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:

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 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.""" # 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 )

Architecture Patterns

Pattern 1: RAG with LangGraph

from langgraph.graph import StateGraph, START, END from langchain_anthropic import ChatAnthropic from langchain_voyageai import VoyageAIEmbeddings from langchain_pinecone import PineconeVectorStore from langchain_core.documents import Document from langchain_core.prompts import ChatPromptTemplate from typing import TypedDict, Annotated

class RAGState(TypedDict): question: str context: Annotated[list[Document], "retrieved documents"] answer: str

Initialize components

llm = ChatAnthropic(model="claude-sonnet-4-6") embeddings = VoyageAIEmbeddings(model="voyage-3-large") vectorstore = PineconeVectorStore(index_name="docs", embedding=embeddings) retriever = vectorstore.as_retriever(search_kwargs={"k": 4})

Define nodes

async def retrieve(state: RAGState) -> RAGState: """Retrieve relevant documents.""" docs = await retriever.ainvoke(state["question"]) return {"context": docs}

async def generate(state: RAGState) -> RAGState: """Generate answer from context.""" prompt = ChatPromptTemplate.from_template( """Answer based on the context below. If you cannot answer, say so.

    Context: {context}

    Question: {question}

    Answer:"""
)
context_text = "\n\n".join(doc.page_content for doc in state["context"])
response = await llm.ainvoke(
    prompt.format(context=context_text, question=state["question"])
)
return {"answer": response.content}

Build graph

builder = StateGraph(RAGState) builder.add_node("retrieve", retrieve) builder.add_node("generate", generate) builder.add_edge(START, "retrieve") builder.add_edge("retrieve", "generate") builder.add_edge("generate", END)

rag_chain = builder.compile()

Use the chain

result = await rag_chain.ainvoke({"question": "What is the main topic?"})

Pattern 2: Custom Agent with Structured Tools

from langchain_core.tools import StructuredTool from pydantic import BaseModel, Field

class SearchInput(BaseModel): """Input for database search.""" query: str = Field(description="Search query") filters: dict = Field(default={}, description="Optional filters")

class EmailInput(BaseModel): """Input for sending email.""" recipient: str = Field(description="Email recipient") subject: str = Field(description="Email subject") content: str = Field(description="Email body")

async def search_database(query: str, filters: dict = {}) -> str: """Search internal database for information.""" # Your database search logic return f"Results for '{query}' with filters {filters}"

async def send_email(recipient: str, subject: str, content: str) -> str: """Send an email to specified recipient.""" # Email sending logic return f"Email sent to {recipient}"

tools = [ StructuredTool.from_function( coroutine=search_database, name="search_database", description="Search internal database", args_schema=SearchInput ), StructuredTool.from_function( coroutine=send_email, name="send_email", description="Send an email", args_schema=EmailInput ) ]

agent = create_react_agent(llm, tools)

Pattern 3: Multi-Step Workflow with StateGraph

from langgraph.graph import StateGraph, START, END from typing import TypedDict, Literal

class WorkflowState(TypedDict): text: str entities: list analysis: str summary: str current_step: str

async def extract_entities(state: WorkflowState) -> WorkflowState: """Extract key entities from text.""" prompt = f"Extract key entities from: {state['text']}\n\nReturn as JSON list." response = await llm.ainvoke(prompt) return {"entities": response.content, "current_step": "analyze"}

async def analyze_entities(state: WorkflowState) -> WorkflowState: """Analyze extracted entities.""" prompt = f"Analyze these entities: {state['entities']}\n\nProvide insights." response = await llm.ainvoke(prompt) return {"analysis": response.content, "current_step": "summarize"}

async def generate_summary(state: WorkflowState) -> WorkflowState: """Generate final summary.""" prompt = f"""Summarize: Entities: {state['entities']} Analysis: {state['analysis']}

Provide a concise summary."""
response = await llm.ainvoke(prompt)
return {"summary": response.content, "current_step": "complete"}

def route_step(state: WorkflowState) -> Literal["analyze", "summarize", "end"]: """Route to next step based on current state.""" step = state.get("current_step", "extract") if step == "analyze": return "analyze" elif step == "summarize": return "summarize" return "end"

Build workflow

builder = StateGraph(WorkflowState) builder.add_node("extract", extract_entities) builder.add_node("analyze", analyze_entities) builder.add_node("summarize", generate_summary)

builder.add_edge(START, "extract") builder.add_conditional_edges("extract", route_step, { "analyze": "analyze", "summarize": "summarize", "end": END }) builder.add_conditional_edges("analyze", route_step, { "summarize": "summarize", "end": END }) builder.add_edge("summarize", END)

workflow = builder.compile()

Pattern 4: Multi-Agent Orchestration

from langgraph.graph import StateGraph, START, END from langgraph.prebuilt import create_react_agent from langchain_core.messages import HumanMessage from typing import Literal

class MultiAgentState(TypedDict): messages: list next_agent: str

Create specialized agents

researcher = create_react_agent(llm, research_tools) writer = create_react_agent(llm, writing_tools) reviewer = create_react_agent(llm, review_tools)

async def supervisor(state: MultiAgentState) -> MultiAgentState: """Route to appropriate agent based on task.""" prompt = f"""Based on the conversation, which agent should handle this?

