Comparison
LangChain vs LlamaIndex
| LangChain | LlamaIndex | |
|---|---|---|
| Stars | 147,380 | 52,383 |
| License | 📜 MIT | 📜 MIT |
| Status | Active | Active |
| Momentum |
|
|
| Category | ai, agents, orchestration | ai, llm, rag |
LangChain
Pros
- Fast to prototype an LLM-backed feature
- Actively maintained, frequent releases
- Works with nearly any model provider, not locked to one vendor
Cons
- Abstraction layers can make it harder to see (or control) exactly what prompt gets sent
- API has changed significantly across major versions, so older tutorials can mislead
- For a single, well-understood use case, calling a provider's SDK directly is often simpler
LlamaIndex
Pros
- Retrieval and document indexing are the primary design goal, not a feature bolted onto a broader orchestration framework
- Broad vector store and embedding-provider support across its integration ecosystem
- Active development and a large, fast-growing community
Cons
- Python-first: the flagship `llama_index` package is the primary, most complete surface, so non-Python stacks have a narrower integration path
- Heavier orchestration/indexing framework than is needed for a single well-understood retrieval use case, where calling a vector store and an LLM API directly can be simpler
- The project's own hosted offerings (LlamaParse, LlamaCloud) are separate paid services — useful for production document parsing at scale, but a reason to check whether a given feature is open-source or hosted before relying on it
How they differ
Both are MIT-licensed, actively maintained frameworks for building LLM-powered applications, and each lists the other as its open-source alternative — the split is in what each one is built around first.
LangChain is built around general-purpose orchestration: a common set of abstractions — prompts, chains, memory, tool-calling, retrieval — for composing LLM calls into larger applications across dozens of model providers, vector stores, and tools behind one API. That breadth is also its main tradeoff: the abstraction layers can make it harder to see (or control) exactly what prompt gets sent, and its API has changed significantly across major versions, so older tutorials can mislead.
LlamaIndex is built around retrieval first: data connectors, indexing structures, and
query interfaces purpose-built for retrieval-augmented generation (RAG) — pulling in
documents, APIs, or databases, building an index over them, and querying that index so
an LLM's answers are grounded in your own data. It supports building agents on top of
that retrieval layer too, but document ingestion, parsing, and indexing are the core of
the project, and the flagship llama_index package is Python-first, so non-Python
stacks have a narrower integration path. Its own hosted offerings (LlamaParse,
LlamaCloud) are separate paid services on top of the open-source core.
In short: reach for LangChain when the application needs general-purpose orchestration — chains, memory, and agents — across whichever providers and tools it touches; reach for LlamaIndex when the job is fundamentally retrieval — getting your own documents and data indexed and queryable by an LLM — and that's the primary thing being built.