LangChain vs LlamaIndex: Do You Need an LLM Framework
Practice4 minInnovialabs editorial team
In short
For a chatbot, extraction or a simple RAG, the provider's SDK plus your own code is often enough; LlamaIndex pays off for document-heavy RAG, LangChain for many integrations and agent workflows, but each adds abstractions you have to learn and maintain.
The short answer
You do not need a framework to work with an LLM. A model call is an HTTP request, and the official SDKs already handle messages, streaming, tool calling and structured output.
A framework makes sense when you would otherwise rebuild what it already offers:
- LlamaIndex — when the core of the product is search over your documents (RAG): loading many formats, chunking, indexing, retrieval strategies.
- LangChain — when you need many ready-made integrations (models, vector stores, tools) and multi-step agent workflows; its companion library LangGraph targets stateful agent graphs.
- Plain SDK — when the logic is a handful of prompts, a few tools and one data source.
Comparison
| Criterion | Plain SDK | LangChain | LlamaIndex |
|---|---|---|---|
| Focus | Direct model calls | Chains, agents, integrations | Data ingestion, indexing, retrieval |
| Learning curve | Low: one API | Higher: many concepts and layers | Medium: concepts around indexes and retrievers |
| Prototype speed | Medium | High if a ready component fits | High for document Q&A |
| Control and transparency | Full | Lower: prompts and logic hidden in components | Medium |
| Debugging | Ordinary code and logs | Needs tracing to see what happened | Needs tracing for retrieval steps |
| Switching model providers | Write a thin adapter yourself | Built in | Built in |
| Maintenance cost | Your code only | Follow API changes across many packages | Follow API changes as well |
Both frameworks evolve quickly, and their APIs and package structures have changed noticeably over time. Factor that into the maintenance cost.
When plain SDK calls are enough
- A support chatbot with a system prompt and a few tools.
- Extracting fields from documents into JSON with structured output.
- Classification, summarization, translation.
- A simple RAG: one source, a vector database, one retrieval step.
Without a framework you see every prompt and every request, debugging is ordinary, and upgrading the SDK rarely breaks anything. The code for a basic RAG fits in a few small functions: embed the query, search the vector store, assemble the prompt, call the model.
When a framework helps
LlamaIndex pays off if you have:
- many document types (PDF, Word, web pages, databases) and need ready-made loaders;
- advanced retrieval: hybrid search, re-ranking, hierarchical chunks, metadata filters;
- the need to experiment quickly with different indexing strategies.
LangChain pays off if you have:
- many integrations, and providers or vector stores may change;
- agents with tools, branching and state that you would otherwise build from scratch;
- a team ready to use its ecosystem, including tracing and evaluation tools.
When a framework gets in the way
- Hidden prompts. A component sends text to the model that you did not write, and you discover it only when quality drops.
- Abstraction for its own sake. Three layers of classes around one HTTP call.
- Fragile upgrades. Updating the framework breaks code that worked fine.
- Hard debugging. An error deep in a chain is harder to trace than in your own function.
- Dependency weight. Many packages and transitive dependencies for a small feature.
Selection criteria
Ask yourself:
- How many data sources and formats? Many — consider LlamaIndex.
- How complex is the logic? Multi-step agents with state — consider LangChain/LangGraph. Linear "prompt → answer" — SDK.
- Who will maintain it? If the team does not know the framework, the learning cost is real.
- How critical is control over prompts and latency? The higher, the more reasons to stay close to the SDK.
- How long will the product live? A quick prototype can use any framework; a long-lived product needs predictable upgrades.
A practical compromise: write the core on the SDK and borrow individual components from a framework, such as document loaders or text splitters, without building the whole architecture around it.