title: "LangChain Components Explained: Chains, Agents, and Memory" publishedAt: "2025-01-11" summary: "Deep dive into LangChain's core components - chains, agents, and memory. Learn their architecture, when to use each, and how they work together with practical examples and visual diagrams." keywords: ["langchain components", "langchain architecture", "langchain chains", "langchain agents", "langchain memory", "llm orchestration", "ai application development"]
LangChain has revolutionized how we build applications with Large Language Models (LLMs) by providing a modular architecture of components that work together seamlessly. Understanding these core components - chains, agents, and memory - is crucial for building sophisticated AI applications. In this comprehensive guide, we'll explore each component in detail, understand their interactions, and learn when to use each one.
Table of Contents
- Understanding LangChain Architecture
- Chains: Sequential Processing Workflows
- Agents: Autonomous Decision Makers
- Memory: Maintaining Context
- Component Interactions and Integration
- Choosing the Right Component
- Best Practices and Patterns
- Conclusion
Understanding LangChain Architecture
Before diving into individual components, let's understand how LangChain's architecture enables building complex AI applications:
┌─────────────────────────────────────────────────────────────┐
│ LangChain Application │
├─────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Chains │ │ Agents │ │ Memory │ │
│ │ │ │ │ │ │ │
│ │ Sequential │ │ Autonomous │ │ Context │ │
│ │ Processing │◄───┤ Decision │◄───┤ Management │ │
│ │ │ │ Making │ │ │ │
│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │
│ │ │ │ │
│ └───────────────────┴───────────────────┘ │
│ │ │
│ ┌────────▼────────┐ │
│ │ LLM Interface │ │
│ └─────────────────┘ │
└─────────────────────────────────────────────────────────────┘
Each component serves a specific purpose:
- Chains: Define sequential workflows for processing tasks
- Agents: Make autonomous decisions about which tools or actions to use
- Memory: Maintain conversation context and state across interactions
Chains: Sequential Processing Workflows
Chains are the fundamental building blocks in LangChain that allow you to combine multiple components into a sequential workflow. They represent a "chained" sequence of calls to various components.
Chain Architecture
┌─────────────────────────────────────────────────────┐
│ Chain Pipeline │
├─────────────────────────────────────────────────────┤
│ │
│ Input ──► Step 1 ──► Step 2 ──► Step 3 ──► Output │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ [Prompt] [LLM Call] [Parser] │
│ │
└─────────────────────────────────────────────────────┘
Types of Chains
1. LLMChain - The Basic Building Block
The simplest chain that combines a prompt template with an LLM:
from langchain import LLMChain
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
# Define a prompt template
prompt_template = PromptTemplate(
input_variables=["product"],
template="Write a creative product description for {product}. Make it engaging and highlight key benefits."
)
# Create LLM instance
llm = ChatOpenAI(temperature=0.7)
# Create chain
product_chain = LLMChain(
llm=llm,
prompt=prompt_template,
verbose=True # Shows intermediate steps
)
# Run the chain
result = product_chain.run(product="wireless noise-canceling headphones")
print(result)
2. Sequential Chain - Connecting Multiple Chains
Sequential chains allow you to connect multiple chains where the output of one becomes the input of the next:
from langchain.chains import SimpleSequentialChain
# First chain: Generate product description
description_prompt = PromptTemplate(
input_variables=["product"],
template="Write a detailed description for {product}"
)
description_chain = LLMChain(llm=llm, prompt=description_prompt)
# Second chain: Extract key features
features_prompt = PromptTemplate(
input_variables=["description"],
template="Extract 5 key features from this description: {description}"
)
features_chain = LLMChain(llm=llm, prompt=features_prompt)
# Combine chains
sequential_chain = SimpleSequentialChain(
chains=[description_chain, features_chain],
verbose=True
)
result = sequential_chain.run("smart fitness tracker")
3. RouterChain - Dynamic Routing
Router chains dynamically select which chain to use based on input:
from langchain.chains.router import RouterChain, MultiPromptChain
from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser
from langchain.prompts import PromptTemplate
# Define destination chains
physics_template = """You are a physics expert.
Question: {input}
Answer:"""
math_template = """You are a mathematics expert.
