Decision comparison
LangChain vs LlamaIndex
LangChain and LlamaIndex are both frameworks you build an LLM application on, and they are organised around different halves of the problem. LangChain's centre is orchestration: chains, tools, memory and — through LangGraph — explicit stateful graphs with checkpointing. LlamaIndex's centre is retrieval: ingestion, chunking, indexing, and a wide set of retrieval strategies with reranking and query routing built in.
Architecture choice. These take different approaches to the same problem. Read the table as a fit question rather than a feature race.
Applies to: Building an LLM application, and whether the centre of it is retrieval or orchestration.
All 2 are agent frameworks.
Quick Comparison
| Decision factor | LangChain | LlamaIndex |
|---|---|---|
| What it is | A framework for LLM applications built around composable chains, tools, memory and agents, with a large integration catalogue | A data framework for LLM applications built around ingestion, chunking, indexing and retrieval |
| Centre of gravity | Orchestration: what runs, in what order, with which tools and what memory | Retrieval: getting the right context in front of the model |
| Retrieval primitives | Retrievers and vector store integrations, assembled by you | Vector, keyword, hybrid, recursive and graph retrieval, with reranking and query routing built in |
| Agent support | Agents plus LangGraph for explicit stateful orchestration with checkpointing | Agent abstractions over its own retrieval and tool layer |
| Managed service | LangSmith for tracing and evaluation; LangGraph Platform for hosting stateful agents | LlamaCloud for managed parsing and indexing, with LlamaParse for documents |
| Licensing | Open source, with paid managed tiers | Open source under MIT, with paid LlamaCloud tiers |
| Best fit | Applications whose difficulty is control flow, tools and state | Applications whose difficulty is finding the right context in your own documents |
LangChain
- What it is:
- A framework for LLM applications built around composable chains, tools, memory and agents, with a large integration catalogue
- Centre of gravity:
- Orchestration: what runs, in what order, with which tools and what memory
- Retrieval primitives:
- Retrievers and vector store integrations, assembled by you
- Agent support:
- Agents plus LangGraph for explicit stateful orchestration with checkpointing
- Managed service:
- LangSmith for tracing and evaluation; LangGraph Platform for hosting stateful agents
- Licensing:
- Open source, with paid managed tiers
- Best fit:
- Applications whose difficulty is control flow, tools and state
LlamaIndex
- What it is:
- A data framework for LLM applications built around ingestion, chunking, indexing and retrieval
- Centre of gravity:
- Retrieval: getting the right context in front of the model
- Retrieval primitives:
- Vector, keyword, hybrid, recursive and graph retrieval, with reranking and query routing built in
- Agent support:
- Agent abstractions over its own retrieval and tool layer
- Managed service:
- LlamaCloud for managed parsing and indexing, with LlamaParse for documents
- Licensing:
- Open source under MIT, with paid LlamaCloud tiers
- Best fit:
- Applications whose difficulty is finding the right context in your own documents
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | LangChain | LlamaIndex |
|---|---|---|
| GitHub commits, 90d(Product adoption) | 541 | Not available |
| GitHub stars(Product adoption) | 146,000+ | Not available |
| Search interest(Market interest) | 14 | Not available |
| Hacker News mentions, 90d(Community interest) | 31 | Not available |
| npm weekly downloads(Product adoption) | 2.1M | Not available |
| PyPI weekly downloads(Product adoption) | 38.2M | Not available |
| Stack Overflow questions(Community interest) | 2.0k | Not available |
As of September 14, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
LangChain
September 14, 2026Package vulnerabilities
npm · langchain@1.5.11 · PyPI · langchain@1.4.0
0 vulnerabilities
across 2 packages
Repository security score
Not available
LlamaIndex
Package vulnerabilities
Not available
Repository security score
Not available
Interface Preview
LangChain
Feature Comparison
| Feature | LangChain | LlamaIndex |
|---|---|---|
| Retrieval | ||
| Vector store integrations | Full support | Full support |
| Hybrid keyword and vector retrieval | Partial support | Full support |
| Reranking and query routing | Partial support | Full support |
| Document parsing and chunking strategies | Partial support | Full support |
| Orchestration | ||
| Tool calling | Full support | Full support |
| Multi-step agents | Full support | Full support |
| Explicit stateful graphs with checkpointing | Full support | Partial support |
| Conversation memory strategies | Full support | Partial support |
| Ecosystem | ||
| Large integration catalogue | Full support | Partial support |
