LangChain and LlamaIndex: Choose a Framework Without Losing the Basics

An AI app may load documents, search them, call a model and validate an answer. A framework provides reusable parts for these jobs. It can reduce repeated work, but it also adds interfaces and dependencies you need to understand.

Begin with the actual task

If your app makes one model call, the provider SDK may be enough. If it needs a controlled workflow, write down the states and failure paths first. If it answers from documents, understand the retrieval and access rules before choosing a framework.

Do not choose a tool because every tutorial mentions it. A small implementation you can explain is a useful baseline for deciding what a framework improves.

What LangChain provides

LangChain offers model and tool integrations and an agent-building interface. Its current Python documentation uses create_agent for a configurable agent harness. The harness is the code around the model loop: tools, instructions, state handling and other controls.

LangGraph offers lower-level orchestration for workflows with explicit state and transitions. It can be useful when some steps are fixed and others use model choices. These names are related, but they do not mean every app needs all packages.

What LlamaIndex provides

LlamaIndex provides components for working with data in model applications, including loading, indexing, retrieval and agent or workflow features. It is often useful when the central problem is finding and using information from a collection.

There is overlap between these ecosystems. “LangChain for agents, LlamaIndex only for documents” is too strict. Compare the current features that solve your task, and use official version-matched examples.

One app, with or without a framework

1. Accept a question and identify the user.
2. Find only documents that user may access.
3. Select relevant passages with source IDs.
4. Send a bounded request to the model.
5. Validate the output and source references.
6. Return an answer or a clear missing-information result.

Use the complete local RAG retrieval example to understand the search step first. Then use either framework's official starter guide to replace one component. Keep the same test questions so you can see whether the change helps.

A practical comparison exercise

Write five questions, two unrelated questions and one question about a restricted document. Record the expected sources and behaviour. Run your baseline and your chosen framework version against the same set. Compare correctness, access enforcement, latency and the amount of code you must maintain.

Keep package versions with the example. Old imports from an older tutorial may no longer match current packages. Read migration notes instead of installing several unrelated packages until an import happens to work.

Keep control of the boundaries

A framework does not make a tool safe. Your server still checks the user, arguments and allowed operation. Limit repeated model and tool calls. Add timeouts, useful error handling and checkpoints if your task needs recovery.

Tracing can help you see prompts and tool results, but it may also capture private data. Review what gets recorded and sent to external services. Use redaction and an appropriate retention policy.

Practice

Explain which part you would replace in the six-step app, why it helps and how you would remove it later. Choose one ecosystem for the exercise. You can learn another when a real requirement makes it worthwhile.

Official references

Reviewed on 25 October 2026. Use documentation for your installed versions.

• LangChain overview
• LangGraph overview
• LlamaIndex starter guide

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