langchain-architecture

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# LangChain & LangGraph Architecture

Master modern LangChain 1.x and LangGraph for building sophisticated LLM applications with agents, state management, 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

## Package Structure (LangChain 1.x)

```
langchain (1.2.x)         # High-level orchestration
langchain-core (1.2.x)    # Core abstractions (messages, prompts, tools)
langchain-community       # Third-party integrations
langgraph                 # Agent orchestration and state management
langchain-openai          # OpenAI integrations
langchain-anthropic       # Anthropic/Claude integrations
langchain-voyageai        # Voyage AI embeddings
langchain-pinecone        # Pinecone vector store
```

## Core Concepts

### 1. LangGraph Agents

LangGraph is the standard for building agents in 2026. It provides:

**Key Features:**

- **StateGraph**: Explicit state management with typed state
- **Durable Execution**: Agents persist through failures
- **Human-in-the-Loop**: Inspect and modify state at any point
- **Memory**: Short-term and long-term memory across sessions
- **Checkpointing**: Save and resume agent state

**Agent Patterns:**

- **ReAct**: Reasoning + Acting with `create_react_agent`
- **Plan-and-Execute**: Separate planning and execution nodes
- **Multi-Agent**: Supervisor routing between specialized agents
- **Tool-Calling**: Structured tool invocation with Pydantic schemas

### 2. State Management

LangGraph uses TypedDict for explicit state:

```python
from typing import Annotated, TypedDict
from langgraph.graph import MessagesState

# Simple message-based state
class AgentState(MessagesState):
    """Extends MessagesState with custom fields."""
    context: Annotated[list, "retrieved documents"]

# Custom state for complex agents
class CustomState(TypedDict):
    messages: Annotated[list, "conversation history"]
    context: Annotated[dict, "retrieved context"]
    current_step: str
    results: list
```

### 3. Memory Systems

Modern memory implementations:

- **ConversationBufferMemory**: Stores all messages (short conversations)
- **ConversationSummaryMemory**: Summarizes older messages (long conversations)
- **ConversationTokenBufferMemory**: Token-based windowing
- **VectorStoreRetrieverMemory**: Semantic similarity retrieval
- **LangGraph Checkpointers**: Persistent state across sessions

### 4. Document Processing

Loading, transforming, and storing documents:

**Components:**

- **Document Loaders**: Load from various sources
- **Text Splitters**: Chunk documents intelligently
- **Vector Stores**: Store and retrieve embeddings
- **Retrievers**: Fetch relevant documents

### 5. Callbacks & Tracing

LangSmith is the standard for observability:

- Request/response logging
- Token usage tracking
- Latency monitoring
- Error tracking
- Trace visualization

## Quick Start

### Modern ReAct Agent with LangGraph

```python
from langgraph.prebuilt import create_react_agent
from langgraph.checkpoint.memory import MemorySaver
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
import ast
import operator

# Initialize LLM (Claude Sonnet 4.6 recommended)
llm = ChatAnthropic(model="claude-sonnet-4-6", temperature=0)

# Define tools with Pydantic schemas
@tool
def search_database(query: str) -> str:
    """Search internal database for information."""

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