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)