Question: {input}
Answer:"""
# Create destination chains
destination_chains = {}
for name, template in [("physics", physics_template), ("math", math_template)]:
prompt = PromptTemplate(template=template, input_variables=["input"])
chain = LLMChain(llm=llm, prompt=prompt)
destination_chains[name] = chain
# Router configuration
destinations = [
{"name": "physics", "description": "Good for physics questions"},
{"name": "math", "description": "Good for math questions"},
]
router_template = """Given the input, select the best destination.
{destinations}
Input: {input}
Destination:"""
router_prompt = PromptTemplate(
template=router_template,
input_variables=["input", "destinations"]
)
router_chain = LLMRouterChain.from_llm(
llm,
router_prompt,
destinations=destinations
)
# Create the multi-prompt chain
chain = MultiPromptChain(
router_chain=router_chain,
destination_chains=destination_chains,
default_chain=destination_chains["math"],
verbose=True
)
# Test routing
result = chain.run("What is the formula for kinetic energy?")
Advanced Chain Patterns
┌─────────────────────────────────────────────────────────┐
│ Transform Chain Pattern │
├─────────────────────────────────────────────────────────┤
│ │
│ Input │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Transform │ │
│ │ Chain │ │
│ └──────┬──────┘ │
│ │ │
│ ┌────┴────┬─────────┬──────────┐ │
│ ▼ ▼ ▼ ▼ │
│ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ │
│ │Chain1│ │Chain2│ │Chain3│ │Chain4│ │
│ └──┬───┘ └──┬───┘ └──┬───┘ └──┬───┘ │
│ │ │ │ │ │
│ └────────┴────────┴─────────┘ │
│ │ │
│ ┌────▼────┐ │
│ │ Combine │ │
│ └────┬────┘ │
│ │ │
│ Output │
└─────────────────────────────────────────────────────────┘
Agents: Autonomous Decision Makers
Agents are perhaps the most powerful component in LangChain. Unlike chains which follow a predetermined sequence, agents can dynamically decide which actions to take based on the input and available tools.
Agent Architecture
┌───────────────────────────────────────────────────────────┐
│ Agent System │
├───────────────────────────────────────────────────────────┤
│ │
│ User Input │
│ │ │
│ ▼ │
│ ┌─────────────┐ ┌──────────────┐ │
│ │ Agent │◄────┤ Memory │ │
│ │ (LLM + │ │ (Optional) │ │
│ │ Reasoning) │ └──────────────┘ │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Action │ │
│ │ Selection │ │
│ └──────┬──────┘ │
│ │ │
│ ┌────┴────────────┬──────────────┬──────────┐ │
│ ▼ ▼ ▼ ▼ │
│ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ │
│ │Tool 1│ │Tool 2│ │Tool 3│ │Tool N│ │
│ └──┬───┘ └──┬───┘ └──┬───┘ └──┬───┘ │
│ │ │ │ │ │
│ └───────────────┴──────────────┴──────────┘ │
│ │ │
│ ┌────▼────┐ │
│ │ Output │ │
│ └─────────┘ │
└───────────────────────────────────────────────────────────┘
Types of Agents
1. Zero-shot ReAct Agent
The most common agent type that reasons about actions without examples:
from langchain.agents import create_react_agent, AgentExecutor
from langchain.tools import Tool
from langchain.prompts import PromptTemplate
import requests
# Define custom tools
def search_wikipedia(query: str) -> str:
"""Search Wikipedia for information."""
response = requests.get(
"https://en.wikipedia.org/w/api.php",
params={
"action": "query",
"list": "search",
"srsearch": query,
"format": "json"
}
)
return str(response.json()["query"]["search"][:3])
def calculate(expression: str) -> str:
"""Perform mathematical calculations."""
try:
result = eval(expression)
return f"The result is: {result}"
except:
return "Invalid mathematical expression"
# Create tools
tools = [
Tool(
name="Wikipedia",
func=search_wikipedia,
description="Search Wikipedia for factual information"
),
Tool(
name="Calculator",
func=calculate,
description="Perform mathematical calculations"
)
]
# Create ReAct prompt
react_prompt = PromptTemplate.from_template("""
Answer the following questions as best you can. You have access to the following tools:
{tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: {input}
Thought: {agent_scratchpad}
""")
# Create agent
agent = create_react_agent(
llm=llm,
tools=tools,
prompt=react_prompt
)
# Create executor
agent_executor = AgentExecutor(
agent=agent,
tools=tools,
verbose=True,
max_iterations=5
)
# Run agent
result = agent_executor.invoke({
"input": "What is the population of Tokyo and what is 15% of that number?"