Options:
- researcher: For finding information
- writer: For creating content
- reviewer: For reviewing and editing
- FINISH: Task is complete

Messages: {state['messages']}

Respond with just the agent name."""

response = await llm.ainvoke(prompt)
return {"next_agent": response.content.strip().lower()}

def route_to_agent(state: MultiAgentState) -> Literal["researcher", "writer", "reviewer", "end"]: """Route based on supervisor decision.""" next_agent = state.get("next_agent", "").lower() if next_agent == "finish": return "end" return next_agent if next_agent in ["researcher", "writer", "reviewer"] else "end"

Build multi-agent graph

builder = StateGraph(MultiAgentState) builder.add_node("supervisor", supervisor) builder.add_node("researcher", researcher) builder.add_node("writer", writer) builder.add_node("reviewer", reviewer)

builder.add_edge(START, "supervisor") builder.add_conditional_edges("supervisor", route_to_agent, { "researcher": "researcher", "writer": "writer", "reviewer": "reviewer", "end": END })

Each agent returns to supervisor

for agent in ["researcher", "writer", "reviewer"]: builder.add_edge(agent, "supervisor")

multi_agent = builder.compile()

Memory Management

Token-Based Memory with LangGraph

from langgraph.checkpoint.memory import MemorySaver from langgraph.prebuilt import create_react_agent

In-memory checkpointer (development)

checkpointer = MemorySaver()

Create agent with persistent memory

agent = create_react_agent(llm, tools, checkpointer=checkpointer)

Each thread_id maintains separate conversation

config = {"configurable": {"thread_id": "session-abc123"}}

Messages persist across invocations with same thread_id

result1 = await agent.ainvoke({"messages": [("user", "My name is Alice")]}, config) result2 = await agent.ainvoke({"messages": [("user", "What's my name?")]}, config)

Agent remembers: "Your name is Alice"

Production Memory with PostgreSQL

from langgraph.checkpoint.postgres import PostgresSaver

Production checkpointer

checkpointer = PostgresSaver.from_conn_string( "postgresql://user:pass@localhost/langgraph" )

agent = create_react_agent(llm, tools, checkpointer=checkpointer)

Vector Store Memory for Long-Term Context

from langchain_community.vectorstores import Chroma from langchain_voyageai import VoyageAIEmbeddings

embeddings = VoyageAIEmbeddings(model="voyage-3-large") memory_store = Chroma( collection_name="conversation_memory", embedding_function=embeddings, persist_directory="./memory_db" )

async def retrieve_relevant_memory(query: str, k: int = 5) -> list: """Retrieve relevant past conversations.""" docs = await memory_store.asimilarity_search(query, k=k) return [doc.page_content for doc in docs]

async def store_memory(content: str, metadata: dict = {}): """Store conversation in long-term memory.""" await memory_store.aadd_texts([content], metadatas=[metadata])

Callback System & LangSmith

LangSmith Tracing

import os from langchain_anthropic import ChatAnthropic

Enable LangSmith tracing

os.environ["LANGCHAIN_TRACING_V2"] = "true" os.environ["LANGCHAIN_API_KEY"] = "your-api-key" os.environ["LANGCHAIN_PROJECT"] = "my-project"

All LangChain/LangGraph operations are automatically traced

llm = ChatAnthropic(model="claude-sonnet-4-6")

Custom Callback Handler

from langchain_core.callbacks import BaseCallbackHandler from typing import Any, Dict, List

class CustomCallbackHandler(BaseCallbackHandler): def on_llm_start( self, serialized: Dict[str, Any], prompts: List[str], **kwargs ) -> None: print(f"LLM started with {len(prompts)} prompts")

def on_llm_end(self, response, **kwargs) -> None:
    print(f"LLM completed: {len(response.generations)} generations")

def on_llm_error(self, error: Exception, **kwargs) -> None:
    print(f"LLM error: {error}")

def on_tool_start(
    self, serialized: Dict[str, Any], input_str: str, **kwargs
) -> None:
    print(f"Tool started: {serialized.get('name')}")

def on_tool_end(self, output: str, **kwargs) -> None:
    print(f"Tool completed: {output[:100]}...")

Use callbacks

result = await agent.ainvoke( {"messages": [("user", "query")]}, config={"callbacks": [CustomCallbackHandler()]} )

Streaming Responses

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-sonnet-4-6", streaming=True)

Stream tokens

async for chunk in llm.astream("Tell me a story"): print(chunk.content, end="", flush=True)

Stream agent events

async for event in agent.astream_events( {"messages": [("user", "Search and summarize")]}, version="v2" ): if event["event"] == "on_chat_model_stream": print(event["data"]["chunk"].content, end="") elif event["event"] == "on_tool_start": print(f"\n[Using tool: {event['name']}]")

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)