AutoGen is a Python framework for building AI agents and applications, offering layered components for prototyping, conversational agents, and scalable multi-agent systems. It supports deterministic and dynamic agentic workflows, with extensibility for external services and custom components. Developers can choose between a no-code UI, a Python agent chat framework, or an event-driven core for complex systems.

LangGraph is a low-level Python framework for orchestrating stateful, long-running AI agents using a graph-based approach. It focuses on capabilities like durable execution, comprehensive memory, and human-in-the-loop interactions. While it integrates with LangChain components, it provides fine-grained control for complex agent designs.

• +Provides AutoGen Studio, a web-based UI for prototyping agents without writing code.
• +Offers AgentChat, a programming framework for building conversational single and multi-agent applications.
• +Built on Core, an event-driven framework for building scalable multi-agent AI systems.
• +Supports deterministic and dynamic agentic workflows for business processes.
• +Enables research on multi-agent collaboration and distributed agents for multi-language applications.
• +Features an extension mechanism to interface with external services and libraries.
• +Includes built-in extensions for using Model-Context Protocol (MCP) servers and OpenAI Assistant API.
• +Supports running model-generated code in a Docker container via a built-in extension.
• +Facilitates distributed agents via GrpcWorkerAgentRuntime.

• +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 Observability for deep visibility into agent behavior, tracing, and debugging.
• +Designed for production-ready deployment of scalable, stateful, long-running agent systems.
• +Can be used standalone without relying on other LangChain components.

• −Requires Python 3.10 or newer, which may limit compatibility with older environments.

• −It is a very low-level orchestration framework, requiring developers to understand underlying agent components.
• −It does not abstract prompts or architecture, demanding manual definition of these elements.
• −Not recommended for beginners or those seeking a higher-level abstraction, as LangChain's agents offer prebuilt architectures.
• −Requires familiarity with components like models and tools before effective use.

AutoGen supports conversational and multi-agent applications, requiring state management to maintain context throughout interactions.

LangGraph manages state through its `StateGraph` abstraction, enabling the definition of custom state objects like `MessagesState` for persisting agent progress and memory across executions.