LangGraph vs LlamaIndex
A side-by-side comparison of capabilities, autonomy, integrations, and pricing to help you choose.
Short answer: choose LangGraph if you want low-level framework for stateful, durable, graph-based llm agents (Supervised agent, freemium); choose LlamaIndex if you want open-source data framework for rag pipelines and data-grounded agents (Supervised agent, freemium).
| LangGraph | LlamaIndex | |
|---|---|---|
| What it is | Low-level framework for stateful, durable, graph-based LLM agents | Open-source data framework for RAG pipelines and data-grounded agents |
| Type | framework | framework |
| Autonomy | Supervised agent | Supervised agent |
| Pricing | freemium · Framework free (MIT); LangGraph Platform via LangSmith (free Developer tier) | freemium · Framework free (MIT); LlamaCloud has a free tier |
| Best for | developers, enterprise, mid-market | developers, enterprise, mid-market |
| Deployment | self-hosted, api, saas | self-hosted, api, saas |
| Modalities | text, code, api | text, code, api |
| Models | model-agnostic, gpt, claude, gemini, open-source | model-agnostic, gpt, claude, open-source |
| Protocols | function-calling, mcp, rest-api | function-calling, mcp, rest-api |
| Integrations | OpenAI, Anthropic, Google, AWS Bedrock, LangSmith | OpenAI, Anthropic, Pinecone, Qdrant, AWS Bedrock, Hugging Face |
| Capabilities | 4 documented | 4 documented |
LangGraph
- +Explicit graph model makes complex agent control flow (loops, branching, multi-agent routing) inspectable and controllable
- +Production-grade primitives: durable execution, checkpointing/time-travel, and first-class human-in-the-loop interrupts
- +Open source and model-agnostic, with a hosted LangGraph Platform and LangSmith observability for deployment
- -Lower-level and more verbose than higher-level agent libraries; a steeper learning curve
- -Framework, not a product: autonomy and quality depend entirely on what the developer builds
LlamaIndex
- +Best-in-class data and retrieval primitives (readers, indexes, retrievers, query engines) for grounding agents in your own data
- +Event-driven Workflows orchestrate multi-step agent processes with reflection and error-correction
- +Open source and model-agnostic, with LlamaCloud for managed document parsing and indexing
- -Framework, not a product: autonomy and quality depend entirely on what the developer builds
- -More oriented to data/RAG than to complex multi-agent orchestration compared with some peers
Which should you choose?
LangGraph is low-level framework for stateful, durable, graph-based llm agents, best for developers, enterprise, mid-market. LlamaIndex is open-source data framework for rag pipelines and data-grounded agents, best for developers, enterprise, mid-market. The right choice depends on the autonomy level you want, your existing integrations, and your budget, all compared above.
This comparison is generated from the sourced LangGraph and LlamaIndex profiles. Open either profile to review its evidence and last-reviewed date.