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.