LangGraph is a low-level, graph-based orchestration framework for building robust, stateful AI agents in Python. It provides core runtime capabilities like durable execution, human-in-the-loop support, and comprehensive memory, but requires explicit management of prompts and agent architecture. It integrates with LangChain components and LangSmith for observability and deployment.

Pydantic AI is a Python agent framework for building production-grade Generative AI applications and workflows, emphasizing type safety, observability, and durable execution. It is model-agnostic, integrating with various LLM providers and offering extensive capabilities like web search, tool approval, and graph support. The framework aims to bring the ergonomic development experience of FastAPI to GenAI.

• +Provides durable execution, allowing agents to persist through failures and resume from where they left off.
• +Supports human-in-the-loop workflows, enabling inspection and modification of agent state at any point.
• +Offers comprehensive memory capabilities for both short-term working memory and long-term memory across sessions.
• +Integrates with LangSmith for deep debugging visibility, tracing execution paths, and capturing state transitions.
• +Designed for production-ready deployment of scalable, stateful, long-running agent systems via LangSmith.

• +Built by the Pydantic Team, leveraging widely adopted Pydantic Validation.
• +Model-agnostic, supporting numerous LLM providers and custom model implementations.
• +Seamless observability through tight integration with Pydantic Logfire, compatible with other OpenTelemetry platforms.
• +Fully type-safe design enhances auto-completion and type checking, shifting errors to write-time.
• +Powerful evaluation capabilities allow systematic testing and performance monitoring of agentic systems.
• +Extensible by design, supporting composable capabilities, a Harness library, third-party packages, and YAML/JSON agent definitions.
• +Integrates with Model Context Protocol (MCP), Agent2Agent (A2A), and UI event stream standards for broad interoperability.
• +Provides human-in-the-loop tool approval for flagged tool calls based on context.
• +Supports durable execution to preserve agent progress across failures and manage long-running workflows.
• +Offers streamed structured outputs with immediate validation for real-time data access.
• +Includes graph support to define complex applications with type hints, preventing spaghetti code.
• +Utilizes dependency injection via `RunContext` for type-safe customization and simplified testing.
• +Guarantees structured outputs conform to Pydantic models, including schema generation and validation with self-correction.

• −Is a very low-level framework, focusing solely on orchestration and not abstracting prompts or agent architecture.
• −Requires familiarity with underlying components like models and tools, potentially increasing complexity for beginners.
• −Recommends higher-level abstractions like LangChain agents for those just starting or seeking simpler solutions.

LangGraph manages state through a graph structure, supporting durable execution, persistence, comprehensive memory for both short-term and long-term context, and the ability to inspect and modify agent state.

Agents preserve their progress across failures and restarts via durable execution, while `RunContext` manages runtime dependencies and contextual data passed into instructions and tool functions.