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feat: Introduce ManagedModel and ModelRunner
feat: Add ModelRunner ABC with invoke_model() and invoke_structured_model() feat: Add ManagedModel replacing Chat; expose get_model_runner() escape hatch feat!: Rename ChatResponse to ModelResponse in providers/types.py feat!: Extract OpenAIModelRunner from OpenAIRunnerFactory; factory is now model-creation-only feat!: Extract LangChainModelRunner from LangChainRunnerFactory; factory is now model-creation-only feat: Add OpenAIHelper with shared utilities for model and future agent runners feat: Add LangChainHelper with shared utilities for model and future agent runners feat!: LangChainRunnerFactory is now a no-arg factory; static helpers moved to LangChainHelper fix: LDClient.create_chat() is deprecated in favour of create_model() fix: Chat alias in ldai.chat is deprecated in favour of ManagedModel fix: Rename ai_provider param to model_runner in Judge and ManagedModel
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Lines changed: 758 additions & 703 deletions

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

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"""LaunchDarkly AI SDK - LangChain Connector."""
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from ldai_langchain.langchain_helper import LangChainHelper
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from ldai_langchain.langchain_model_runner import LangChainModelRunner
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from ldai_langchain.langchain_runner_factory import LangChainRunnerFactory
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__version__ = "0.1.0"
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__all__ = [
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'__version__',
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'LangChainRunnerFactory',
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'LangChainHelper',
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'LangChainModelRunner',
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]

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

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"""Shared LangChain utilities for the LaunchDarkly AI SDK."""
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from typing import Any, Dict, List, Optional, Union
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
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from ldai import LDMessage
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from ldai.models import AIConfigKind
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from ldai.providers.types import LDAIMetrics
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from ldai.tracker import TokenUsage
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class LangChainHelper:
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"""
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Shared utilities for LangChain-based runners (model, agent, agent graph).
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All methods are static — this class is a namespace, not meant to be instantiated.
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"""
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@staticmethod
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def map_provider(ld_provider_name: str) -> str:
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"""
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Map a LaunchDarkly provider name to its LangChain equivalent.
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:param ld_provider_name: LaunchDarkly provider name
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:return: LangChain-compatible provider name
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"""
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lowercased_name = ld_provider_name.lower()
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# Bedrock is the only provider that uses "provider:model_family" (e.g. Bedrock:Anthropic).
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if lowercased_name.startswith('bedrock:'):
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return 'bedrock_converse'
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mapping: Dict[str, str] = {
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'gemini': 'google-genai',
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'bedrock': 'bedrock_converse',
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}
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return mapping.get(lowercased_name, lowercased_name)
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@staticmethod
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def convert_messages(
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messages: List[LDMessage],
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) -> List[Union[HumanMessage, SystemMessage, AIMessage]]:
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"""
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Convert LaunchDarkly messages to LangChain message objects.
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:param messages: List of LDMessage objects
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:return: List of LangChain message objects
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:raises ValueError: If an unsupported message role is encountered
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"""
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result: List[Union[HumanMessage, SystemMessage, AIMessage]] = []
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for msg in messages:
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if msg.role == 'system':
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result.append(SystemMessage(content=msg.content))
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elif msg.role == 'user':
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result.append(HumanMessage(content=msg.content))
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elif msg.role == 'assistant':
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result.append(AIMessage(content=msg.content))
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else:
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raise ValueError(f'Unsupported message role: {msg.role}')
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return result
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@staticmethod
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def create_langchain_model(ai_config: AIConfigKind) -> BaseChatModel:
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"""
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Create a LangChain BaseChatModel from a LaunchDarkly AI configuration.
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:param ai_config: The LaunchDarkly AI configuration
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:return: A configured LangChain BaseChatModel
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"""
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from langchain.chat_models import init_chat_model
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config_dict = ai_config.to_dict()
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model_dict = config_dict.get('model') or {}
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provider_dict = config_dict.get('provider') or {}
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model_name = model_dict.get('name', '')
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provider = provider_dict.get('name', '')
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parameters = model_dict.get('parameters') or {}
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return init_chat_model(
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model_name,
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model_provider=LangChainHelper.map_provider(provider),
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**parameters,
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)
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@staticmethod
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def get_ai_metrics_from_response(response: Any) -> LDAIMetrics:
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"""
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Extract LaunchDarkly AI metrics from a LangChain response.
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:param response: The response from a LangChain model (BaseMessage or similar)
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:return: LDAIMetrics with success status and token usage
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"""
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usage: Optional[TokenUsage] = None
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if hasattr(response, 'response_metadata') and response.response_metadata:
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token_usage = (
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response.response_metadata.get('tokenUsage')
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or response.response_metadata.get('token_usage')
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)
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if token_usage:
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usage = TokenUsage(
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total=token_usage.get('totalTokens', 0) or token_usage.get('total_tokens', 0),
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input=token_usage.get('promptTokens', 0) or token_usage.get('prompt_tokens', 0),
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output=token_usage.get('completionTokens', 0) or token_usage.get('completion_tokens', 0),
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)
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return LDAIMetrics(success=True, usage=usage)

