Code beginner · 3 min read

How to build an AI agent in python with OpenAI

Q: How to build an AI agent in python with OpenAI

Use the OpenAI Python SDK OpenAI client to create a chat completion with a system prompt defining the agent's role and user messages to interact, enabling a basic AI agent in Python.

Direct answer

Use the OpenAI Python SDK OpenAI client to create a chat completion with a system prompt defining the agent's role and user messages to interact, enabling a basic AI agent in Python.

Setup

Install

bash

pip install openai

Env vars

OPENAI_API_KEY

Imports

python

import os
from openai import OpenAI

Examples

inUser: What's the weather like today?

outAgent: I don't have real-time weather data, but you can check a weather website or app for the latest updates.

inUser: Write a Python function to reverse a string.

outAgent: Here's a Python function to reverse a string: ```python def reverse_string(s): return s[::-1] ```

inUser: Summarize the plot of 'The Great Gatsby'.

outAgent: 'The Great Gatsby' is about Jay Gatsby's pursuit of wealth and love in 1920s America, exploring themes of decadence and the American Dream.

Integration steps

Install the OpenAI Python SDK and set your API key in the environment variable OPENAI_API_KEY
Import the OpenAI client and initialize it with the API key from os.environ
Define the agent's behavior with a system message describing its role
Build a messages list including the system message and user input
Call the chat.completions.create method with the chosen model and messages
Extract and display the agent's response from the API output

Full code

python

import os
from openai import OpenAI

# Initialize OpenAI client
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Define a simple AI agent function

def ai_agent(user_input: str) -> str:
    messages = [
        {"role": "system", "content": "You are a helpful AI assistant."},
        {"role": "user", "content": user_input}
    ]
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=messages
    )
    return response.choices[0].message.content

# Example usage
if __name__ == "__main__":
    user_question = "Write a Python function to check if a number is prime."
    answer = ai_agent(user_question)
    print("Agent:", answer)

output

Agent: Here's a Python function to check if a number is prime:

```python
def is_prime(n):
    if n <= 1:
        return False
    for i in range(2, int(n ** 0.5) + 1):
        if n % i == 0:
            return False
    return True
```

API trace

Request

json

{"model": "gpt-4o", "messages": [{"role": "system", "content": "You are a helpful AI assistant."}, {"role": "user", "content": "Write a Python function to check if a number is prime."}]}

Response

json

{"choices": [{"message": {"content": "Here's a Python function to check if a number is prime: ..."}}], "usage": {"total_tokens": 120}}

Extractresponse.choices[0].message.content

Variants

Streaming response version ›

Use streaming to display the agent's response token-by-token for better user experience with long outputs.

python

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain recursion with an example."}
]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    stream=True
)

for chunk in response:
    print(chunk.choices[0].delta.get('content', ''), end='')

Async version ›

Use async when integrating the agent into applications requiring concurrency or non-blocking calls.

python

import os
import asyncio
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

async def ai_agent_async(user_input: str) -> str:
    messages = [
        {"role": "system", "content": "You are a helpful AI assistant."},
        {"role": "user", "content": user_input}
    ]
    response = await client.chat.completions.acreate(
        model="gpt-4o",
        messages=messages
    )
    return response.choices[0].message.content

async def main():
    answer = await ai_agent_async("Explain polymorphism in OOP.")
    print("Agent:", answer)

if __name__ == "__main__":
    asyncio.run(main())

Alternative model: Claude 3.5 Sonnet ›

Use Claude 3.5 Sonnet for stronger coding capabilities or when you prefer Anthropic's API.

python

import os
import anthropic

client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

system_prompt = "You are a helpful AI assistant."
user_input = "Generate a JavaScript function to debounce a button click."

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=512,
    system=system_prompt,
    messages=[{"role": "user", "content": user_input}]
)

print("Agent:", response.content[0].text)

Performance

Latency~800ms for gpt-4o non-streaming calls

Cost~$0.002 per 500 tokens for gpt-4o

Rate limitsTier 1: 500 requests per minute (RPM) / 30,000 tokens per minute (TPM)

Keep system prompts concise to reduce token usage
Batch user inputs when possible to minimize calls
Use shorter model variants like gpt-4o-mini for cost savings on simpler tasks

Approach	Latency	Cost/call	Best for
Standard call (gpt-4o)	~800ms	~$0.002	General purpose AI agents
Streaming (gpt-4o)	Starts ~300ms, streams tokens	~$0.002	Long responses, better UX
Async (gpt-4o)	~800ms	~$0.002	Concurrent or non-blocking apps
Claude 3.5 Sonnet	~700ms	~$0.0025	Advanced coding and reasoning tasks

✓

Quick tip

Define your agent's role clearly in the system message to guide consistent and relevant responses.

⚠

Common mistake

Forgetting to set the API key in the environment variable or using deprecated SDK methods causes authentication or runtime errors.

Verified 2026-04 · gpt-4o, claude-3-5-sonnet-20241022

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