LangChain vs LlamaIndex: which LLM framework fits your system

As of October 2026, pick LangChain (with LangGraph) when you are building agents and multi-step workflows that call tools, need durable state, pause for human approval and run for a long time. Pick LlamaIndex when the core problem is getting the right information out of messy documents: parsing PDFs and tables, indexing, and retrieval over large collections. Many teams use LlamaIndex for ingestion and retrieval inside a LangGraph agent, and some use neither, calling model SDKs directly when the workflow is simple.

At a glance

When to choose LangChain and LangGraph

• Agents that take actions. Workflows that read a request, call internal APIs, query databases and write results back benefit from LangGraph's explicit state machine, retries and branching.
• Human approval steps. If a person must approve a refund, a trade instruction or an outbound email before the agent continues, LangGraph's checkpointing lets the run pause and resume cleanly.
• Multi-agent designs. Supervisors, sub-agents and handoffs are first-class patterns.
• Teams that want one tracing and evaluation tool. LangSmith gives traces, datasets and evaluation runs in one place.

LangChain's early reputation for heavy abstractions was earned. The ecosystem has since consolidated around LangGraph as the agent runtime and a slimmer core, but the lesson stands: keep prompts and tool definitions in your own code, and use the framework for state, control flow and integrations.

When to choose LlamaIndex

• Retrieval over large or messy document sets. Contracts, filings, manuals, research reports and scanned PDFs with tables need careful parsing and chunking. LlamaIndex's node parsers, metadata extraction and LlamaParse are built for this.
• Advanced retrieval patterns. Hierarchical retrieval, sub-question decomposition, routing across multiple indexes and structured data queries are well supported.
• Document workflows. Extraction pipelines that turn documents into structured records, then answer questions over them.
• Teams whose main risk is retrieval quality. If your evaluations show answers fail because the wrong passages come back, LlamaIndex's retrieval toolkit is the shorter path to fixing it.

Architecture, lock-in and production trade-offs

Quality depends on retrieval and evaluation, not the framework

Neither framework makes a system accurate by itself. Accuracy comes from parsing quality, chunking strategy, hybrid search, reranking, metadata filters and an evaluation set built from real questions. Choose the framework whose tools make those steps easiest for your data, and measure everything. Our RAG vs fine-tuning guide covers evaluation in more detail.

Latency and cost

Framework overhead is small compared with model calls. What drives latency and cost is how many model calls a chain or agent makes, how much context each carries, and whether you use caching and smaller models for simple steps. Trace every call. Graph-based agents make the number of steps explicit, which helps you spot loops and unnecessary calls.

Lock-in and upgrades

Both projects move fast and have made breaking changes as they matured. Reduce exposure by pinning versions, wrapping framework calls behind your own interfaces, keeping prompts in your repo, and storing data in standard databases (Postgres with pgvector, or a managed vector database) rather than framework-specific formats. If you use managed services like LangSmith or LlamaCloud, review where data and traces are stored and whether self-hosted or region-specific options meet your requirements.

Data privacy

The open-source libraries run in your environment. Data leaves only when you call an external model, embedding API, parser or tracing service. For regulated data, run embeddings and models through your cloud account (for example Amazon Bedrock or Microsoft Foundry), self-host tracing or redact traces, and keep document parsing inside your network if the documents are sensitive.

When to use neither

A single model call with a structured output schema, or a short sequence of calls in plain Python or TypeScript, does not need a framework. Model provider SDKs now include tool calling, structured output and, in some cases, agent toolkits. Start simple, add a framework when state, retries and integrations become painful to hand-roll.

Common mistakes

• Choosing a framework before writing an evaluation set.
• Building agents on deprecated chain abstractions instead of a graph or workflow runtime.
• Using default chunking on complex PDFs and never inspecting what was indexed.
• Sending production traces with customer data to a hosted tool without a data review.

How Ryz fits

Ryz Labs AI pod teams build agents and retrieval systems in your cloud and repos, with LangGraph, LlamaIndex or plain SDKs depending on what the system needs, and ship them to production. Pods bring a tech lead, ML and backend engineers on US business hours, and work with OpenAI and Anthropic models on AWS or Azure. Our pods have shipped an AI voice platform that has handled more than a million outbound calls and an AI driver-support agent; see our case studies. You can also hire LangChain developers or LlamaIndex developers to work on your team, or start with AI agent development.

If you want a proprietary, ready-made AI platform rather than a system your team owns, a platform vendor is the better fit.

Related

• AI pod teams
• RAG development
• AI agents vs chatbots
• Pinecone vs pgvector
• Hire LLM engineers

FAQ

Is LangChain or LlamaIndex better for RAG?

LlamaIndex is generally stronger for document-heavy retrieval: parsing, indexing and advanced retrieval patterns. LangChain handles common RAG well and is stronger when retrieval is one tool inside a larger agent workflow. Both can produce accurate systems with good chunking, hybrid search and evaluation.

Can I use LangChain and LlamaIndex together?

Yes. A common pattern uses LlamaIndex for ingestion and retrieval, exposed as a tool or retriever, inside a LangGraph agent that handles orchestration, state and human approvals.

What is the difference between LangChain and LangGraph?

LangChain provides model interfaces, integrations and building blocks. LangGraph is the orchestration runtime for stateful, multi-step agents, with checkpointing, branching and human-in-the-loop support. As of October 2026, LangChain's agent features are built on LangGraph.

Do I need a framework to build an LLM application?

No. Simple applications often work best with direct model SDK calls and structured outputs. Frameworks pay off when you need durable state, many integrations, complex retrieval or multi-step agents.

Which framework do Ryz AI pods use?

Whichever fits the system and your team's standards. Our pods build with LangGraph, LlamaIndex and direct SDKs, and keep prompts, tools and data in your repos so your team can own the system after it ships.

Questions we didn't answer? Email info@ryzlabs.com.