LangChain vs LlamaIndex
LangChain and LlamaIndex both answer the same question: how do you call models and build agents? The large orchestration framework for chains, agents and retrieval, with integrations for nearly every tool and store. Framework focused on getting your documents into a model's context: ingestion, chunking, indexing and retrieval. Both cover the ai sdk layer competently, so the decision comes down to which set of tradeoffs you would rather live with for the next two years.
Verdict
Both cover the ai sdk layer competently, so the decision comes down to which set of tradeoffs you would rather live with for the next two years.
Pick LangChain if
- ✓Integration catalogue covers almost every vector store, loader and model you might need.
- ✓Graph-based agent orchestration handles cycles and human approval steps.
- ✓Available in both Python and TypeScript with broadly matching concepts.
Pick LlamaIndex if
- ✓Document loaders and chunking strategies are the deepest of any framework.
- ✓Retrieval patterns like reranking and hybrid search come prebuilt.
- ✓Handles messy PDFs and tables better than rolling your own parser.
| Comparison | LangChain | LlamaIndex |
|---|---|---|
| Pricing shape | Free and open source, with paid observability and deployment products sold alongside it. | Free and open source, with hosted parsing and cloud indexing billed by usage. |
| Frameworks | Next.js, Django | Next.js, Django |
| In one line | The large orchestration framework for chains, agents and retrieval, with integrations for nearly every tool and store. | Framework focused on getting your documents into a model's context: ingestion, chunking, indexing and retrieval. |
Pricing described qualitatively because published plans change often. Checked 2026-08-23. Confirm current terms on LangChain and LlamaIndex.
LangChain
Strengths
- ✓Integration catalogue covers almost every vector store, loader and model you might need.
- ✓Graph-based agent orchestration handles cycles and human approval steps.
- ✓Available in both Python and TypeScript with broadly matching concepts.
- ✓Enormous community, so prototypes for unusual pipelines usually already exist.
Tradeoffs
- ✗Heavy abstraction hides prompts, making debugging harder than direct calls.
- ✗The dependency tree is large and integrations vary widely in maintenance quality.
- ✗API churn across versions has repeatedly broken working code.
- ✗Easy to adopt the whole framework when a plain HTTP call would do.
LlamaIndex
Strengths
- ✓Document loaders and chunking strategies are the deepest of any framework.
- ✓Retrieval patterns like reranking and hybrid search come prebuilt.
- ✓Handles messy PDFs and tables better than rolling your own parser.
- ✓Query engines compose over multiple indexes without custom routing code.
Tradeoffs
- ✗Centred on retrieval, so general agent workflows fit less naturally.
- ✗Many overlapping abstractions make the right entry point unclear at first.
- ✗Best document parsing is a paid hosted service, not the open source path.
- ✗Python remains the primary target, with the TypeScript port lagging behind.
Frequently asked questions
Is LangChain or LlamaIndex better?
Neither is better in the abstract. Both cover the ai sdk layer competently, so the decision comes down to which set of tradeoffs you would rather live with for the next two years. Pick the one whose downside you can absorb, because both upsides are real.
What is the main drawback of LangChain?
Heavy abstraction hides prompts, making debugging harder than direct calls. The dependency tree is large and integrations vary widely in maintenance quality.
What is the main drawback of LlamaIndex?
Centred on retrieval, so general agent workflows fit less naturally. Many overlapping abstractions make the right entry point unclear at first.
Can you switch from one to the other later?
Usually, at a cost that grows with how much of your product leans on the ai sdk layer. Plan for it in the data model, not in the framework, and the switch stays survivable.