LangChain vs. LlamaIndex – Definition and Differences
Compare LangChain and LlamaIndex: their centre of gravity, how each handles retrieval and agents, where they overlap, and how to choose.
Both are open-source frameworks for building applications on language models, and both now cover retrieval and agents. They differ in what they were built around first, which still shows in their abstractions.
1. What Each One Is
LangChain is a general composition framework: model adapters, prompt templates, output parsers, tools, memory, and a runnable interface for piping components together, with a graph runtime for cyclic agent behaviour.
LlamaIndex started as a data framework for retrieval: ingesting documents, indexing them in several structures, and querying them well. Agent and workflow features were added on that foundation.
2. Centre of Gravity
| Aspect | LangChain | LlamaIndex |
|---|---|---|
| Built around | Composing model calls and tools | Ingesting and querying documents |
| Retrieval | One capability among many | The core abstraction |
| Index types | Vector store integrations | Vector, summary, keyword, tree, knowledge graph |
| Agents | Graph runtime with explicit state | Workflow and agent abstractions over query engines |
| Connectors | Broad tool and provider coverage | Large library of document readers |
3. Where They Overlap
Both can load documents, chunk them, embed them, retrieve passages, call tools, and run multi-step agents. Both support the major model providers and vector stores. For a standard question-answering application over a document set, either will do the job.
The overlap is large enough that framework choice is rarely the reason a project succeeds or fails.
4. Where LangChain Fits Better
- Applications where retrieval is one part of a larger flow with several tools.
- Cyclic agent behaviour needing explicit state and conditional transitions.
- Provider portability, where swapping models is a design requirement.
- Teams wanting one composition idiom across prompting, tools, and parsing.
5. Where LlamaIndex Fits Better
- Document-centric applications where retrieval quality is the product.
- Corpora needing more than flat vector similarity — summarisation over sections, hierarchical indexes, graph-shaped relationships.
- Heavy ingestion needs, where its connector library saves real work.
- Query-engine composition, such as routing between several indexes.
6. Practical Considerations
- Both move quickly. Pin versions and expect churn between releases.
- Both add indirection. For a single prompt against one provider, a direct SDK call is shorter and easier to debug.
- They interoperate. Using one for ingestion and retrieval and the other for orchestration is common and reasonable.
- Retrieval quality is not a framework property. Chunking, extraction, and re-ranking decide it, and both frameworks let you get those wrong.
7. How to Choose
Ask what the application is mostly doing. If it is answering questions over a document corpus, start with the retrieval-first framework. If it is coordinating tools and models with retrieval as one step, start with the composition-first one.
If the answer is "both", pick one for the primary axis and accept that the other could have worked — that decision is far less consequential than the data pipeline underneath it.
8. Summary
LangChain is a composition framework that includes retrieval; LlamaIndex is a retrieval framework that has grown orchestration. Their capabilities overlap substantially and their idioms differ.
Choose on the application's centre of gravity, pin versions, and remember that the quality of the answers will be decided by extraction and chunking rather than by which framework wraps them.