Langchain Architecture

When to use this skill

Use Langchain Architecture when an AI agent needs a reusable SKILL.md workflow for this job: Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

When not to use it

Skip Langchain Architecture when the task is outside the coding category, or when a more specific skill in this directory already covers the same workflow with clearer triggers.

How to install

1. Personal install: create ~/.claude/skills/langchain-architecture/SKILL.md (and any bundled scripts) so Claude Code, Claude Desktop, and compatible agents can load it in every project.
2. Project install: commit the same folder at .claude/skills/langchain-architecture/ so teammates get the skill with the repo.
3. Restart the agent session after copying files so it re-scans the skills directory, then ask for the task in words that match the skill description.

Full install guide for Claude, Cursor, and Codex

What this skill does

# 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 5 recommended) llm = ChatAnthropic(model="claude-sonnet-5")

# Define tools with Pydantic schemas @tool def search_database(query: str) -> str: """Search internal database for information.""" # Your database search logic return f"Results for: {query}"

@tool def calculate(expression: str) -> str: """Safely evaluate a mathematical expression.

Supports: +, -, *, /, **, %, parentheses Example: '(2 + 3) * 4' returns '20' """ # Safe math evaluation using ast allowed_operators = { ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul, ast.Div: operator.truediv, ast.Pow: operator.pow, ast.Mod: operator.mod, ast.USub: operator.neg, }

def _eval(node): if isinstance(node, ast.Constant): return node.value elif isinstance(node, ast.BinOp): left = _eval(node.left) right = _eval(node.right) return allowed_operators[type(node.op)](left, right) elif isinstance(node, ast.UnaryOp): operand = _eval(node.operand) return allowed_operators[type(node.op)](operand) else: raise ValueError(f"Unsupported operation: {type(node)}")

try: tree = ast.parse(expression, mode='eval') return str(_eval(tree.body)) except Exception as e: return f"Error: {e}"

tools = [search_database, calculate]

# Create checkpointer for memory persistence checkpointer = MemorySaver()

# Create ReAct agent agent = create_react_agent( llm, tools, checkpointer=checkpointer )

# Run agent with thread ID for memory config = {"configurable": {"thread_id": "user-123"}} result = await agent.ainvoke( {"messages": [("user", "Search for Python tutorials and calculate 25 * 4")]}, config=config ) ```

## Detailed patterns and worked examples

Detailed pattern documentation lives in `references/details.md`. Read that file when the navigation tier above is insufficient.

## Testing Strategies

```python import pytest from unittest.mock import AsyncMock, patch

@pytest.mark.asyncio async def test_agent_tool_selection(): """Test agent selects correct tool.""" with patch.object(llm, 'ainvoke') as mock_llm: mock_llm.return_value = AsyncMock(content="Using search_database")

result = await agent.ainvoke({ "messages": [("user", "search for documents")] })

# Verify tool was called assert "search_database" in str(result)

@pytest.mark.asyncio async def test_memory_persistence(): """Test memory persists across invocations.""" config = {"configurable": {"thread_id": "test-thread"}}

# First message await agent.ainvoke( {"messages": [("user", "Remember: the code is 12345")]}, config )

# Second message should remember result = await agent.ainvoke( {"messages": [("user", "What was the code?")]}, config )

assert "12345" in result["messages"][-1].content ```

## Performance Optimization

### 1. Caching with Redis

```python from langchain_community.cache import RedisCache from langchain_core.globals import set_llm_cache import redis

redis_client = redis.Redis.from_url("redis://localhost:6379") set_llm_cache(RedisCache(redis_client)) ```

### 2. Async Batch Processing

```python import asyncio from langchain_core.documents import Document

async def process_documents(documents: list[Document]) -> list: """Process documents in parallel.""" tasks = [process_single(doc) for doc in documents] return await asyncio.gather(*tasks)

async def process_single(doc: Document) -> dict: """Process a single document.""" chunks = text_splitter.split_documents([doc]) embeddings = await embeddings_model.aembed_documents( [c.page_content for c in chunks] ) return {"doc_id": doc.metadata.get("id"), "embeddings": embeddings} ```

### 3. Connection Pooling

```python from langchain_pinecone import PineconeVectorStore from pinecone import Pinecone

# Reuse Pinecone client pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"]) index = pc.Index("my-index")

# Create vector store with existing index vectorstore = PineconeVectorStore(index=index, embedding=embeddings) ```

Intended uses

• 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