})
2. Conversational Agent
An agent that maintains conversation history:
from langchain.agents import create_openai_functions_agent
from langchain.memory import ConversationBufferMemory
from langchain.prompts import MessagesPlaceholder
# Create memory
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
# Create prompt with memory
prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful assistant with access to tools."),
MessagesPlaceholder(variable_name="chat_history"),
("user", "{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
])
# Create conversational agent
conversational_agent = create_openai_functions_agent(
llm=llm,
tools=tools,
prompt=prompt
)
# Create executor with memory
agent_executor = AgentExecutor(
agent=conversational_agent,
tools=tools,
memory=memory,
verbose=True
)
# Have a conversation
result1 = agent_executor.invoke({"input": "What's the capital of France?"})
result2 = agent_executor.invoke({"input": "What's the population of that city?"})
3. Plan-and-Execute Agent
An agent that creates a plan before executing:
from langchain.experimental.plan_and_execute import (
PlanAndExecute,
load_agent_executor,
load_chat_planner
)
# Create planner
planner = load_chat_planner(llm)
# Create executor
executor = load_agent_executor(
llm,
tools,
verbose=True
)
# Create plan-and-execute agent
plan_agent = PlanAndExecute(
planner=planner,
executor=executor,
verbose=True
)
# Run complex task
result = plan_agent.run(
"Research the history of artificial intelligence, "
"identify three key milestones, and calculate the years "
"between each milestone."
)
Agent Decision Flow
┌─────────────────────────────────────────────────┐
│ Agent Decision Process │
├─────────────────────────────────────────────────┤
│ │
│ Start ──► Receive Input │
│ │ │
│ ▼ │
│ ┌─────────┐ │
│ │ Think │ │
│ └────┬────┘ │
│ │ │
│ ▼ │
│ ┌─────────┐ Yes ┌─────────┐ │
│ │ Need ├────────────┤ Select │ │
│ │ Tool? │ │ Tool │ │
│ └────┬────┘ └────┬────┘ │
│ │ No │ │
│ │ ▼ │
│ │ ┌─────────┐ │
│ │ │ Execute │ │
│ │ │ Tool │ │
│ │ └────┬────┘ │
│ │ │ │
│ │ ┌────────────┘ │
│ ▼ ▼ │
│ ┌─────────────────┐ │
│ │ Generate Answer │ │
│ └────────┬────────┘ │
│ │ │
│ ▼ │
│ Output │
└─────────────────────────────────────────────────┘
Memory: Maintaining Context
Memory in LangChain allows applications to maintain state and context across interactions. This is crucial for building conversational applications and maintaining coherent long-term interactions.
Memory Architecture
┌──────────────────────────────────────────────────────┐
│ Memory System │
├──────────────────────────────────────────────────────┤
│ │
│ ┌─────────────┐ ┌─────────────┐ │
│ │ Input │ │ Output │ │
│ │ Messages │ │ Messages │ │
│ └──────┬──────┘ └──────▲──────┘ │
│ │ │ │
│ ▼ │ │
│ ┌─────────────────────────────────┐ │
│ │ Memory Interface │ │
│ ├─────────────────────────────────┤ │
│ │ • save_context() │ │
│ │ • load_memory_variables() │ │
│ │ • clear() │ │
│ └──────────────┬──────────────────┘ │
│ │ │
│ ┌────────────┴────────────┐ │
│ ▼ ▼ │
│ ┌──────────────┐ ┌──────────────┐ │
│ │ Short-term │ │ Long-term │ │
│ │ Memory │ │ Memory │ │
│ │ │ │ │ │
│ │ • Buffer │ │ • Summary │ │
│ │ • Window │ │ • Vector DB │ │
│ │ • Token Limit│ │ • Knowledge │ │
│ └──────────────┘ └──────────────┘ │
└──────────────────────────────────────────────────────┘
Types of Memory
1. ConversationBufferMemory
The simplest memory type that stores the entire conversation:
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain
# Create memory
memory = ConversationBufferMemory()
# Create conversation chain
conversation = ConversationChain(
llm=llm,
memory=memory,
verbose=True
)
# Have a conversation
print(conversation.predict(input="Hi, my name is Alice"))
print(conversation.predict(input="What's my name?"))
