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LangGraph vs LlamaIndex

LangGraphLlamaIndex

Bottom line: LangGraph for engineering teams building production agents; LlamaIndex for document-heavy RAG applications.

LangGraphLangGraph

Graph-based orchestration for stateful, controllable LLM agents.

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LlamaIndexLlamaIndex

Data framework for LLM apps and knowledge agents

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Votes00
PricingFreemiumFreemium
CategoryAgent FrameworksAi Agents
Tags
agentsorchestrationllm-frameworkopen-sourceworkflow
llm-frameworkragdocument-parsingopen-sourceknowledge-agents
Best for
  • Engineering teams building production agents
  • Developers needing controllable, resumable workflows
  • Teams already invested in LangChain
  • Document-heavy RAG applications
  • Enterprise knowledge agents
  • Teams needing reliable PDF parsing
Pros
  • Explicit, debuggable control over agent state and flow
  • MIT-licensed core, free to self-host
  • Model-agnostic, not tied to one LLM vendor
  • Strong support for cycles, checkpointing, and human-in-the-loop
  • Backed by the widely used LangChain ecosystem
  • Open-source frameworks are free (MIT)
  • Strong focus on retrieval and data quality
  • LlamaParse excels at complex document parsing
  • Managed LlamaCloud for production data workflows
  • Python and TypeScript support
Cons
  • Steeper learning curve than high-level agent builders
  • Requires comfort with graph and state concepts
  • Paid platform and observability are separate products
  • Documentation and APIs have evolved quickly
  • Can be overkill for simple single-shot LLM tasks
  • Overlaps with LangChain, adding decision fatigue
  • LlamaCloud credit costs scale with document complexity
  • Framework surface area has grown large
  • Fast-moving APIs can change between versions
  • Advanced agent features less mature than dedicated agent frameworks

Comparison generated from each tool's listing. Add or remove tools above to change it.