# LangChain vs LlamaIndex

> Source: https://theagentsindex.com/compare/langchain-vs-llamaindex (structured, researched, re-verified)

LangChain and LlamaIndex are both open-source, MIT-licensed Python frameworks for building LLM applications, and both are free at their core — but they were built to solve different problems. LangChain provides model, tool and memory abstractions, and through LangGraph an explicit graph of nodes and edges for controlling multi-step, stateful agent flows (loops, branching, human-in-the-loop, multi-agent coordination). LlamaIndex is a data framework built around ingestion, indexing, retrieval and response synthesis — its primitives are documents, indices and query engines, not agent control flow.

Checked live on 2026-07-20: LangChain's root repository has 142,167 GitHub stars and 23,644 forks; LlamaIndex has 50,962 stars and 7,783 forks. Both are MIT-licensed. A less obvious but more decision-relevant difference is API maturity: LangChain reached a stable 1.0 GA in October 2025, shipping a new `create_agent` abstraction with an explicit commitment to no breaking changes until 2.0 — the "abstractions have churned across versions" reputation describes LangChain's pre-1.0 era, not its current state. LlamaIndex, by contrast, is still pre-1.0 (v0.14.23 as of 2026-07-20) with an established pattern of frequent point releases — real, ongoing API-churn risk a team adopting it today should budget for.

Pricing is free-vs-free at the framework level; the paid layers solve different problems entirely, so they are not a like-for-like comparison. LangChain's LangSmith (agent tracing, evals, deployment) publishes self-serve tiers: Developer $0/seat (5,000 base traces/month), Plus $39/seat/month (10,000 base traces, unlimited seats), Enterprise custom. LlamaIndex's LlamaCloud/LlamaParse (managed document parsing and indexing) is credit-based: Free $0 (10,000 credits/month), Starter $50/month (40,000 credits), Pro $500/month (400,000 credits), Enterprise custom. One is an observability platform; the other is a document-processing service — comparing their prices alone would be misleading.

The honest framing: this is a real buyer decision for your PRIMARY framework (retrieval-grounded Q&A and enterprise search → LlamaIndex; multi-step or multi-agent orchestration → LangChain/LangGraph), but it is also a false dichotomy in practice. LlamaIndex's own listing on this site already notes "many production stacks use both together," and that matches what teams building RAG-backed agents actually do — LlamaIndex handles the retrieval layer (chunking, hybrid search, citation), LangGraph handles the orchestration layer (tool calls, multi-step control, handoffs) — rather than picking one and forcing it to do the other's job.

| Fact | LangChain | LlamaIndex |
| --- | --- | --- |
| What it is | The most widely-used framework for building LLM apps and agents, with LangGraph for stateful control. | The data framework for LLM apps, RAG-first, now with agent workflows and a document-parsing cloud. |
| Pricing | Free | Free |
| API | Yes | Yes |
| Tags | Self-hosted · Multi-agent · API · Open source · Free tier | Self-hosted · Multi-agent · API · Open source · Free tier |
| Full facts | https://theagentsindex.com/langchain.md | https://theagentsindex.com/llamaindex.md |

## Verdict

Your app is primarily retrieval-grounded — document Q&A, enterprise search, a knowledge base with citations → start with LlamaIndex; its retrieval patterns (hybrid search, recursive retrieval, sub-question decomposition) are more built-out than LangChain's. Your app is primarily a multi-step or multi-agent process — tool-calling loops, human-in-the-loop approval, coordinated sub-agents → start with LangChain/LangGraph; its state-machine control is what LlamaIndex does not natively provide. Building a RAG-backed agent that needs both → the common production pattern is LlamaIndex as the retrieval layer inside a LangGraph-orchestrated agent, not a single-framework choice.

## FAQ

**Is LangChain vs LlamaIndex really a fair comparison, or are they solving different problems?**

Both — it is a genuine buyer decision for which one is your PRIMARY framework (retrieval-grounded Q&A favors LlamaIndex, multi-step agent orchestration favors LangChain/LangGraph), but in production the two are frequently combined rather than chosen instead of each other. LlamaIndex's own listing on this site already notes that many stacks use LlamaIndex for retrieval inside a LangChain/LangGraph-orchestrated agent — treat this page's verdict as ‘which one first,' not ‘which one only.'

**Which has more GitHub traction?**

LangChain leads by a wide margin: 142,167 stars and 23,644 forks versus LlamaIndex's 50,962 stars and 7,783 forks, checked live 2026-07-20. LangChain also has a head start (created October 2022 vs LlamaIndex's November 2022) and is used well beyond retrieval-augmented generation, which partly explains the gap.

**Is LangChain still unstable and prone to breaking changes?**

That reputation describes LangChain before its 1.0 GA in October 2025 (confirmed via changelog.langchain.com and docs.langchain.com), which shipped a new create_agent abstraction and an explicit commitment to no breaking changes until 2.0. LlamaIndex, by contrast, is still pre-1.0 (v0.14.23 as of 2026-07-20) with a live pattern of frequent point releases — today, LlamaIndex is the framework with more active API-churn risk, not LangChain.

**How does the pricing actually compare?**

Both frameworks are free and MIT-licensed at the core. The paid layers are not comparable like-for-like: LangChain's LangSmith (tracing/evals/deployment) is $0/seat up to 5,000 traces/month, then $39/seat/month for 10,000 traces with unlimited seats, then Enterprise custom. LlamaIndex's LlamaCloud/LlamaParse (managed document parsing) is credit-based — $0 for 10,000 credits/month, $50/month for 40,000 credits, $500/month for 400,000 credits, Enterprise custom. One prices agent observability; the other prices document processing — they solve different problems, so cheaper is not simply ‘better.'

**Which is easier to learn?**

Neither is trivial, but the learning curve is shaped differently. LlamaIndex's core RAG patterns (indices, query engines) are quick to get a basic pipeline running, though its more sophisticated retrieval features (hybrid search, sub-question decomposition) add real depth to learn. LangChain/LangGraph has a steeper initial curve — you are explicitly modeling state as a graph — but that investment is what gives you fine-grained control once you are building a bespoke, production-grade agent rather than a single retrieval pipeline.