adk-python

LangChain provides common interfaces and modular components for building agents and other LLM-powered applications. Developers can connect models, embeddings, vector stores, retrievers, and third-party tools while swapping providers without rebuilding the whole application. The core framework works on its own; LangGraph handles lower-level orchestration, while LangSmith products support debugging, evaluation, and deployment workflows.

MLflow records and manages the development and operation of LLM applications, AI agents, and machine-learning models. Teams can start a server, enable automatic tracing, inspect calls and metrics in the UI, and use evaluation, prompt optimization, experiment tracking, model registry, and deployment workflows to improve their systems. It can run locally, on private infrastructure, or in cloud environments, making it useful when generative AI and traditional model lifecycles need a shared operational layer.

Pydantic AI is a Python framework for building agents with typed instructions, dependencies, tool calls, and structured outputs. It supports many model providers, MCP integrations, streamed results, tool-call approval, durable execution, and graph-based workflows. This makes it a fit for developers who want agent behavior checked by Python types and evaluated over time.

Agent Lightning connects existing agents to optimization workflows with minimal code changes by collecting prompts, tool calls, and rewards as structured traces. It can selectively optimize one or more agents built with LangChain, the OpenAI Agent SDK, AutoGen, CrewAI, Microsoft Agent Framework, or plain Python. Supported approaches include reinforcement learning, automatic prompt optimization, and supervised fine-tuning.