LangChain vs LlamaIndex (2026)

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 2026-08-17: LangChain's root repository has 144,401 GitHub stars and 24,037 forks; LlamaIndex has 51,703 stars and 7,953 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, checked 2026-08-17): no SemVer stability guarantee applies, so a breaking change can still ship in any point release, even though its own release cadence has slowed (five releases between March and June 2026, none since).

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.