AutoGen is a Python framework for developing AI agents and applications, ranging from no-code prototyping to scalable multi-agent systems. It supports conversational AI, dynamic workflows, and distributed agent collaboration through its modular, event-driven architecture.

LangGraph is a low-level orchestration framework for constructing stateful, long-running AI agents. It offers fine-grained control to combine deterministic and LLM-driven steps, providing durable execution, persistence, and human-in-the-loop capabilities. This framework is ideal for bespoke agent workflows, but it is not a high-level abstraction.

• +Provides a web-based UI for prototyping agents without writing code via AutoGen Studio.
• +Offers a programming framework for building conversational single and multi-agent applications using AgentChat.
• +Features an event-driven core framework designed for scalable multi-agent AI systems.
• +Supports deterministic and dynamic agentic workflows suitable for business processes.
• +Facilitates research into multi-agent collaboration paradigms.
• +Enables distributed agents, supporting multi-language applications.
• +Highly extensible, allowing integration with external services and libraries through built-in and custom extensions.

• +Provides fine-grained control to mix deterministic, hand-coded steps with LLM-driven agentic steps in a single graph.
• +Enables durable execution, allowing agents to persist through failures and resume from where they left off.
• +Supports human-in-the-loop functionality for inspecting and modifying 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 visibility into complex agent behavior, tracing execution paths, and capturing state transitions.
• +Provides low-level infrastructure supporting production-ready deployment of stateful workflows.

• −Operates at a very low level of abstraction, requiring more manual configuration than higher-level frameworks.
• −Does not abstract prompts or architectural patterns, placing responsibility on the developer.
• −Requires familiarity with underlying components like models and tools before effective use.
• −Higher-level abstractions, such as LangChain's agents, are recommended for beginners or simpler use cases.

AutoGen manages state through conversational contexts and event-driven interactions between agents.

LangGraph manages state through its `StateGraph` mechanism, enabling durable execution, persistence, and comprehensive memory for long-running, stateful agents.