@ async def test_returns_failure_when_exception_thrown(self):
         runner = LangChainAgentRunner(mock_agent)
         result = await runner.run("Hello")
 
-        assert result.output == ""
+        assert result.content == ""
         assert result.metrics.success is False