# Inspect memory
print(memory.buffer)
2. ConversationBufferWindowMemory
Maintains only the last K interactions:
from langchain.memory import ConversationBufferWindowMemory
# Keep last 3 exchanges
window_memory = ConversationBufferWindowMemory(k=3)
conversation = ConversationChain(
llm=llm,
memory=window_memory,
verbose=True
)
# Long conversation
for i in range(5):
response = conversation.predict(
input=f"This is message {i+1}. Remember this number."
)
print(f"Turn {i+1}: {response}")
# Check what's remembered
print(conversation.predict(input="What numbers do you remember?"))
3. ConversationSummaryMemory
Summarizes conversations to save tokens:
from langchain.memory import ConversationSummaryMemory
# Create summary memory
summary_memory = ConversationSummaryMemory(llm=llm)
conversation = ConversationChain(
llm=llm,
memory=summary_memory,
verbose=True
)
# Long conversation
long_text = """
I want to tell you about my startup. We're building a platform for
sustainable agriculture using IoT sensors and AI. The sensors monitor
soil conditions, weather patterns, and crop health. Our AI then provides
recommendations for optimal planting times, irrigation schedules, and
pest management strategies.
"""
response = conversation.predict(input=long_text)
print(response)
# Check summary
print(f"Summary: {summary_memory.buffer}")
4. VectorStore Memory
Uses embeddings for semantic search through conversation history:
from langchain.memory import VectorStoreRetrieverMemory
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
# Create embeddings and vector store
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_texts([], embeddings)
# Create retriever memory
retriever = vectorstore.as_retriever(search_kwargs=dict(k=2))
vector_memory = VectorStoreRetrieverMemory(retriever=retriever)
# Add memories
vector_memory.save_context(
{"input": "My favorite programming language is Python"},
{"output": "Python is a great choice!"}
)
vector_memory.save_context(
{"input": "I also enjoy working with JavaScript"},
{"output": "JavaScript is excellent for web development"}
)
# Retrieve relevant memories
relevant_docs = vector_memory.load_memory_variables(
{"input": "What programming languages do I know?"}
)
print(relevant_docs)
Memory Patterns Comparison
┌─────────────────────────────────────────────────────────────┐
│ Memory Pattern Comparison │
├───────────────┬─────────────┬─────────────┬─────────────────┤
│ Pattern │ Storage │ Pros │ Cons │
├───────────────┼─────────────┼─────────────┼─────────────────┤
│ Buffer │ Everything │ • Simple │ • Token limit │
│ │ │ • Complete │ • Expensive │
├───────────────┼─────────────┼─────────────┼─────────────────┤
│ Window │ Last K │ • Efficient │ • Loses context │
│ │ turns │ • Bounded │ • Arbitrary │
├───────────────┼─────────────┼─────────────┼─────────────────┤
│ Summary │ Compressed │ • Efficient │ • Info loss │
│ │ summary │ • Scalable │ • Summary cost │
├───────────────┼─────────────┼─────────────┼─────────────────┤
│ Vector │ Embeddings │ • Semantic │ • Complex │
│ │ │ • Scalable │ • Retrieval cost│
└───────────────┴─────────────┴─────────────┴─────────────────┘
Component Interactions and Integration
The real power of LangChain comes from combining these components. Here's how they work together:
Chain with Memory
from langchain.prompts import (
ChatPromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate
)
# Create prompt with memory placeholder
prompt = ChatPromptTemplate(
messages=[
SystemMessagePromptTemplate.from_template(
"You are a helpful AI assistant. Use the conversation history to provide context-aware responses."
),
MessagesPlaceholder(variable_name="history"),
HumanMessagePromptTemplate.from_template("{input}")
]
)
# Create chain with memory
memory = ConversationBufferMemory(return_messages=True)
conversation_chain = LLMChain(
llm=llm,
prompt=prompt,
memory=memory
)
# Use the chain
response1 = conversation_chain.predict(input="My name is Bob and I'm learning LangChain")
response2 = conversation_chain.predict(input="What am I learning about?")
