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.

LlamaIndex is a Python framework for building LLM-powered applications, especially agents and workflows, by integrating proprietary data. It provides tools for data ingestion, indexing, and natural language querying through context augmentation. This allows developers to quickly prototype and deploy complex LLM solutions that leverage private datasets.

• +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.

• +Provides a comprehensive framework for context-augmented LLM applications, from prototype to production.
• +Offers high-level APIs for quick data ingestion and querying (5 lines of code) alongside extensive low-level customization.
• +Includes robust data connectors for ingesting diverse data sources like APIs, PDFs, and SQL databases.
• +Supports advanced LLM applications, including autonomous agents, multi-modal capabilities, and fine-tuning.
• +Facilitates complex, event-driven workflows with reflection and error-correction for sophisticated LLM tasks.
• +Integrates observability and evaluation tools for rigorous application experimentation and monitoring.
• +Offers managed cloud services (LlamaCloud) for enterprise-grade document parsing, extraction, indexing, and retrieval.

• −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.

• −Requires an OpenAI API key for its 30-second quickstart, indicating a default reliance on commercial LLM services.
• −Implementing complex, multi-agent workflows with reflection and error-correction demands significant architectural design and setup.

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

LlamaIndex manages state through its event-driven workflows, which orchestrate multi-step processes, agents, and data interactions with features like reflection and error-correction.