langchain-agents
Use when "LangChain", "LLM chains", "ReAct agents", "tool calling", or asking about "RAG pipelines", "conversation memory", "document QA", "agent tools", "LangSmith"
Installation
Details
Usage
After installing, this skill will be available to your AI coding assistant.
Verify installation:
npx agent-skills-cli listSkill Instructions
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
- Docs: https://docs.langchain.com
- LangSmith: https://smith.langchain.com
- Templates: https://github.com/langchain-ai/langchain/tree/master/templates
More by eyadsibai
View allUse when "organizing files", "cleaning up folders", "finding duplicates", "structuring directories", or asking about "Downloads cleanup", "folder structure", "file management"
Use when "data pipelines", "ETL", "data warehousing", "data lakes", or asking about "Airflow", "Spark", "dbt", "Snowflake", "BigQuery", "data modeling"
This skill should be used when the user asks to "create a commit", "write commit message", "create a pull request", "generate changelog", "manage branches", "git workflow", "merge strategy", "PR description", or mentions git operations and version control workflows.
Use when implementing end-to-end tests, using Playwright or Cypress, testing user journeys, debugging flaky tests, or asking about "E2E testing", "Playwright", "Cypress", "browser testing", "visual regression", "test automation"