| name | langchain-agents |
| description | Use when "LangChain", "LLM chains", "ReAct agents", "tool calling", or asking about "RAG pipelines", "conversation memory", "document QA", "agent tools", "LangSmith" |
| version | 1.0.0 |
LangChain - LLM Applications with Agents & RAG
The most popular framework for building LLM-powered applications.
When to Use
- Building agents with tool calling and reasoning (ReAct pattern)
- Implementing RAG (retrieval-augmented generation) pipelines
- Need to swap LLM providers easily (OpenAI, Anthropic, Google)
- Creating chatbots with conversation memory
- 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
