LlamaIndex vs LangChain for RAG and Agents (2026)

Last updated: 1 October 2026

Quick Answer: LlamaIndex vs LangChain comes down to which problem is harder for you, retrieval or orchestration. LlamaIndex is built around ingesting, indexing and querying your own data, while LangChain is built around agents, tools and control flow. Both ship MIT-licensed Python packages, and the two can run together.

LlamaIndex and LangChain overlap, and teams can run both in one codebase. The question is not which is better but which half of your system you have not solved.

This page answers both phrasings, LlamaIndex vs LangChain and LangChain vs LlamaIndex, because the decision is the same either way. Every capability below was read on each project's own docs on 1 October 2026.

What is the difference between LlamaIndex and LangChain?

LlamaIndex is a data framework. LangChain is an agent framework. LlamaIndex describes itself as data connectors, index and graph structures over your content, and a retrieval and query interface on top. LangChain describes its core as create_agent, a configurable harness of model, tools, prompt and middleware around a model loop.

The difference shows up in the first hour. With LlamaIndex you point a reader at a directory, a Notion space or an S3 bucket and choose an index type. With LangChain you give a model tools and decide what the loop may do. The defaults pull in different directions.

One fact to carry into the decision: LlamaIndex has told its own users that the framework stays available as an open toolkit while its primary focus has moved to document parsing. LangChain consolidated its agent stack around LangGraph as the runtime, which our LangChain vs LangGraph comparison covers.

Capabilities as documented by each vendor on 1 October 2026; links in the text.

The table also answers the question buyers ask next. The names that come up for production agent workflows are LangChain with LangGraph, AutoGen and MetaGPT, with LlamaIndex as the retrieval-heavy option. We rank that field in AI agent frameworks for production.

Pydantic AI vs LangChain

Pydantic AI vs LangChain is a question about typing, not scope. Pydantic AI presents itself as a typed Python AI SDK with an extensible agent loop and model swapping by string, and the same agent object runs behind a web front end, a terminal, a voice call or a background queue. Teams already validating everything with Pydantic models find it the shorter path.

Langflow vs LangChain

Langflow is a visual builder, so Langflow vs LangChain is really a question of who edits the flow. It is an open-source Python framework with a drag-and-drop editor where each component node is one step, and it requires no specific model or vector store.

Which is better for RAG?

For retrieval work, LlamaIndex gives you more structure out of the box. It documents RAG as five stages, loading, indexing, storing, querying and evaluation, and ships named index types for each retrieval shape, including a property graph index for relationship-heavy content. LangChain gives you the same pieces as composable primitives, and says an existing knowledge base can be attached as an agent tool instead of rebuilt.

So the practical test is your corpus. Messy PDFs, scanned contracts and mixed formats favor LlamaIndex, where the parsing investment went. A clean warehouse or an existing search index favors LangChain, because you are wiring retrieval into an agent rather than building an index.

Teams looking for alternatives to LangChain on the retrieval side often consider LlamaIndex or Haystack, deepset's open-source framework for agents, RAG and multimodal search. If a packaged knowledge base tool already covers your use case it beats a framework build, and a custom pipeline waits until retrieval quality or the data boundary stops fitting. Retrieval also comes before fine-tuning, which changes behavior rather than supplying facts. The patterns are compared in agentic RAG vs RAG.

Which is better for agents and multi-step workflows?

LangChain goes deeper on orchestration. LangGraph, built by LangChain Inc and usable without LangChain, is a low-level orchestration framework and runtime for long-running stateful agents that mixes deterministic steps with model-driven steps in one graph. That is what a multi-step business process needs: branches you control, state that survives a restart, and a place to insert an approval.

LlamaIndex is not absent. It documents three multi-agent patterns and when to use each: AgentWorkflow with built-in handoffs, an orchestrator agent with sub-agents exposed as tools when you need control over the sequence, and a custom planner only when neither fits. Its LlamaAgents layer adds event-driven orchestration with branching, parallelism, human-in-the-loop review and durability.

The split: if the hard part is deciding what happens next, build on LangChain. If it is getting trustworthy context into the prompt, build on LlamaIndex and let it serve the agent. Engineering partners that build multi-agent systems for business processes can keep that division rather than forcing one framework to do both jobs.

How do they compare in production (observability, evals, deployment)?

Neither framework is a production platform on its own, and both answer production questions with a second product. LangChain routes tracing and evaluation through LangSmith: tracing switches on with two environment variables and no code change, and evaluation covers offline experiments on datasets plus online evaluators that score live traces. Self-hosting LangSmith is possible, but it is an add-on to the Enterprise plan and needs a license key.

