LangChain provides a powerful framework for building AI agents that can reason, plan, and execute tasks. This comprehensive guide walks you through creating sophisticated AI agents.
Understanding LangChain
LangChain offers a modular approach to building LLM applications with several key components:
- Models: Interface with different LLMs
- Prompts: Template and manage prompts effectively
- Chains: Combine multiple components
- Memory: Store and retrieve context
- Tools: Enable agents to take actions
Basic Agent Setup
from langchain.agents import Tool, AgentExecutor
from langchain.prompts import StringPromptTemplate
from langchain import OpenAI, LLMChain
# Initialize LLM
llm = OpenAI(temperature=0)
# Define tools
tools = [
Tool(
name="Search",
func=search_function,
description="Search for information"
)
]
# Create agent
agent = create_agent(llm=llm, tools=tools)Tools the Agent Can Use
Tools are the agent's hands. Wrap any function with a description the LLM can reason about:
from langchain.tools import tool
@tool
def search_orders(email: str) -> list[dict]:
"""Look up the most recent orders for a customer by email."""
return db.query("SELECT * FROM orders WHERE email = %s ORDER BY created_at DESC LIMIT 20", email)
@tool
def issue_refund(order_id: str, amount: float, reason: str) -> dict:
"""Issue a refund for a specific order. Requires human approval for amounts over $500."""
if amount > 500:
raise NeedsApproval(order_id, amount, reason)
return stripe.refunds.create(charge=order_id, amount=int(amount * 100))Memory and Context
LangChain ships several memory backends: ConversationBufferMemory for short sessions, ConversationSummaryMemory when context grows, and vector-backed memory for semantic recall across sessions.
LangGraph for Complex Flows
Once you need branching logic, retries, or multi-agent handoffs, drop into langgraph. Express your agent as a state machine with explicit nodes and edges — far easier to debug than a free-form ReAct loop.
Streaming and Observability
- Stream tokens via
llm.stream()for snappy UX - Trace every chain with LangSmith — invaluable when debugging tool-call failures
- Log token usage per step to control cost
Common Pitfalls
- Vague tool descriptions — the LLM cannot pick the right tool
- Unbounded loops — always cap
max_iterations - Letting the agent execute irreversible actions without approval
- Skipping evals — agents regress silently as you change prompts and models
Conclusion
LangChain makes building AI agents accessible and powerful. Start with simple agents, define tools with strict schemas, layer in memory, graduate to LangGraph for complex flows, and instrument everything with LangSmith. The framework keeps maturing — your engineering rigor is what turns a demo into a product.
