launchdarkly · jsonbailey · Apr 2, 2026 · Mar 30, 2026 · Mar 30, 2026 · Mar 30, 2026
@@ -24,6 +24,9 @@ dependencies = [
     "langchain>=1.0.0",
 ]
 
+[project.optional-dependencies]
+graph = ["langgraph>=1.0.0"]
+
 [project.urls]
 Homepage = "https://docs.launchdarkly.com/sdk/ai/python"
 Repository = "https://github.com/launchdarkly/python-server-sdk-ai"
@@ -36,6 +39,7 @@ dev = [
     "mypy==1.18.2",
     "pycodestyle>=2.11.0",
     "isort>=5.12.0",
+    "langgraph>=1.0.0",
 ]
 
 [build-system]

@@ -1,16 +1,22 @@
-"""LangChain agent runner for LaunchDarkly AI SDK."""
-
 from typing import Any
 
 from ldai import log
 from ldai.providers import AgentResult, AgentRunner
 from ldai.providers.types import LDAIMetrics
 
-from ldai_langchain.langchain_helper import sum_token_usage_from_messages
+from ldai_langchain.langchain_helper import (
+    extract_last_message_content,
+    sum_token_usage_from_messages,
+)
 
 
 class LangChainAgentRunner(AgentRunner):
     """
+    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.
 
     Wraps a compiled LangChain agent graph (from ``langchain.agents.create_agent``)
@@ -37,11 +43,7 @@ async def run(self, input: Any) -> AgentResult:
                 "messages": [{"role": "user", "content": str(input)}]
             })
             messages = result.get("messages", [])
-            output = ""
-            if messages:
-                last = messages[-1]
-                if hasattr(last, 'content') and isinstance(last.content, str):
-                    output = last.content
+            output = extract_last_message_content(messages)
             return AgentResult(
                 output=output,
                 raw=result,

@@ -1,7 +1,7 @@
 from typing import Any, Dict, List, Optional, Union
 
 from langchain_core.language_models.chat_models import BaseChatModel
-from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
 from ldai import LDMessage, log
 from ldai.models import AIConfigKind
 from ldai.providers import ToolRegistry
@@ -51,18 +51,12 @@ def convert_messages_to_langchain(
     return result
 
 
-def create_langchain_model(ai_config: AIConfigKind, tool_registry: Optional[ToolRegistry] = None) -> BaseChatModel:
+def create_langchain_model(ai_config: AIConfigKind) -> BaseChatModel:
     """
     Create a LangChain BaseChatModel from a LaunchDarkly AI configuration.
 
-    If the config includes tool definitions and a tool_registry is provided, tools found
-    in the registry are bound to the model. Tools not found in the registry are skipped
-    with a warning. Built-in provider tools (e.g. code_interpreter) are not supported
-    via LangChain's bind_tools abstraction and are skipped with a warning.
-
     :param ai_config: The LaunchDarkly AI configuration
-    :param tool_registry: Optional registry mapping tool names to callable implementations
-    :return: A configured LangChain BaseChatModel, with tools bound if applicable
+    :return: A configured LangChain BaseChatModel
     """
     from langchain.chat_models import init_chat_model
 
@@ -73,112 +67,51 @@ def create_langchain_model(ai_config: AIConfigKind, tool_registry: Optional[Tool
     model_name = model_dict.get('name', '')
     provider = provider_dict.get('name', '')
     parameters = dict(model_dict.get('parameters') or {})
-    tool_definitions = parameters.pop('tools', []) or []
+    parameters.pop('tools', None)
     mapped_provider = map_provider(provider)
 
     # Bedrock requires the foundation provider (e.g. Bedrock:Anthropic) passed in
     # parameters separately from model_provider, which is used for LangChain routing.
     if mapped_provider == 'bedrock_converse' and 'provider' not in parameters:
         parameters['provider'] = provider.removeprefix('bedrock:')
 
-    model = init_chat_model(
+    return init_chat_model(
         model_name,
         model_provider=mapped_provider,
         **parameters,
     )
 
-    if tool_definitions and tool_registry is not None:
-        bindable = _resolve_tools_for_langchain(tool_definitions, tool_registry)
-        if bindable:
-            model = model.bind_tools(bindable)
-
-    return model
-
-
-def _iter_valid_tools(
-    tool_definitions: List[Dict[str, Any]],
-    tool_registry: ToolRegistry,
-) -> List[tuple]:
-    """
-    Filter LD tool definitions against a registry, returning (name, td) pairs for each
-    valid function tool that has a callable implementation. Built-in provider tools and
-    tools missing from the registry are skipped with a warning.
-    """
-    valid = []
-    for td in tool_definitions:
-        if not isinstance(td, dict):
-            continue
-
-        tool_type = td.get('type')
-        if tool_type and tool_type != 'function':
-            log.warning(
-                f"Built-in tool '{tool_type}' is not reliably supported via LangChain and will be skipped. "
-                "Use a provider-specific runner to use built-in provider tools."
-            )
-            continue
-
-        name = td.get('name')
-        if not name:
-            continue
-
-        if name not in tool_registry:
-            log.warning(f"Tool '{name}' is defined in the AI config but was not found in the tool registry; skipping.")
-            continue
-
-        valid.append((name, td))
-
-    return valid
-
 
