pydantic-ai

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.

Google ADK is a code-first Python toolkit for building, evaluating, and deploying AI agents. Its Agent and Workflow APIs support graph-based routing, nested workflows, and structured agent-to-agent tasks, while the CLI can run an agent locally or open a web UI for an agent directory. Version 2.0 changes the agent API, event model, and session schema, so existing applications need an upgrade check.

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.