Agent with Memory and Custom Tools
from langchain.tools import StructuredTool
from langchain.agents import initialize_agent, AgentType
from pydantic import BaseModel, Field
# Define tool input schema
class CalculatorInput(BaseModel):
expression: str = Field(description="Mathematical expression to evaluate")
# Create structured tool
def advanced_calculator(expression: str) -> str:
"""Evaluate complex mathematical expressions."""
try:
# Safe evaluation
allowed_names = {
k: v for k, v in math.__dict__.items() if not k.startswith("__")
}
result = eval(expression, {"__builtins__": {}}, allowed_names)
return f"Result: {result}"
except Exception as e:
return f"Error: {str(e)}"
calculator_tool = StructuredTool.from_function(
func=advanced_calculator,
name="AdvancedCalculator",
description="Perform complex mathematical calculations",
args_schema=CalculatorInput
)
# Create agent with memory
agent_memory = ConversationSummaryBufferMemory(
llm=llm,
max_token_limit=200,
return_messages=True
)
agent_with_memory = initialize_agent(
tools=[calculator_tool, search_tool],
llm=llm,
agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
memory=agent_memory,
verbose=True
)
# Complex interaction
result = agent_with_memory.run(
"Calculate the compound interest on $10,000 at 5% annual rate for 10 years, "
"then search for current inflation rates to compare"
)
Complex Workflow Integration
┌────────────────────────────────────────────────────────────┐
│ Integrated Workflow Example │
├────────────────────────────────────────────────────────────┤
│ │
│ User Input │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Memory │◄─────────────────┐ │
│ │ Retrieval │ │ │
│ └──────┬──────┘ │ │
│ │ │ │
│ ▼ │ │
│ ┌─────────────┐ │ │
│ │ Router │ │ │
│ │ Chain │ │ │
│ └──────┬──────┘ │ │
│ │ │ │
│ ┌────┴────┐ │ │
│ ▼ ▼ │ │
│ ┌──────┐ ┌──────┐ │ │
│ │Chain │ │Agent │ │ │
│ │Path │ │Path │ │ │
│ └──┬───┘ └──┬───┘ │ │
│ │ │ │ │
│ │ ▼ │ │
│ │ ┌──────────┐ │ │
│ │ │ Tools │ │ │
│ │ └────┬─────┘ │ │
│ │ │ │ │
│ └────────┴─────────────────────┘ │
│ │ │
│ ┌────▼────┐ │
│ │ Output │ │
│ └─────────┘ │
└────────────────────────────────────────────────────────────┘
Choosing the Right Component
When to Use Chains vs Agents
Use Chains when:
- You have a predetermined sequence of operations
- The workflow is predictable and doesn't require dynamic decision-making
- You need consistent, reproducible results
- Performance is critical (chains are generally faster)
- You're building simple to moderately complex workflows
Use Agents when:
- The task requires dynamic decision-making
- You need to select from multiple tools based on context
- The workflow varies significantly based on input
- You're building autonomous systems
- You need reasoning capabilities
Decision Matrix
┌─────────────────────────────────────────────────────────────┐
│ Component Selection Guide │
├─────────────────┬────────────┬────────────┬────────────────┤
│ Requirement │ Chain │ Agent │ Memory │
├─────────────────┼────────────┼────────────┼────────────────┤
│ Fixed workflow │ ✓ │ │ │
│ Dynamic routing │ ✓ │ ✓ │ │
│ Tool selection │ │ ✓ │ │
│ Context aware │ │ │ ✓ │
│ Multi-turn │ ✓ │ ✓ │ ✓ │
│ Reasoning │ │ ✓ │ │
│ Performance │ ✓ │ │ │
│ Complexity │ Low │ High │ Medium │
└─────────────────┴────────────┴────────────┴────────────────┘
Best Practices and Patterns
1. Chain Design Best Practices
# Good: Modular chain design
class ChainFactory:
@staticmethod
def create_analysis_chain(llm):
"""Create a reusable analysis chain."""
prompt = PromptTemplate(
input_variables=["text", "analysis_type"],
template="""
Analyze the following text for {analysis_type}:
Text: {text}
Provide a structured analysis with key findings.