LlamaIndex splits the job. Evaluation is first-party, with modules for correctness, faithfulness, context relevancy, answer relevancy and guideline adherence, many needing no labeled ground truth. Tracing goes to partners through an OpenTelemetry integration, Arize Phoenix and hosted LlamaTrace among them. For serving, the llama-agents-server package exposes a workflow over HTTP through a WorkflowServer, deployed to LlamaCloud or run yourself.

For a regulated deployment, that choice decides the architecture. Both frameworks are MIT-licensed and portable; telemetry is where data can leave, so settle where traces land before you write the first agent.

Langfuse vs LangChain

Langfuse vs LangChain is a choice of telemetry, not of framework. Langfuse is an open-source, self-hostable AI engineering platform for tracing, prompt management and evaluation, with native integrations for LangChain and LlamaIndex. When a security review blocks a hosted tracing service, it is a common answer.

Can you use LlamaIndex and LangChain together?

Yes. LlamaIndex documents integration with outer application frameworks, LangChain included, and LangChain's retrieval guidance tells you to attach a knowledge base you already run as an agent tool rather than rebuild it.

The clean pattern is a boundary, not a blend. Let LlamaIndex own ingestion, indexing and the retriever, expose it behind one function or HTTP endpoint, and let the LangChain agent call it as a tool. Either half is then replaceable on its own.

What does not work is two orchestration layers in one service: two sets of retries, two trace formats and no single place to read what the system did. Pick one conductor.

Which should your team pick?

Work through four questions.

1. Which half is harder? Mixed-format documents and retrieval quality point to LlamaIndex. Multi-step processes with approvals and state point to LangChain.
2. Where do traces have to live? If telemetry must stay inside your network without an enterprise contract, plan a self-hostable tracing layer from day one.
3. Who maintains it? A Python team that reviews everything in code is served by either; a team that wants a visual canvas needs a builder on top.
4. What is the exit cost? Both are MIT, so the lock-in is your index format and trace history, not the library.

Choose LlamaIndex when retrieval over your own documents is the product and the agent loop is thin. Choose LangChain when the agent is the product and retrieval is one tool among several. Choose both, with a hard interface between them, when both halves are hard.

What about AutoGen, MetaGPT and CrewAI?

Three names come up in every agent discussion, and their 2026 status differs sharply. AutoGen is in maintenance mode by Microsoft's own statement: no new features, community managed, new users pointed at Microsoft Agent Framework with a migration guide. That makes it a weak foundation for new work.

MetaGPT is narrower than it looks. It assigns software-company roles (product manager, architect, project manager, engineer) to models and runs them through orchestrated standard procedures, with "Code = SOP(Team)" as its philosophy. It is MIT-licensed and interesting for software generation, and a poor fit for a support assistant.

LangChain vs CrewAI

On LangChain vs CrewAI, the difference is the unit of design. CrewAI pairs Crews, teams of collaborating agents, with Flows, structured event-driven workflows that manage state and control execution, and the role-and-crew model gets a multi-agent demo working quickly. LangChain with LangGraph starts lower, at nodes and state: more work up front, easier to constrain when the process must be deterministic and auditable.

What mistakes should you avoid when choosing a framework?

The expensive mistake is choosing on a benchmark post instead of your own corpus. Run twenty real questions, the ugly ones included, through a one-day prototype in each framework and score the retrieved context, not the prose. Retrieval failures cause many bad answers, and testing exposes them quickly.

Three more worth naming: deciding orchestration before you can measure quality, which leaves no way to prove a change helped; treating telemetry as a detail, then learning at security review that traces leave the network; and wiring the framework straight into business logic with no interface, so swapping it means a rewrite.

How Origins AI builds RAG and agent systems in production

Origins AI (originshq.com) is a US-based AI-augmented engineering company that builds custom AI workflows, agents and LLM integrations and deploys its own self-hosted enterprise AI products inside the customer's infrastructure.

The published stack names LangChain alongside TensorFlow, PyTorch, MLOps pipelines and Kubernetes, and the services FAQ runs from AI strategy consulting and data engineering through to AI agent deployment. Engagement models listed there are dedicated teams, project-based contracts, time-and-materials and build-operate-transfer, and the company does not publish a rate card. Security controls the same FAQ lists are encryption at rest and in transit, secure authentication, continuous security monitoring and least-privilege access.

Where a team wants the retrieval layer as a product rather than a build, the Origins AI Velocity AI Suite covers it. Its Knowledge Foundation layer handles data intake, document intelligence, knowledge structuring and retrieval indexing, and the product page reports 1900+ sources and 91+ document formats. The AI Core layer adds a retrieval engine, model orchestration, bring-your-own API and private storage, with stated compatibility for Pinecone, Chroma and Weaviate plus SQL, Postgres and MongoDB. It is deployed in your environment with an implementation team.

Talk to an engineer

Bring your corpus and your hardest workflow, and an engineer will say which half to build first and where the retrieval layer belongs. Book a call with Origins AI.