LangGraph is a low-level orchestration framework for building stateful, long-running agents, offering fine-grained control over mixed deterministic and LLM-driven steps. It emphasizes durable execution, persistence, and human-in-the-loop capabilities, integrating with LangChain components for models and tools.

Pydantic AI is a Python SDK for building typed, extensible AI agents that integrate with virtually any model and provider. It supports various interfaces and durable execution, enabling complex, long-running agent workflows with robust observability.

• +Offers fine-grained control to mix deterministic, hand-coded steps with LLM-driven agentic steps in a single graph.
• +Provides durable execution, allowing agents to persist through failures and resume operations.
• +Supports human-in-the-loop interaction, enabling inspection and modification of agent state at any point.
• +Includes comprehensive memory for both short-term working memory and long-term memory across sessions.
• +Integrates with LangSmith for deep visibility, tracing, debugging, and production-ready deployment of agents.
• +Can be used standalone without requiring the broader LangChain framework.

• +Supports virtually every model and provider, swappable with a string or through Pydantic AI Gateway for failover and cost monitoring.
• +Provides structured outputs, typed dependency injection, and typed tools, moving errors from runtime to write-time, with Pydantic Graph for typed workflows.
• +OpenTelemetry-native instrumentation integrates with Pydantic Logfire for real-time debugging, tracing, and cost tracking, and uses Pydantic Evals for testing agent behavior.
• +Uses `capabilities` as reusable units for tools, instructions, and model settings, supporting composition of complex agents like Coder and Researcher.
• +Agents can run across various interfaces, including CLI, built-in web chat, realtime speech, UI event streams (AG-UI, Vercel AI), and as an editor agent.
• +Offers first-party, co-maintained durable execution on Temporal, DBOS, or Prefect, allowing agents to survive restarts and long waits with human-in-the-loop approval.
• +Built by the Pydantic team, leveraging its validation layer which is foundational for major AI SDKs and FastAPI, ensuring robust data handling.

• −It is a very low-level framework, requiring familiarity with agent components like models and tools.
• −It does not abstract prompts or agent architecture, focusing solely on orchestration.
• −Beginners or those seeking higher-level abstractions may find it too complex and are recommended to use LangChain's prebuilt agents.
• −Requires explicit integration with external components for LLM models and tools.

• −The ACP editor agent interface is marked as experimental, indicating it may not be fully stable or production-ready.
• −The `llms-full.txt` format, designed for LLMs, may be too large for some LLMs, limiting its utility for certain integrations.

LangGraph manages state for long-running agents by providing durable execution, persistence, and comprehensive memory for both short-term reasoning and long-term sessions.

The framework manages agent context and execution longevity using capabilities for memory and context management, and supports durable execution via integrations like Temporal to survive restarts and long waits.