🆚Compare LlamaIndex and LangChain for RAG
Stop picking a RAG framework from a Twitter poll. See LlamaIndex and LangChain side by side on the same pipeline so you can defend your choice for a real workload with real tradeoffs.
Phase 1Different Frameworks, Different Centers of Gravity
See the design centers behind each framework's API
Two frameworks, two completely different starting questions
6 minLlamaIndex starts from 'how does my data get queried?' LangChain starts from 'how do I compose anything that talks to an LLM?'
LlamaIndex thinks in stages of a data pipeline
6 minLoading, parsing, indexing, retrieving, querying — LlamaIndex names every stage of the data-to-answer pipeline and gives you primitives at each one.
LangChain thinks in one primitive composed many ways
6 minLangChain's whole API is the Runnable interface and the pipe. Everything else — RAG, agents, tools, evals — is a particular tree of Runnables.
Same RAG, different shape: a side-by-side preview
7 minOn the same RAG task, LlamaIndex code reads as a pipeline configuration; LangChain code reads as a chain expression. Both are valid; they're not the same shape.
Phase 2Building the Same RAG in Both Frameworks
Build the same minimal RAG in both — line by line
Load and chunk: three lines vs a dozen
7 minLlamaIndex collapses load + chunk into a default `from_documents` call. LangChain makes you pick a loader and a splitter explicitly.
Index and retrieve: where the framework's center of gravity shows
7 minLlamaIndex's `Index` is a first-class object with retrieval baked in. LangChain treats the vector store as one Runnable and the retriever as another.
Generate the answer: hidden synthesizer vs explicit chain
7 minLlamaIndex's `ResponseSynthesizer` does prompt + model + parse for you with three modes. LangChain makes you write the prompt-model-parser chain yourself.
Count the lines, count the concepts
6 minOn a minimal RAG, LangChain has a few more lines but the same ~6 concepts. LlamaIndex has fewer lines and ~3 visible concepts — the rest live behind defaults.
Honest failure modes: what each framework makes hard
7 minLlamaIndex makes general LLM-app composition awkward. LangChain makes opinionated indexing and synthesis pipelines awkward. Both honestly.
Phase 3Matching Workloads to the Right Framework
Match each framework to the workloads it actually wins
Your team has 10M PDFs and a deadline — which framework?
7 minWhen the trunk problem is indexing scale and chunking quality, LlamaIndex's center of gravity does real work for you. LangChain's chain primitive doesn't help here.
An agent must call five tools and stream — which framework?
7 minWhen the trunk problem is composing tool use, branching, and streaming, LangChain's Runnable primitive plus LangGraph beats LlamaIndex's pipeline-shaped abstractions.
RAG inside an agent: the case for using both frameworks
7 minReal production apps often want LlamaIndex's index plus LangChain's orchestration. Mixing them is a legitimate architectural pattern, not a smell.
Migration cost is real — pick like you'll live with it
7 minThe migration cost between these frameworks isn't theoretical. Counting integration points before picking is cheaper than discovering them six months in.
Phase 4Picking and Defending Your Framework Choice
Pick a framework for a 10M-doc app and defend it
Pick the framework for a 10M-doc, tool-using app and defend it
12 minPick the framework for a 10M-doc, tool-using app and defend it
Frequently asked questions
- What is the actual difference between LlamaIndex and LangChain?
- This is covered in the “Compare LlamaIndex and LangChain for RAG” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- Which is better for RAG with millions of documents — LlamaIndex or LangChain?
- This is covered in the “Compare LlamaIndex and LangChain for RAG” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- Can you use LlamaIndex inside LangChain (or the other way around)?
- This is covered in the “Compare LlamaIndex and LangChain for RAG” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- Is LangChain overkill if I only need retrieval-augmented generation?
- This is covered in the “Compare LlamaIndex and LangChain for RAG” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- When does LlamaIndex's chunking and indexing beat LangChain's?
- This is covered in the “Compare LlamaIndex and LangChain for RAG” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
Related paths
🐍Python Decorators Introduction
Build one mental model for Python decorators that covers closures, argument passing, functools.wraps, and stacking — then ship a working caching or logging decorator from scratch in under 30 lines.
🦀Rust Lifetimes Explained
Stop reading `'a` as line noise and start reading it as scope arithmetic — one failing snippet at a time — until you can thread lifetimes through a small parser or iterator adapter without fighting the borrow checker.
☸️Kubernetes Core Concepts
Stop drowning in 30+ resource types. Build the mental model one primitive at a time -- pods, deployments, services, ingress, config -- then deploy a real app with rolling updates and health checks.
📈Big O Intuition
Stop treating Big O as math you memorized for an interview — build the intuition to spot O(n²) disasters, pick the right data structure without thinking, and rewrite a slow function from O(n²) to O(n) in under five minutes.