| Package | PyPI | Docs |
|---|---|---|
| launchdarkly-server-sdk-ai |  |
Reference |
| launchdarkly-server-sdk-ai-openai |  |
Reference |
| launchdarkly-server-sdk-ai-langchain |  |
Reference |
| launchdarkly-observability |  |
Reference |
Each example is a self-contained application you can run independently to explore LaunchDarkly's AI APIs hands-on. Pick one that matches your provider or use case, follow the README, and you'll be up and running in minutes.
For more comprehensive instructions, visit the Quickstart page or the Python reference guide.
These examples show how to integrate LaunchDarkly AI with different providers using completion_config and agent_config.
| Example | Description |
|---|---|
| Bedrock | completion_config with AWS Bedrock, metrics tracking |
| Gemini | completion_config with Google Gemini, metrics tracking |
| LangChain | completion_config with LangChain, async metrics tracking |
| LangGraph Agent | agent_config with a single LangGraph ReAct agent, tool calling, metrics tracking |
| LangGraph Multi-Agent | agent_config with multiple LangGraph agents, custom StateGraph workflow, per-node metrics |
| OpenAI | completion_config with OpenAI, automatic metrics tracking |
These examples demonstrate LaunchDarkly's managed APIs and standalone capabilities.
| Example | Description |
|---|---|
| Judge | create_judge for standalone evaluation of AI responses |
| Managed Agent | create_agent with tool calling, automatic metrics tracking, and judge evaluation |
| Managed Agent Graph | create_agent_graph with multi-node workflows, tool calling, per-node metrics, and judge evaluation |
| Managed Model | create_model with managed chat, automatic metrics tracking, and judge evaluation |