launchdarkly-ai-claude-agents

Anthropic Claude handler for launchdarkly-ai-server using the Claude Agent SDK (claude-agent-sdk). Runs an agentic query loop with native MCP tool support.

provides_for: ['Anthropic', 'agent'] — matches flag variations where provider.name is "Anthropic" and meta.mode is "agent".

Installation

pip install launchdarkly-ai-server launchdarkly-ai-claude-agents

Set ANTHROPIC_API_KEY in your environment (the Anthropic SDK reads it automatically).

Usage

With config()

import asyncio
from launchdarkly_ai_server import config, shutdown
from launchdarkly_ai_claude_agents import create_claude_agents_handler

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

    print(result.response)
    await shutdown()

asyncio.run(main())

Convenience wrapper

import asyncio
from launchdarkly_ai_claude_agents import claude_agents

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

asyncio.run(main())

Agent graphs — claude_graph()

Runs a LaunchDarkly agent graph with the Claude agent handler pre-bound. Equivalent to calling the base graph() with handlers=[create_claude_agents_handler()]. See the core client docs for the full graph() API.

import asyncio
from launchdarkly_ai_claude_agents import claude_graph

async def main():
    result = await claude_graph("support-graph").invoke(
        "I was double charged",
        {"kind": "user", "key": "user-123"},
    )
    print(result["response"])

asyncio.run(main())

Native graph adapter — to_claude_agents()

Converts a resolve_graph() result into a framework-native Claude sub-agent tree (post-order traversal: leaves → root). Each child node is wrapped as an in-process MCP tool; the root runs a single query() with its children accessible as sub-agent tools. No cloud-registered agents required.

import asyncio
from launchdarkly_ai_server import resolve_graph
from launchdarkly_ai_claude_agents import to_claude_agents

async def main():
    ctx = {"kind": "user", "key": "user-123"}
    result = await to_claude_agents(
        resolve_graph("support-graph", context=ctx),
        {"tool_handlers": registry.tools, "context": ctx},
    ).invoke("I was double charged")
    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, the Claude Agent SDK handles all tool dispatch and the agentic loop automatically — no extra wiring needed.
• Emits an OTel span and LaunchDarkly telemetry for every call.

Built-in Claude Tools

This package exports NativeTool sentinels for Claude Code built-in capabilities. Place them as values in tool_handlers to enable the corresponding native Claude tool without writing a handler function:

from launchdarkly_ai_claude_agents import ClaudeWebSearch, create_claude_agents_handler
from launchdarkly_ai_server import config

result = await config(
    key="my-ai-config-flag",
    tool_handlers={"web-search": ClaudeWebSearch},
    handler=[create_claude_agents_handler()],
).invoke("What are the latest LD release notes?", {"kind": "user", "key": "user-123"})

Environment Variables