-def _resolve_tools_for_langchain(
-    tool_definitions: List[Dict[str, Any]],
-    tool_registry: ToolRegistry,
-) -> List[Dict[str, Any]]:
-    """
-    Match LD tool definitions against a registry, returning function-calling tool dicts
-    for tools that have a callable implementation. Built-in provider tools and tools
-    missing from the registry are skipped with a warning.
-    """
-    return [
-        {
-            'type': 'function',
-            'function': {
-                'name': name,
-                'description': td.get('description', ''),
-                'parameters': td.get('parameters', {'type': 'object', 'properties': {}}),
-            },
-        }
-        for name, td in _iter_valid_tools(tool_definitions, tool_registry)
-    ]
-
-
-def build_structured_tools(ai_config: AIConfigKind, tool_registry: ToolRegistry) -> List[Any]:
+def build_tools(ai_config: AIConfigKind, tool_registry: ToolRegistry) -> List[Any]:
     """
-    Build a list of LangChain StructuredTool instances from LD tool definitions and a registry.
+    Return callables from the registry for each tool defined in the AI config.
 
-    Tools found in the registry are wrapped as StructuredTool with the name and description
-    from the LD config. Built-in provider tools and tools missing from the registry are
-    skipped with a warning.
+    Tools not found in the registry are skipped with a warning. The returned
+    callables can be passed directly to bind_tools or langchain.agents.create_agent.
+    Functions should have type-annotated parameters so LangChain can infer the schema.
 
     :param ai_config: The LaunchDarkly AI configuration
     :param tool_registry: Registry mapping tool names to callable implementations
-    :return: List of StructuredTool instances ready to pass to langchain.agents.create_agent
+    :return: List of callables ready to pass to bind_tools or create_agent
     """
-    from langchain_core.tools import StructuredTool
-
     config_dict = ai_config.to_dict()
     model_dict = config_dict.get('model') or {}
     parameters = dict(model_dict.get('parameters') or {})
     tool_definitions = parameters.pop('tools', []) or []
 
-    return [
-        StructuredTool.from_function(
-            func=tool_registry[name],
-            name=name,
-            description=td.get('description', ''),
-        )
-        for name, td in _iter_valid_tools(tool_definitions, tool_registry)
-    ]
+    tools = []
+    for td in tool_definitions:
+        if not isinstance(td, dict):
+            continue
+        name = td.get('name')
+        if not name:
+            continue
+        fn = tool_registry.get(name)
+        if fn is None:
+            log.warning(f"Tool '{name}' is defined in the AI config but was not found in the tool registry; skipping.")
+            continue
+        tools.append(fn)
+    return tools
 
 
 def get_ai_usage_from_response(response: Any) -> Optional[TokenUsage]:
@@ -234,6 +167,20 @@ def get_tool_calls_from_response(response: Any) -> List[str]:
     return names
 
 
+def extract_last_message_content(messages: List[Any]) -> str:
+    """
+    Extract the string content of the last message in a list.
+
+    :param messages: List of LangChain message objects
+    :return: String content of the last message, or empty string if none or content is not a str
+    """
+    if messages:
+        last = messages[-1]
+        if hasattr(last, 'content') and isinstance(last.content, str):
+            return last.content
+    return ''
+
+
 def sum_token_usage_from_messages(messages: List[Any]) -> Optional[TokenUsage]:
     """
     Sum token usage across LangChain messages using get_ai_usage_from_response per message.

@@ -5,7 +5,7 @@
 
 from ldai_langchain.langchain_agent_runner import LangChainAgentRunner
 from ldai_langchain.langchain_helper import (
-    build_structured_tools,
+    build_tools,
     create_langchain_model,
 )
 from ldai_langchain.langchain_model_runner import LangChainModelRunner
@@ -14,8 +14,38 @@
 class LangChainRunnerFactory(AIProvider):
     """LangChain ``AIProvider`` implementation for the LaunchDarkly AI SDK."""
 
