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LangChain vs LlamaIndex

Two broad framework options for AI SDK & agent framework. When each fits, what it costs, who moves from one to the other, and what makers who chose it say.

LangChainBroad frameworkllms.txt
LlamaIndexBroad frameworkllms.txt
Ask your AI about this, with this page as the source:ChatGPT ↗Claude ↗Perplexity ↗

Which fits you

Choose LangChain if
  • You need many prebuilt connectors for models, tools and data sources

Use it whenYou need many connectors and want LangGraph for stateful, multi-step agents.

Trade-offLayers of abstraction make debugging harder, and the API has changed often.

Choose LlamaIndex if
  • The agent mostly retrieves and reasons over your own documents

Use it whenIngesting, indexing and querying data is the core of the product.

Trade-offLess natural for agents whose work is mostly calling external tools rather than reading data.

At a glance

LangChainLlamaIndex
Used by65 makers' products · 205 open-source projects16 makers' products · 66 open-source projects
Downloads5.1M/wk−1% vs npm120.7k/wk−19% vs npm
PricingOpen source and free to use; optional paid LangSmith platform for tracing, evals, and deployment. · paid from $39/seat/mo (LangSmith)Open source and free to use; optional paid LlamaCloud managed parsing/indexing service. · paid from $50/mo (LlamaCloud)
Free tierYesYes
Open sourceYes · self-hostableYes · self-hostable

What makers say

Makers on using it for AI SDK & agent framework, from Product Hunt and Starter Story interviews, each linked to the source. Products with a page of their own and fuller notes first.

On LangChain
We chose to integrate with LangChain as it is the leading LLM framework. We are happy that it was so straightforward to integrate with LangChain and build some use cases already.
Tilores entity resolution, the makerSep 2026 ↗
LangChain helped us orchestrate complex reasoning, memory, and planning steps behind our AI employee. It’s the brain behind turning a conversation into a functional website.
mysite.ai, the makerSep 2026 ↗
The whole ecosystem has been a great help till now. Even though we often have to develop our own. Langsmith is one of our go to tool for tracing and evaluating.
Kuration AI, the makerSep 2026 ↗
45 more on the LangChain page →
On LlamaIndex
LlamaIndex provides sufficient independent tooling for building custom AI agents. Their open-source approach and developer community made it a solid choice for our platform.
Tometo, the makerSep 2026 ↗
The unsung hero behind Webjourney’s AI reasoning. It makes connecting structured data, embeddings, and context feel effortless.
Webjourney, the makerSep 2026 ↗
The flexibility is what really got us hooked. We can quickly connect our LLMs with any datasource we've thrown at LlamaIndex.
Web Search Agents by Nimble, the makerSep 2026 ↗
7 more on the LlamaIndex page →

Loved and watch-outs

Themes that recur in makers' words and Hacker News comments, each linked to what it summarises.

LangChain
Most loved
  • One interface across many model providers, so switching models, prompts and chains is cheap. PHPH 2PH 3
  • A large ecosystem of document loaders, chunkers, retrievers and tool integrations for RAG and agents. PHPH 2PH 3
  • LangSmith tracing and evaluation show what an agent did and where it went wrong. PHPH 2PH 3
Watch-outs
  • Frequent breaking API changes force rewrites, and coding agents trained on older versions produce messy code for it. HNHN 2HN 3
  • Layers of abstraction make debugging slow, and many developers find calling model APIs or lighter libraries simpler. HNHN 2HN 3HN 4
  • Its built-in patterns for subagents and deep research lag current practice, such as handing off context through a file system. HNHN 2
On Product Hunt: 4.9★, 115 reviews · mentioned most: agentic workflow support, model integration, LangGraph framework
LlamaIndex
Most loved
  • Connects LLMs to whatever data sources you have, with indexing and search over large datasets. PHPH 2
  • LlamaParse handles complex PDFs, tables and scanned material with high-quality results. PHHN
  • Can run fully local with local models, which suits private document collections. HNHN 2
Watch-outs
  • Out-of-the-box chunking can split words and sentences mid-way, giving poor retrieval results. HN
  • Some developers found it a poorly written wrapper whose tooling was quickly made irrelevant by model progress. HN

Who uses each

LangChain65 makers' products
8baseAI Context FlowBrev.ioChirpz AgentClueoChatCoda by Conductor QuantumCodegenDataboxFabi.aifileAIGitLawgraph8+53 more
LlamaIndex16 makers' products
Basejump AIdebate AIDezynDroidrunTometoWeb Search Agents by NimbleWebjourneyArceeCaideraFuseBaseOverseer AIToolhouse+4 more

What makers pair each with

With LangChain
LLM API
Frontend Framework
Transactional Email
Hosting
With LlamaIndex
Frontend Framework
Transactional Email
LLM API
Hosting

Neither quite right?

See the whole AI SDK & agent framework decision →
MastraTypeScript frameworkTypeScript and Next.js apps that want agents, workflows, memory and RAG without a Python service.vs LangChain →
OpenAI Agents SDKThin SDKA small agent loop with tools, handoffs, guardrails and tracing built in, from the OpenAI team.
Pydantic AIThin SDKPython agents whose outputs must validate against typed schemas, across any model provider.
AI SDKThin SDKTypeScript agents with tool calling, multi-step loops and streaming to the UI, on any model provider.
Claude Agent SDKAgent harnessAgents that read and edit files, run shell commands and manage long context, built on the same harness as Claude Code.
LangGraphMulti-agent orchestrationStateful agents built as an explicit graph of steps, with checkpoints, retries, streaming and pauses for human approval, in Python or JavaScript.