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

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"""LangChain model runner for LaunchDarkly AI SDK."""
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from typing import Any, Dict, List
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_core.messages import BaseMessage
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from ldai import LDMessage, log
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from ldai.providers.model_runner import ModelRunner
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from ldai.providers.types import LDAIMetrics, ModelResponse, StructuredResponse
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from ldai_langchain.langchain_helper import LangChainHelper
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class LangChainModelRunner(ModelRunner):
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"""
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ModelRunner implementation for LangChain.
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Holds a fully-configured BaseChatModel.
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Returned by LangChainConnector.create_model(config).
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"""
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def __init__(self, llm: BaseChatModel):
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self._llm = llm
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def get_llm(self) -> BaseChatModel:
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"""
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Return the underlying LangChain BaseChatModel.
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:return: The BaseChatModel instance
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"""
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return self._llm
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async def invoke_model(self, messages: List[LDMessage]) -> ModelResponse:
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"""
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Invoke the LangChain model with an array of messages.
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:param messages: Array of LDMessage objects representing the conversation
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:return: ModelResponse containing the model's response and metrics
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"""
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try:
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langchain_messages = LangChainHelper.convert_messages(messages)
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response: BaseMessage = await self._llm.ainvoke(langchain_messages)
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metrics = LangChainHelper.get_ai_metrics_from_response(response)
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content: str = ''
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if isinstance(response.content, str):
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content = response.content
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else:
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log.warning(
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f'Multimodal response not supported, expecting a string. '
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f'Content type: {type(response.content)}, Content: {response.content}'
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)
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metrics = LDAIMetrics(success=False, usage=metrics.usage)
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return ModelResponse(
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message=LDMessage(role='assistant', content=content),
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metrics=metrics,
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)
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except Exception as error:
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log.warning(f'LangChain model invocation failed: {error}')
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return ModelResponse(
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message=LDMessage(role='assistant', content=''),
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metrics=LDAIMetrics(success=False, usage=None),
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)
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async def invoke_structured_model(
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self,
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messages: List[LDMessage],
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response_structure: Dict[str, Any],
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) -> StructuredResponse:
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"""
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Invoke the LangChain model with structured output support.
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:param messages: Array of LDMessage objects representing the conversation
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:param response_structure: Dictionary defining the output structure
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:return: StructuredResponse containing the structured data
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"""
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try:
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langchain_messages = LangChainHelper.convert_messages(messages)
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structured_llm = self._llm.with_structured_output(response_structure, include_raw=True)
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response = await structured_llm.ainvoke(langchain_messages)
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if not isinstance(response, dict):
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log.warning(f'Structured output did not return a dict. Got: {type(response)}')
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return StructuredResponse(
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data={},
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raw_response='',
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metrics=LDAIMetrics(success=False, usage=None),
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)
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raw_response = response.get('raw')
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usage = LangChainHelper.get_ai_usage_from_response(raw_response) if raw_response is not None else None
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raw_content = raw_response.content if hasattr(raw_response, 'content') else ''
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if response.get('parsing_error'):
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log.warning('LangChain structured model invocation had a parsing error')
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return StructuredResponse(
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data={},
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raw_response=raw_content,
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metrics=LDAIMetrics(success=False, usage=usage),
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)
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return StructuredResponse(
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data=response.get('parsed') or {},
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raw_response=raw_content,
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metrics=LDAIMetrics(success=True, usage=usage),
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)
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except Exception as error:
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log.warning(f'LangChain structured model invocation failed: {error}')
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return StructuredResponse(
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data={},
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raw_response='',
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metrics=LDAIMetrics(success=False, usage=None),
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)
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