LangChain, LangGraph, LlamaIndex: Which Framework for Your AI?

Building a serious AI application, an assistant, an agent, a RAG system, requires orchestrating multiple steps: fetching data, calling a model, using tools, chaining tasks. Three frameworks stand out: LangChain, LangGraph and LlamaIndex. They don’t serve the same purpose.

LangChain: The General-Purpose Toolkit

LangChain is the Swiss Army knife: connectors to models, prompt management, step chains, tool integration. Useful for quickly assembling AI logic, from simple to complex. Its breadth is also its drawback: it can be overkill for basic needs.

One reservation should accompany that description, and it applies to all three tools. LangChain has moved a great deal since its early days: modules were re-split, some abstractions were deprecated, and a two-year-old tutorial no longer runs. For a first call to a model, a plain HTTP call to the provider’s API is often shorter, more readable and easier to debug than a chain of abstractions. The common-sense rule: adopt a framework once you have met the problem it solves, orchestrating several steps, recovering from errors, tracing runs, not before.

LangGraph: Stateful Agents

LangGraph models AI processes as a graph of stateful steps. It’s the tool when building true agents: loops, decisions, retries, and memory between steps. More structured and robust than linear chains for advanced use cases.

The important word in that description is “state”, and it is what justifies the tool. LangGraph persists state between steps, which enables three things a plain chain cannot do: resume an interrupted run where it stopped, step back to replay a branch, and insert human approval mid-path. That is exactly what an agent triggering real actions needs, sending an email, creating an order, updating a database. Without resumption or checkpoints, an agent that fails at step seven starts over, and repeats the first six actions.

LlamaIndex: Data and RAG Specialist

LlamaIndex focuses on data indexing and RAG: ingesting documents, chunking them, indexing them, and serving search to augment the model. When the project’s core is “making documents speak,” it’s often the most straightforward choice.

On this ground, quality is decided before the framework, and it is the point LlamaIndex rightly emphasises: document chunking, the choice of embedding model and the retrieval strategy weigh more than the library assembling them. Two measurements are enough to steer by: recall, is the right answer among the retrieved passages?, and faithfulness, meaning the share of statements not supported by those passages. Vectara’s leaderboard measures that second dimension and shows that even the best models remain at a few percent hallucination.

Which to Choose, or None at All

• LlamaIndex if the project is primarily document-based RAG.
• LangGraph for agents with logic and state.
• LangChain for quickly assembling varied logic.
• None, sometimes: for a simple case, a few well-written API calls are enough, without the complexity of a framework.

One criterion is missing from that list, and it becomes decisive as soon as an agent can act: security. Prompt injection ranks first in the OWASP Top 10 for large language model applications, text sitting in an indexed document or typed by a user can hijack the system’s instructions. The rule that follows is framework-independent: an agent should have only the tools it strictly needs, with minimal permissions, and every irreversible action should require approval. The Model Context Protocol standardises how a model reaches those tools, but obviously does not remove the need to restrict them.

The right choice depends on the use case, which is the focus of our AI project scoping, before building our AI assistants connected to your data.

An AI project to architect? We’ll help you choose (or avoid) the right framework.