README.md

launchdarkly-ai-langchain-messages

LangChain handler for launchdarkly-ai-server using LangChain chat models (langchain-core). Works with any BaseChatModel — defaults to ChatOpenAI. Runs a manual tool-call loop using LangChain's bind_tools API.

provides_for: ['*', 'messages'] — matches any flag variation where meta.mode is "messages" and no more-specific handler is registered. LangChain is a framework adapter, not a provider: it routes through langchain-anthropic, langchain-openai, and others at runtime based on config.provider.name. Use '*' so that flags configured with provider.name = "Anthropic" or "OpenAI" are automatically handled without requiring a separate native handler.

Installation

pip install launchdarkly-ai-server launchdarkly-ai-langchain-messages

The default model is ChatOpenAI, so set OPENAI_API_KEY unless you pass a custom BaseChatModel.

Usage

With the default model (ChatOpenAI)

import asyncio
from launchdarkly_ai_server import config, shutdown
from launchdarkly_ai_langchain_messages import create_langchain_messages_handler

async def main():
    result = await config(
        key="my-ai-config-flag",
        handler=create_langchain_messages_handler(),
    ).invoke("What is feature flagging?", {"kind": "user", "key": "user-123"})

    print(result.response)
    await shutdown()

asyncio.run(main())

With a custom BaseChatModel

from langchain_anthropic import ChatAnthropic
from launchdarkly_ai_langchain_messages import create_langchain_messages_handler

handler = create_langchain_messages_handler(ChatAnthropic(model="claude-opus-4-5"))

A constructed instance cannot see flag parameters. Pass a function instead if the model should be built after evaluation:

handler = create_langchain_messages_handler(
    lambda config: ChatAnthropic(
        **{
            **(config["model"].get("parameters") or {}),
            "model": config["model"]["name"],
        }
    )
)

Convenience wrapper

import asyncio
from launchdarkly_ai_langchain_messages import langchain_messages

async def main():
    user_input = "What is feature flagging?"
    result = await langchain_messages(
        user_input,
        {"kind": "user", "key": "user-123"},
        {"key": "my-ai-config-flag"},
        variables={"user_input": user_input},
    )
    print(result.response)

asyncio.run(main())

How It Works

• Uses the system prompt and conversation history defined in your LaunchDarkly flag config.
• Template placeholders ({{variable}}) in the prompt are substituted using variables before the call.
• If tools are defined in the flag config, binds them to the model and executes them as requested, feeding results back until the model produces a final response.
• Emits an OTel span and LaunchDarkly telemetry for every call.

Choosing Between langchain-agents and langchain-messages

	langchain-agents	langchain-messages
Orchestration	LangGraph StateGraph	Manual tool loop
Reasoning style	ReAct (reason + act cycles)	Single invoke per tool round-trip
Best for	Complex multi-step reasoning	Straightforward tool calls

Environment Variables

Variable	Description
OPENAI_API_KEY	Required when using the default ChatOpenAI model
LD_SDK_KEY	LaunchDarkly server-side SDK key
LD_SERVICE_NAME	OTel service.name resource attribute (default: python-sdk)
LD_ENVIRONMENT	deployment.environment attribute attached to telemetry
OTEL_EXPORTER_OTLP_ENDPOINT	OTLP endpoint override (default: LaunchDarkly Observability backend)