LangChain, LlamaIndex & LangGraph
The AI Framework Ecosystem. Master LangChain LCEL, LlamaIndex query engines, LangGraph stateful graphs, and how to choose and use the right framework for your production RAG and agent systems.
The AI Framework Ecosystem. Master LangChain LCEL, LlamaIndex query engines, LangGraph stateful graphs, and how to choose and use the right framework for your production RAG and agent systems. This hands-on tutorial focuses on practical implementation of langchain, llamaindex & langgraph concepts.
LangChain, LlamaIndex & LangGraph
You've built agents and RAG systems from scratch. Now let's look at the frameworks that the AI industry actually uses in production. These tools handle the boilerplate — routing, state management, tracing, memory — so you can focus on your application logic.
[!NOTE] Knowing both the raw API approach (from previous chapters) and the framework approach makes you a stronger engineer. Frameworks hide complexity; understanding both helps you debug when things break.
1. LangChain: The Swiss Army Knife 🔗
LangChain is the most popular AI framework. Its core abstraction is the Chain — a composable pipeline of components.
LangChain Expression Language (LCEL)
LCEL uses the | pipe operator to compose chains declaratively:
📚 Official Docs
2. LlamaIndex: Data-First AI Framework 📚
LlamaIndex specializes in connecting LLMs to your data. Its core abstraction is the Index — optimized data structures for LLM querying.
📚 Official Docs
3. LangGraph: Stateful Graphs for Production Agents 🗺️
LangGraph builds on LangChain to add stateful, graph-based agent orchestration — the most powerful pattern for production agents.
📚 Official Docs
4. When to Use Which Framework 🎯
| Use Case | Best Framework | Reason |
|---|---|---|
| Simple RAG chatbot | LangChain | Fast setup, huge community, LangSmith tracing |
| Complex document Q&A | LlamaIndex | Best data connectors, sub-question decomposition |
| Production agent with loops | LangGraph | Checkpointing, HITL, complex state management |
| Role-based content workflow | CrewAI | High-level team abstractions, fast setup |
| Maximum control, no abstraction | Raw API | Full control, easier debugging, fewer dependencies |
5. Framework Pitfalls ⚠️
Quiz
Quiz
Question 1 of 3What does LCEL's | (pipe) operator enable in LangChain?
Key Takeaways
✅ LangChain is best for composable chains, multi-step workflows, and the largest ecosystem.
✅ LlamaIndex excels at data-heavy RAG with 80+ connectors and advanced query engines.
✅ LangGraph is the right choice for production agents needing state management and HITL.
✅ Avoid framework over-abstraction — use raw APIs for simple cases.
✅ Abstract your business logic from the framework to enable easy migration.
Official Resources
- 📖 LangChain Documentation
- 📖 LlamaIndex Documentation
- 📖 LangGraph Documentation
- 📖 CrewAI Documentation
What's Next?
Frameworks help us build. Now let's learn how to take it to production.
Next Module: Module 9 — AI in Production: Architecture, Deployment, and Operations.