"""
)
return LLMChain(llm=llm, prompt=prompt)
@staticmethod
def create_summary_chain(llm):
"""Create a reusable summary chain."""
prompt = PromptTemplate(
input_variables=["content"],
template="Summarize this content in 3 bullet points: {content}"
)
return LLMChain(llm=llm, prompt=prompt)
# Use factory pattern
analyzer = ChainFactory.create_analysis_chain(llm)
summarizer = ChainFactory.create_summary_chain(llm)
# Combine in sequential chain
combined_chain = SimpleSequentialChain(
chains=[analyzer, summarizer],
verbose=True
)
2. Agent Error Handling
from langchain.callbacks import StdOutCallbackHandler
from langchain.agents import AgentExecutor
class SafeAgentExecutor:
def __init__(self, agent, tools, max_retries=3):
self.executor = AgentExecutor(
agent=agent,
tools=tools,
max_iterations=5,
early_stopping_method="generate",
handle_parsing_errors=True,
callbacks=[StdOutCallbackHandler()]
)
self.max_retries = max_retries
def run(self, input_text):
for attempt in range(self.max_retries):
try:
result = self.executor.invoke({"input": input_text})
return result
except Exception as e:
if attempt == self.max_retries - 1:
return f"Failed after {self.max_retries} attempts: {str(e)}"
continue
# Use safe executor
safe_agent = SafeAgentExecutor(agent, tools)
result = safe_agent.run("Complex query that might fail")
3. Memory Optimization
class OptimizedMemory:
def __init__(self, llm, max_tokens=1000):
# Combine multiple memory types
self.buffer = ConversationBufferWindowMemory(
k=5, # Recent context
return_messages=True
)
self.summary = ConversationSummaryMemory(
llm=llm,
return_messages=True
)
self.max_tokens = max_tokens
def save_context(self, inputs, outputs):
# Save to both memories
self.buffer.save_context(inputs, outputs)
self.summary.save_context(inputs, outputs)
def load_memory_variables(self, inputs):
# Combine recent and summary
buffer_vars = self.buffer.load_memory_variables(inputs)
summary_vars = self.summary.load_memory_variables(inputs)
return {
"recent_history": buffer_vars.get("history", []),
"summary": summary_vars.get("history", [])
}
4. Component Integration Pattern
class IntegratedAssistant:
def __init__(self, llm, tools):
self.llm = llm
self.tools = tools
self.memory = ConversationSummaryBufferMemory(
llm=llm,
max_token_limit=200,
return_messages=True
)
self.router = self._create_router()
self.agent = self._create_agent()
def _create_router(self):
# Route between chain and agent based on complexity
router_prompt = PromptTemplate(
input_variables=["input"],
template="""
Classify this request as 'simple' or 'complex':
{input}
Simple: Direct questions, basic calculations
Complex: Multi-step tasks, research, tool usage
Classification:"""
)
return LLMChain(llm=self.llm, prompt=router_prompt)
def _create_agent(self):
return initialize_agent(
tools=self.tools,
llm=self.llm,
agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
memory=self.memory,
verbose=True
)
def process(self, user_input):
# Determine routing
classification = self.router.run(user_input).strip().lower()
if "simple" in classification:
# Use direct chain
simple_chain = LLMChain(
llm=self.llm,
prompt=PromptTemplate(
input_variables=["input"],
template="Answer this question concisely: {input}"
)
)
return simple_chain.run(user_input)
else:
# Use agent for complex tasks
return self.agent.run(user_input)
Conclusion
Understanding LangChain's core components - chains, agents, and memory - is essential for building sophisticated AI applications. Each component serves a specific purpose:
- Chains provide predictable, sequential processing workflows perfect for structured tasks
- Agents offer autonomous decision-making capabilities for dynamic, complex scenarios
- Memory maintains context and state, enabling coherent multi-turn interactions
The true power emerges when these components work together, creating intelligent systems that can reason, remember, and respond appropriately to varied inputs. As you build with LangChain, consider:
- Start simple with chains for predictable workflows
- Graduate to agents when you need dynamic behavior
- Always consider memory for conversational applications
- Test component interactions thoroughly
- Monitor performance and optimize accordingly
By mastering these components and their interactions, you'll be well-equipped to build production-ready AI applications that leverage the full power of large language models.
Further Reading
Ready to dive deeper? Check out these related articles:
- Getting Started with LangChain: A Beginner's Guide
- Building Your First LangChain Application
- LangChain Production Best Practices
Remember, the best way to understand these components is through hands-on experimentation. Start with simple examples and gradually increase complexity as you become comfortable with each component's capabilities and limitations.