+    def create_agent(self, config: Any, tools: Optional[ToolRegistry] = None) -> 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.
+
+        Create a configured LangChainAgentRunner for the given AI agent config.
+
+        :param config: The LaunchDarkly AI agent configuration
+        :param tools: ToolRegistry mapping tool names to callables
+        :return: LangChainAgentRunner ready to run the agent
+        """
+        from langchain.agents import create_agent as lc_create_agent
+        instructions = (config.instructions or '') if hasattr(config, 'instructions') else ''
+        llm = create_langchain_model(config)
+        lc_tools = build_tools(config, tools or {})
+
+        agent = lc_create_agent(
+            llm,
+            tools=lc_tools or None,
+            system_prompt=instructions or None,
+        )
+        return LangChainAgentRunner(agent)
+
     def create_agent_graph(self, graph_def: Any, tools: ToolRegistry) -> Any:
         """
+        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.
+
         Create a configured LangGraphAgentGraphRunner for the given graph definition.
 
         :param graph_def: The AgentGraphDefinition to execute
@@ -36,23 +66,3 @@ def create_model(self, config: AIConfigKind) -> LangChainModelRunner:
         """
         llm = create_langchain_model(config)
         return LangChainModelRunner(llm)
-
-    def create_agent(self, config: Any, tools: Optional[ToolRegistry] = None) -> LangChainAgentRunner:
-        """
-        Create a configured LangChainAgentRunner for the given AI agent config.
-
-        :param config: The LaunchDarkly AI agent configuration
-        :param tools: ToolRegistry mapping tool names to callables
-        :return: LangChainAgentRunner ready to run the agent
-        """
-        from langchain.agents import create_agent as lc_create_agent
-        instructions = (config.instructions or '') if hasattr(config, 'instructions') else ''
-        llm = create_langchain_model(config)
-        lc_tools = build_structured_tools(config, tools or {})
-
-        agent = lc_create_agent(
-            llm,
-            tools=lc_tools or None,
-            system_prompt=instructions or None,
-        )
-        return LangChainAgentRunner(agent)
@@ -1,5 +1,3 @@
-"""LangGraph agent graph runner for LaunchDarkly AI SDK."""
-
 import operator
 import time
 from typing import Annotated, Any, List
@@ -10,7 +8,9 @@
 from ldai.providers.types import LDAIMetrics
 
 from ldai_langchain.langchain_helper import (
+    build_tools,
     create_langchain_model,
+    extract_last_message_content,
     get_ai_metrics_from_response,
     get_ai_usage_from_response,
     get_tool_calls_from_response,
@@ -20,6 +20,11 @@
 
 class LangGraphAgentGraphRunner(AgentGraphRunner):
     """
+    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.
+
     AgentGraphRunner implementation for LangGraph.
 
     Compiles and runs the agent graph with LangGraph and automatically records
@@ -73,12 +78,7 @@ def handle_traversal(node: AgentGraphNode, ctx: dict) -> None:
                 model = None
                 if node_config.model:
                     lc_model = create_langchain_model(node_config)
-                    tool_defs = node_config.model.get_parameter('tools') or []
-                    tool_fns = [
-                        tools_ref[t.get('name', '')]
-                        for t in tool_defs
-                        if t.get('name', '') in tools_ref
-                    ]
+                    tool_fns = build_tools(node_config, tools_ref)
                     model = lc_model.bind_tools(tool_fns) if tool_fns else lc_model
 
                 def invoke(state: WorkflowState) -> WorkflowState:
@@ -124,12 +124,8 @@ def invoke(state: WorkflowState) -> WorkflowState:
             )
             duration = (time.perf_counter_ns() - start_ns) // 1_000_000
 
-            output = ''
             messages = result.get('messages', [])
-            if messages:
-                last = messages[-1]
-                if hasattr(last, 'content'):
-                    output = str(last.content)
+            output = extract_last_message_content(messages)
 
             if tracker:
                 tracker.track_path(exec_path)

@@ -422,7 +422,7 @@ def test_creates_agent_runner_with_instructions_and_tool_definitions(self):
 
         mock_agent = MagicMock()
         with patch('ldai_langchain.langchain_runner_factory.create_langchain_model') as mock_create, \
-             patch('ldai_langchain.langchain_runner_factory.build_structured_tools') as mock_tools, \
+             patch('ldai_langchain.langchain_runner_factory.build_tools') as mock_tools, \
              patch('langchain.agents.create_agent', return_value=mock_agent):
             mock_create.return_value = MagicMock()
             mock_tools.return_value = [MagicMock()]
@@ -447,7 +447,7 @@ def test_creates_agent_runner_with_no_tools(self):
 
         mock_agent = MagicMock()
         with patch('ldai_langchain.langchain_runner_factory.create_langchain_model') as mock_create, \
-             patch('ldai_langchain.langchain_runner_factory.build_structured_tools', return_value=[]), \
+             patch('ldai_langchain.langchain_runner_factory.build_tools', return_value=[]), \
              patch('langchain.agents.create_agent', return_value=mock_agent):
             mock_create.return_value = MagicMock()