langchain-architecture skill

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 createreactagent
• 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:

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

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.righ

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

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

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

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

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)