langchain-architecture

name	langchain-architecture
description	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. Use when this capability is needed.
metadata	{"author":"AI-Foundry-Core"}

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 = _(node.left)
            right = _(node.right)
             allowed_operators[(node.op)](left, right)
         (node, ast.UnaryOp):
            operand = _(node.operand)
             allowed_operators[(node.op)](operand)
        :
             ValueError()

    :
        tree = ast.parse(expression, mode=)
         (_(tree.body))
     Exception  e:
         

tools = [search_database, calculate]


checkpointer = MemorySaver()


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


config = {: {: }}
result =  agent.ainvoke(
    {: [(, )]},
    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 = .join(doc.page_content  doc  state[])
    response =  llm.ainvoke(
        prompt.(context=context_text, question=state[])
    )
     {: response.content}


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

rag_chain = builder.()


result =  rag_chain.ainvoke({: })

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 "

tools = [
    StructuredTool.from_function(
        coroutine=search_database,
        name=,
        description=,
        args_schema=SearchInput
    ),
    StructuredTool.from_function(
        coroutine=send_email,
        name=,
        description=,
        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[]}
    Analysis: 

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

 () -> [, , ]:
    
    step = state.get(, )
     step == :
         
     step == :
         
     


builder = StateGraph(WorkflowState)
builder.add_node(, extract_entities)
builder.add_node(, analyze_entities)
builder.add_node(, generate_summary)

builder.add_edge(START, )
builder.add_conditional_edges(, route_step, {
    : ,
    : ,
    : END
})
builder.add_conditional_edges(, route_step, {
    : ,
    : END
})
builder.add_edge(, END)

workflow = builder.()

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"
     next_agent  next_agent  [, , ]  


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

builder.add_edge(START, )
builder.add_conditional_edges(, route_to_agent, {
    : ,
    : ,
    : ,
    : END
})


 agent  [, , ]:
    builder.add_edge(agent, )

multi_agent = builder.()

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() -> :
        ()


result =  agent.ainvoke(
    {: [(, )]},
    config={: [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)

Resources

• LangChain Documentation
• LangGraph Documentation
• LangSmith Platform
• LangChain GitHub
• LangGraph GitHub

Common Pitfalls

1. Using Deprecated APIs: Use LangGraph for agents, not initialize_agent
2. Memory Overflow: Use checkpointers with TTL for long-running agents
3. Poor Tool Descriptions: Clear descriptions help LLM select correct tools
4. Context Window Exceeded: Use summarization or sliding window memory
5. No Error Handling: Wrap tool functions with try/except
6. Blocking Operations: Use async methods (ainvoke, astream)
7. Missing Observability: Always enable LangSmith tracing in production

Production Checklist

• Use LangGraph StateGraph for agent orchestration
• Implement async patterns throughout (ainvoke, astream)
• Add production checkpointer (PostgreSQL, Redis)
• Enable LangSmith tracing
• Implement structured tools with Pydantic schemas
• Add timeout limits for agent execution
• Implement rate limiting
• Add comprehensive error handling
• Set up health checks
• Version control prompts and configurations
• Write integration tests for agent workflows

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Source: AI-Foundry-Core/ril-agents — distributed by TomeVault.