LaunchDarkly AI SDK — optimization

[!CAUTION] This package is in pre-release and not subject to backwards compatibility guarantees. The API may change based on feedback.

Pin to a specific minor version and review the changelog before upgrading.

This package provides helpers for running iterative AI prompt optimization workflows from within LaunchDarkly SDK-based applications. It drives the optimization loop — generating candidate variations, evaluating them with judges, and optionally committing winners back to LaunchDarkly — while delegating all LLM calls to your own handler functions.

Requirements

• Python >=3.9
• A configured LaunchDarkly server-side SDK client
• The LaunchDarkly AI package (launchdarkly-server-sdk-ai>=0.16.0) — pulled in automatically as a dependency
• LAUNCHDARKLY_API_KEY environment variable — required only when using auto_commit=True or optimize_from_config. Not needed for basic optimize_from_options runs without auto-commit.

[!NOTE] LAUNCHDARKLY_API_KEY is used exclusively for discrete LaunchDarkly REST API calls (fetching configs, publishing results). It is never included in any LLM prompt and is never forwarded to your handler callbacks. All API calls made by this package are isolated; they have no access to your runtime environment beyond the key you explicitly provide via the environment variable.

Installation

pip install launchdarkly-ai-optimizer

Quick Start

Basic optimization (optimize_from_options)

No LAUNCHDARKLY_API_KEY required unless auto_commit=True.

import ldclient
from ldai import LDAIClient
from ldai_optimizer import (
    OptimizationClient,
    OptimizationJudge,
    OptimizationOptions,
    OptimizationResponse,
    LLMCallConfig,
    LLMCallContext,
)

ldclient.set_config(ldclient.Config("sdk-your-sdk-key"))
ld = LDAIClient(ldclient.get())
client = OptimizationClient(ld)

def handle_llm_call(
    run_id: str,
    config: LLMCallConfig,
    context: LLMCallContext,
    is_evaluation: bool,
) -> OptimizationResponse:
    # config.model, config.instructions, config.key are available
    # context.user_input, context.current_variables are available
    response = your_llm_client.chat(
        model=config.model.name if config.model else "gpt-4o",
        system=config.instructions,
        user=context.user_input or "",
    )
    return OptimizationResponse(completion=response.text)

result = await client.optimize_from_options(
    OptimizationOptions(
        agent_key="my-agent",
        handle_agent_call=handle_llm_call,
        judge_model="gpt-4o-mini",
        judges={
            "quality": OptimizationJudge(
                threshold=1.0,
                acceptance_statement="The response is accurate and concise.",
            )
        },
        model_choices=["gpt-4o", "gpt-4o-mini"],
        variable_choices=[{"user_id": "user-123"}],
        user_input_choices=["What is my account balance?"],
    )
)

Ground truth optimization

from ldai_optimizer import GroundTruthOptimizationOptions, GroundTruthSample

result = await client.optimize_from_options(
    GroundTruthOptimizationOptions(
        agent_key="my-agent",
        handle_agent_call=handle_llm_call,
        judge_model="gpt-4o-mini",
        judges={
            "accuracy": OptimizationJudge(
                threshold=1.0,
                acceptance_statement="The response matches the expected answer.",
            )
        },
        model_choices=["gpt-4o", "gpt-4o-mini"],
        ground_truth_responses=[
            GroundTruthSample(
                user_input="What is 2+2?",
                ground_truth_response="4",
            )
        ],
    )
)

Config-driven optimization (optimize_from_config)

Requires LAUNCHDARKLY_API_KEY.

from ldai_optimizer import OptimizationFromConfigOptions

result = await client.optimize_from_config(
    OptimizationFromConfigOptions(
        config_key="my-optimization-config",
        project_key="my-project",
        handle_agent_call=handle_llm_call,
        auto_commit=True,
    )
)

License

Apache-2.0