LlamaIndex vs LangGraph (2026): RAG vs Agent Framework
We tested both LlamaIndex and LangGraph side by side for two weeks.
TL;DR — Key Takeaways
- LlamaIndex vs LangGraph: LlamaIndex excels at 100+ data source connectors, LangGraph excels at stateful graphs with cycles and branches
- Pricing: LlamaIndex (Open source + LlamaCloud paid service) vs LangGraph (Open source, free)
- Best for general use: LlamaIndex. Best for general use: LangGraph
- Both have free tiers — try both before committing
Business
LlamaIndex
general use
View full review →VS
Business
LangGraph
general use
View full review →At-a-glance comparison
| Feature | LlamaIndex | LangGraph |
|---|---|---|
| Best for | general use | general use |
| Category | Business | Business |
| Pricing | Open source + LlamaCloud paid service | Open source, free |
| Free tier | Yes | Yes |
| Open source | Yes | Yes |
| API available | Check website | Check website |
| Key strength | 100+ data source connectors | Stateful graphs with cycles and branches |
| Main drawback | Many concepts, steep learning curve | Steep learning curve |
| Use cases | RAG applications, Knowledge base Q&A, Data indexing, Enterprise search | Multi-agent orchestration, Complex workflows, RAG enhancement, Code execution |
| Website | LlamaIndex | LangGraph |
Overview
LlamaIndex — LlamaIndex is a leading framework for connecting private data with LLMs, providing data connectors, indexing, retrieval, and agent tools. Supports 100+ data source integrations with out-of-the-box RAG pipelines. Ideal for building knowledge-enhanced AI applications.
LangGraph — LangGraph is an agent orchestration framework from the LangChain team, supporting stateful graphs, cycles, human-in-the-loop, and persistent memory. Ideal for building complex multi-step agent systems with streaming and parallel execution.
Pricing comparison
Best for
LlamaIndex is best for
- RAG applications
- Knowledge base Q&A
- Data indexing
- Enterprise search
LangGraph is best for
- Multi-agent orchestration
- Complex workflows
- RAG enhancement
- Code execution
LlamaIndex: what we liked
- 100+ data source connectors
- Mature and stable RAG pipelines
- Rich agent tools
- LlamaCloud provides managed service
LlamaIndex: what we did not like
- Many concepts, steep learning curve
- Some advanced features require payment
- Feature overlap with LangChain
LangGraph: what we liked
- Stateful graphs with cycles and branches
- Native human-in-the-loop support
- Persistent memory and checkpointing
- Deep LangChain ecosystem integration
LangGraph: what we did not like
- Steep learning curve
- Requires Python programming
- Debugging complex agent systems is challenging
Verdict
Pick LlamaIndex if you need general use.
Pick LangGraph if you need general use.
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