AutoGen is a Python framework for building AI agents and multi-agent applications, offering components from a no-code UI for prototyping to an event-driven core for scalable, distributed systems. It supports various agentic workflows and integrations via extensions.

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.

• +Supports building conversational single and multi-agent applications.
• +Provides an event-driven core for scalable, distributed multi-agent AI systems.
• +Offers multiple abstraction levels, including a no-code UI (AutoGen Studio), Python scripting (AgentChat), and a foundational core.
• +Facilitates deterministic and dynamic agentic workflows for business processes or research.
• +Includes extensions for external services, such as OpenAI models, Model-Context Protocol (MCP) servers, and Docker-based code execution.
• +Supports distributed agents through runtimes like GrpcWorkerAgentRuntime.

• +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.

• −Requires Python 3.10 or newer, which might restrict compatibility for projects on older Python versions.
• −The framework's modularity, while powerful, may present a learning curve for understanding its various components (Core, AgentChat, Studio, Extensions).
• −Focus on multi-agent systems could introduce overhead and complexity for simpler, single-agent automation tasks.
• −External service integrations, such as OpenAI, require specific extension installations (e.g., autogen-ext[openai]).

• −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.

Agents manage their state through conversational context and event-driven interactions, facilitating communication and collaboration within multi-agent systems.

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