| Managed tracing and evaluation | Full support | Partial support |
| Managed document parsing service | Partial support | Full support |
| TypeScript implementation | Full support | Full support |
| Engineering | ||
| Python-first | Full support | Full support |
| Open source | Full support | Full support |
| Structured output enforcement | Full support | Full support |
| Streaming responses | Full support | Full support |
Retrieval
Vector store integrations
Hybrid keyword and vector retrieval
Reranking and query routing
Document parsing and chunking strategies
Orchestration
Tool calling
Multi-step agents
Explicit stateful graphs with checkpointing
Conversation memory strategies
Ecosystem
Large integration catalogue
Managed tracing and evaluation
Managed document parsing service
TypeScript implementation
Engineering
Python-first
Open source
Structured output enforcement
Streaming responses
Which approach fits
LangChain and LlamaIndex are both frameworks you build an LLM application on, and they are organised around different halves of the problem. LangChain's centre is orchestration: chains, tools, memory and — through LangGraph — explicit stateful graphs with checkpointing. LlamaIndex's centre is retrieval: ingestion, chunking, indexing, and a wide set of retrieval strategies with reranking and query routing built in.
When each approach fits
Choose LangChain if:
Choose LangChain when the difficulty is control flow. Applications that call several tools, keep conversation state across sessions, retry and escalate, or need a human to approve a step are what its agent and graph abstractions exist for, and LangSmith answers the tracing problem every agent project eventually has. The integration catalogue also means the component you need usually already exists.
Choose LlamaIndex if:
Choose LlamaIndex when the difficulty is context. Vector, keyword, hybrid, recursive and graph retrieval with reranking and query routing are framework primitives rather than things you assemble, chunking strategies are first-class, and LlamaParse handles the documents — PDFs, tables, scanned pages — that quietly ruin retrieval quality before any model sees them.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
Which half of our problem is actually hard?
Look at ten failures and classify each. If the model could have answered correctly but received the wrong context — answers citing the wrong document, or missing something you know is in the corpus — that is a retrieval problem, and no amount of orchestration fixes it. If retrieval is fine but the application needs to do several things in an order that depends on results, that is a control flow problem. Most projects have a clear majority.
Can we use both?
Yes, and it is the common production pattern: LlamaIndex as the retrieval layer, LangGraph as the orchestration layer around it. Because both are Python libraries rather than platforms, combining them is an import rather than an integration. The cost is two dependency sets and two sets of conventions for your team to hold, which is worth it when both halves are genuinely hard and unnecessary when one dominates.
Does document parsing really matter that much?
More than framework choice, usually. A contract in a scanned PDF with tables becomes either usable text or noise depending entirely on the parser, and no retrieval strategy recovers from noise. If your corpus is clean Markdown the question barely arises; if it is PDFs, slides and spreadsheets from a shared drive, parsing quality determines the ceiling on everything downstream.
What do these need to run?
Python 3.9 or later, an API key for a model provider, and somewhere to run the process — a container on Kubernetes, a serverless function, or a FastAPI service behind a REST endpoint returning JSON. Retrieval adds a vector store: PostgreSQL with pgvector, or a dedicated one such as Qdrant or Pinecone. Both also ship TypeScript implementations if the application is not Python.
What does running one of these in production require?
Tracing every step with inputs, outputs, latency and token cost; a hard ceiling on iterations so a loop that fails to converge stops rather than spends; retries that distinguish a transient API error from a model producing unusable output; and an evaluation set that catches quality regressions when a prompt, a chunking strategy or a model changes. That last one matters more with retrieval, because a chunking change can quietly degrade answers without erroring.
How portable is the work we do?
Prompts, tool implementations and business logic are plain Python and port with modest editing. What ties you to a framework is orchestration structure and retrieval configuration. Keeping prompts and tool functions in their own modules, separate from framework wiring, keeps a later change cheap — and is what makes running both frameworks together straightforward rather than tangled.