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.
apm install @wshobson/langchain-architecture
[](https://apm-p1ls2dz87-atlamors-projects.vercel.app/packages/@wshobson/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.
---
# 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 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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```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)
```