AI Agent Python Tutorial: Build a Working Agent with LangChain in 30 Minutes

Python is the most popular language for building AI agents. With LangChain, you can create a functional agent that searches the web, reads files, and executes multi-step tasks in under 30 minutes.

This tutorial walks you through building a Python AI agent from scratch -- no prior agent development experience needed. You'll end with a working agent you can extend for any workflow.

In this tutorial:

• Setting up your Python environment
• Building your first agent
• Adding tools to your agent
• Adding memory and context
• Running multi-step workflows
• Deploying your agent
• No-code alternative

Related tutorials: Build AI Agent From Scratch · Autonomous AI Agent Tutorial · JavaScript AI Agent Tutorial · AI Agent Tools Tutorial

Setting Up Your Python Environment

Prerequisites

• Python 3.10 or later
• An OpenAI API key (get one here)
• A terminal or IDE (VS Code recommended)

Step 1: Create a Virtual Environment

mkdir ai-agent-tutorial && cd ai-agent-tutorial
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Step 2: Install Dependencies

pip install langchain langchain-openai langchain-community python-dotenv

Step 3: Set Your API Key

Create a .env file:

OPENAI_API_KEY=sk-your-key-here

Load it in Python:

from dotenv import load_dotenv
load_dotenv()

Building Your First Agent

An AI agent has three components: a language model, a set of tools, and a prompt that tells it how to behave.

The Simplest Agent (5 lines)

from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate

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

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful research assistant. Use tools when needed."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

This sets up the model and prompt. But without tools, the agent is just a chatbot. Let's fix that.

Adding Tools to Your Agent

Tools are functions the agent can call to interact with the outside world. Here are three useful tools for a research agent:

Tool 1: Web Search

from langchain_community.tools.tavily_search import TavilySearchResults

search = TavilySearchResults(max_results=3)

Tool 2: Calculator

from langchain_core.tools import tool

@tool
def calculate(expression: str) -> str:
    """Evaluate a math expression. Input should be a valid Python math expression."""
    try:
        result = eval(expression)
        return f"Result: {result}"
    except Exception as e:
        return f"Error: {e}"

Tool 3: Save to File

@tool
def save_to_file(filename: str, content: str) -> str:
    """Save content to a file. Args: filename (str), content (str)"""
    try:
        with open(filename, "w") as f:
            f.write(content)
        return f"Saved to {filename}"
    except Exception as e:
        return f"Error: {e}"

Wire Everything Together

tools = [search, calculate, save_to_file]

agent = create_tool_calling_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

result = agent_executor.invoke({ "input": "Search for the latest Python release version, then save it to a file called python_version.txt" })

print(result["output"])

When you run this, the agent will:
1. Search the web for the latest Python version
2. Extract the version number
3. Save it to a file
4. Report back

## Adding Memory and Context

By default, agents have no memory of previous interactions. Adding conversation memory is straightforward:

```python
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory

message_history = ChatMessageHistory()

agent_with_memory = RunnableWithMessageHistory(
    agent_executor,
    get_session_history=lambda session_id: message_history,
    input_messages_key="input",
    history_messages_key="chat_history",
)

Now your agent remembers context across messages:

# First message
agent_with_memory.invoke(
    {"input": "My name is Alex and I work on AI agents."},
    config={"configurable": {"session_id": "user-1"}}
)

# Second message - agent remembers your name
agent_with_memory.invoke(
    {"input": "What's my name and what do I work on?"},
    config={"configurable": {"session_id": "user-1"}}
)

Running Multi-Step Workflows

For complex tasks, agents need to plan and execute multiple steps. Here's a research workflow:

research_prompt = ChatPromptTemplate.from_messages([
    ("system", """You are a research agent. Follow this workflow:
    1. Break the research question into sub-questions
    2. Search for information on each sub-question
    3. Synthesize findings into a structured report
    4. Save the report to a file
    
    Always cite your sources."""),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

research_agent = create_tool_calling_agent(llm, tools, research_prompt)
research_executor = AgentExecutor(
    agent=research_agent,
    tools=tools,
    verbose=True,
    max_iterations=10,
)

Run a research task:

result = research_executor.invoke({
    "input": "Compare LangChain vs CrewAI vs AutoGen for building multi-agent systems. Create a comparison table and save it."
})

The agent will autonomously search, compare, and produce a structured output.

Deploying Your Agent

Option 1: REST API with FastAPI

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class AgentRequest(BaseModel):
    message: str

@app.post("/agent")
async def run_agent(request: AgentRequest):
    result = agent_executor.invoke({"input": request.message})
    return {"response": result["output"]}

Run with: uvicorn main:app --host 0.0.0.0 --port 8000

Option 2: Scheduled Tasks with Celery

For background processing, use Celery with Redis:

from celery import Celery

app = Celery('agent_tasks', broker='redis://localhost:6379')

@app.task
def run_research(query: str):
    result = research_executor.invoke({"input": query})
    return result["output"]

Cost Considerations

Building agents with Python means you pay for API tokens directly. Here's what to expect:

Pro tip: Use GPT-4o-mini for simpler tasks to cut costs by 90%. Use GPT-4o only when reasoning quality matters.

For a detailed breakdown, see our AI Agent Cost Calculator.

No-Code Alternative with Ivern

If writing Python isn't your priority, Ivern AI lets you build AI agent squads without code:

• No Python needed -- configure agents through a web interface
• Bring Your Own Key -- use your existing OpenAI or Anthropic API key with zero markup
• Multi-agent squads -- create researcher, writer, and reviewer agents that hand off work automatically
• Built-in tools -- web search, file operations, and more without writing tool functions

Getting started takes 2 minutes: Create a free account, add your API key, and pick a template.

Troubleshooting Common Issues

Agent Loops Infinitely

Set max_iterations on the AgentExecutor:

AgentExecutor(agent=agent, tools=tools, max_iterations=5)

Agent Doesn't Use Tools

Make your tool descriptions specific and include examples:

@tool
def search_web(query: str) -> str:
    """Search the web for current information. 
    Input: a specific search query like 'Python 3.13 release date'
    Returns: list of relevant results with titles and URLs."""

Rate Limiting Errors

Add retry logic:

from langchain_core.runnables import RunnableConfig

config = RunnableConfig(max_concurrency=3)
result = agent_executor.invoke({"input": "..."}, config=config)

Next Steps

Now that you have a working Python agent, here's where to go:

1. Add more tools -- file I/O, database queries, API calls
2. Build multi-agent systems -- see our Multi-Agent Tutorial
3. Add RAG -- connect your agent to a knowledge base with our AI Agent RAG Tutorial
4. Deploy to production -- containerize with Docker and deploy on your preferred cloud

Ready to skip the code? Try Ivern AI free and build your first agent squad in 2 minutes.

Related guides: How to Build an AI Agent · AI Agent Framework Comparison · AI Agent Pricing Benchmarks