The AI landscape has shifted dramatically. We’re no longer just building chatbots—we’re creating autonomous agents that can reason, plan, and execute complex tasks. LangChain has emerged as the go-to framework for building these intelligent systems.

Photo by Andrea De Santis on Unsplash

What Makes an AI Agent Different?

Traditional chatbots respond to inputs. Agents take action. They can:

• Break down complex goals into subtasks
• Use tools (APIs, databases, search engines)
• Learn from feedback and iterate
• Maintain memory across interactions

Setting Up Your LangChain Environment

pip install langchain langchain-openai langgraph

from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

llm = ChatOpenAI(model="gpt-4-turbo", temperature=0)

Building Your First Agent

Here’s a minimal agent that can search the web and perform calculations:

from langchain_community.tools import DuckDuckGoSearchRun
from langchain_core.tools import tool

search = DuckDuckGoSearchRun()

@tool
def calculator(expression: str) -> str:
    """Evaluate a mathematical expression."""
    return str(eval(expression))

tools = [search, calculator]
agent = create_react_agent(llm, tools)

# Run the agent
result = agent.invoke({
    "messages": [("user", "What's the population of Tokyo divided by 1000?")]
})

The ReAct Pattern

LangChain agents use the ReAct (Reason + Act) pattern:

1. Thought: The agent reasons about what to do
2. Action: It selects and executes a tool
3. Observation: It processes the result
4. Repeat until the task is complete

Photo by Luke Chesser on Unsplash

Adding Memory

Agents become powerful when they remember context:

from langgraph.checkpoint.memory import MemorySaver

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

config = {"configurable": {"thread_id": "user-123"}}
agent.invoke({"messages": [("user", "My name is Alex")]}, config)
agent.invoke({"messages": [("user", "What's my name?")]}, config)
# Returns: "Your name is Alex"

Production Considerations

1. Rate Limiting

from langchain_core.rate_limiters import InMemoryRateLimiter

rate_limiter = InMemoryRateLimiter(requests_per_second=1)
llm = ChatOpenAI(rate_limiter=rate_limiter)

2. Error Handling

from langchain_core.runnables import RunnableConfig

config = RunnableConfig(
    max_concurrency=5,
    recursion_limit=25,
)

3. Observability

Use LangSmith for tracing:

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=your-api-key

When to Use Agents vs. Chains

What’s Next?

The future is multi-agent systems. LangGraph now supports orchestrating multiple specialized agents working together. Imagine a research agent, a coding agent, and a review agent collaborating on a single task.

AI agents are no longer experimental—they’re production-ready. Start building today.

Want to dive deeper? Check out the LangChain documentation and LangGraph tutorials.

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