langchain-architecture

# LangChain Architecture

Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration.

## When to Use This Skill

- Building autonomous AI agents with tool access
- Implementing complex multi-step LLM workflows
- Managing conversation memory and state
- Integrating LLMs with external data sources and APIs
- Creating modular, reusable LLM application components
- Implementing document processing pipelines
- Building production-grade LLM applications

## Core Concepts

### 1. Agents
Autonomous systems that use LLMs to decide which actions to take.

**Agent Types:**
- **ReAct**: Reasoning + Acting in interleaved manner
- **OpenAI Functions**: Leverages function calling API
- **Structured Chat**: Handles multi-input tools
- **Conversational**: Optimized for chat interfaces
- **Self-Ask with Search**: Decomposes complex queries

### 2. Chains
Sequences of calls to LLMs or other utilities.

**Chain Types:**
- **LLMChain**: Basic prompt + LLM combination
- **SequentialChain**: Multiple chains in sequence
- **RouterChain**: Routes inputs to specialized chains
- **TransformChain**: Data transformations between steps
- **MapReduceChain**: Parallel processing with aggregation

### 3. Memory
Systems for maintaining context across interactions.

**Memory Types:**
- **ConversationBufferMemory**: Stores all messages
- **ConversationSummaryMemory**: Summarizes older messages
- **ConversationBufferWindowMemory**: Keeps last N messages
- **EntityMemory**: Tracks information about entities
- **VectorStoreMemory**: Semantic similarity retrieval

### 4. Document Processing
Loading, transforming, and storing documents for retrieval.

**Components:**
- **Document Loaders**: Load from various sources
- **Text Splitters**: Chunk documents intelligently
- **Vector Stores**: Store and retrieve embeddings
- **Retrievers**: Fetch relevant documents
- **Indexes**: Organize documents for efficient access

### 5. Callbacks
Hooks for