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.

LlamaIndex is a Python and TypeScript framework for building LLM-powered applications that leverage private or proprietary data through context augmentation. It offers tools for data ingestion, indexing, querying, and agent orchestration, supporting workflows from simple RAG to complex autonomous agents.

• +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.

• +High-level API enables quick start with 5 lines of code for data ingestion and querying.
• +Low-level APIs allow extensive customization and extension of core modules, including connectors, indices, and engines.
• +Comprehensive tools for data ingestion from various sources and formats via data connectors.
• +Structures data into efficient intermediate representations using data indexes for LLMs.
• +Provides query and chat engines for natural language access to augmented data.
• +Supports LLM-powered agents augmented by tools and API integrations for complex tasks.
• +Includes observability and evaluation integrations for rigorous application monitoring and experimentation.
• +Features event-driven workflows for combining agents, data, and tools, offering flexibility over graph-based approaches.
• +Offers managed services via LlamaCloud for enterprise-grade parsing, extraction, indexing, and retrieval.

• −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.

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

Workflows orchestrate multi-step processes for agents, data connectors, and tools, featuring event-driven execution with reflection and error-correction for complex LLM applications.