LangChain - LLM Applications with Agents & RAG
The most popular framework for building LLM-powered applications.
When to Use
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Building agents with tool calling and reasoning (ReAct pattern)
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Implementing RAG (retrieval-augmented generation) pipelines
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Need to swap LLM providers easily (OpenAI, Anthropic, Google)
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Creating chatbots with conversation memory
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Rapid prototyping of LLM applications
Core Components
Component Purpose Key Concept
Chat Models LLM interface Unified API across providers
Agents Tool use + reasoning ReAct pattern
Chains Sequential operations Composable pipelines
Memory Conversation state Buffer, summary, vector
Retrievers Document lookup Vector search, hybrid
Tools External capabilities Functions agents can call
Agent Patterns
Pattern Description Use Case
ReAct Reason-Act-Observe loop General tool use
Plan-and-Execute Plan first, then execute Complex multi-step
Self-Ask Generate sub-questions Research tasks
Structured Chat JSON tool calling API integration
Tool Definition
Element Purpose
Name How agent refers to tool
Description When to use (critical for selection)
Parameters Input schema
Return type What agent receives back
Key concept: Tool descriptions are critical—the LLM uses them to decide which tool to call. Be specific about when and why to use each tool.
RAG Pipeline Stages
Stage Purpose Options
Load Ingest documents Web, PDF, GitHub, DBs
Split Chunk into pieces Recursive, semantic
Embed Convert to vectors OpenAI, Cohere, local
Store Index vectors Chroma, FAISS, Pinecone
Retrieve Find relevant chunks Similarity, MMR, hybrid
Generate Create response LLM with context
Chunking Strategies
Strategy Best For Typical Size
Recursive General text 500-1000 chars
Semantic Coherent passages Variable
Token-based LLM context limits 256-512 tokens
Retrieval Strategies
Strategy How It Works
Similarity Nearest neighbors by embedding
MMR Diversity + relevance balance
Hybrid Keyword + semantic combined
Self-query LLM generates metadata filters
Memory Types
Type Stores Best For
Buffer Full conversation Short conversations
Window Last N messages Medium conversations
Summary LLM-generated summary Long conversations
Vector Embedded messages Semantic recall
Entity Extracted entities Track facts about people/things
Key concept: Buffer memory grows unbounded. Use summary or vector for long conversations to stay within context limits.
Document Loaders
Source Loader Type
Web pages WebBaseLoader, AsyncChromium
PDFs PyPDFLoader, UnstructuredPDF
Code GitHubLoader, DirectoryLoader
Databases SQLDatabase, Postgres
APIs Custom loaders
Vector Stores
Store Type Best For
Chroma Local Development, small datasets
FAISS Local Large local datasets
Pinecone Cloud Production, scale
Weaviate Self-hosted/Cloud Hybrid search
Qdrant Self-hosted/Cloud Filtering, metadata
LangSmith Observability
Feature Benefit
Tracing See every LLM call, tool use
Evaluation Test prompts systematically
Datasets Store test cases
Monitoring Track production performance
Key concept: Enable LangSmith tracing early—debugging agents without observability is extremely difficult.
Best Practices
Practice Why
Start simple create_agent() covers most cases
Enable streaming Better UX for long responses
Use LangSmith Essential for debugging
Optimize chunk size 500-1000 chars typically works
Cache embeddings They're expensive to compute
Test retrieval separately RAG quality depends on retrieval
LangChain vs LangGraph
Aspect LangChain LangGraph
Best for Quick agents, RAG Complex workflows
Code to start <10 lines ~30 lines
State management Limited Native
Branching logic Basic Advanced
Human-in-loop Manual Built-in
Key concept: Use LangChain for straightforward agents and RAG. Use LangGraph when you need complex state machines, branching, or human checkpoints.
Resources
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LangSmith: https://smith.langchain.com
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Templates: https://github.com/langchain-ai/langchain/tree/master/templates