How I Built a Production AI Agent in Python for $5/month
When I started building AI agents last year, I was shocked by the bills. A simple chatbot prototype using GPT-4 cost me $200 in a week. That's when I realized there had to be a better way, especially for developers just starting with AI or building side projects.
After experimenting with various approaches, I discovered that using open-source models through OpenRouter could deliver comparable results at a fraction of the cost. In this guide, I'll walk you through building a fully functional AI agent that costs roughly $5/month to run—and I'll show you the exact code and cost breakdowns.
Understanding the Economics of AI APIs
Before jumping into code, let's talk money. Here's what I was paying:
- GPT-4: $0.03 per 1K input tokens, $0.06 per 1K output tokens
- GPT-3.5: $0.0005 per 1K input tokens, $0.0015 per 1K output tokens
- Claude 3 Opus: $0.015 per 1K input tokens, $0.075 per 1K output tokens
A single conversation with 5 back-and-forths using GPT-4 could easily cost $1-2. Scale that to production, and you're looking at hundreds per month.
OpenRouter changed the game for me. They aggregate open-source models and charge significantly less:
- Mistral 7B: $0.00014 per 1K input tokens, $0.00042 per 1K output tokens
- Llama 2 70B: $0.0008 per 1K input tokens, $0.0024 per 1K output tokens
- Neural Chat 7B: $0.00007 per 1K input tokens, $0.00021 per 1K output tokens
The quality difference? For many agent tasks, it's negligible. For my use case (a customer support agent), Mistral 7B performed nearly identically to GPT-3.5.
Setting Up Your Environment
First, let's get the basics in place. You'll need Python 3.8+ and a few dependencies:
pip install openai python-dotenv pydantic
Sign up for OpenRouter at openrouter.io and grab your API key. They offer a free tier with $5 in credits—perfect for testing.
Create a .env file:
OPENROUTER_API_KEY=your_key_here
Building Your First Agent
An AI agent differs from a simple chatbot in one crucial way: it can take actions. It doesn't just respond—it reasons, plans, and executes tasks.
Here's a production-ready agent that can research topics, perform calculations, and maintain context:
import os
import json
import re
from typing import Any
from dotenv import load_dotenv
import openai
load_dotenv()
# Configure OpenRouter
openai.api_key = os.getenv("OPENROUTER_API_KEY")
openai.api_base = "https://openrouter.ai/api/v1"
class Agent:
def __init__(self, model: str = "mistralai/mistral-7b-instruct"):
self.model = model
self.conversation_history = []
self.tools = {
"calculate": self._calculate,
"search": self._search,
"get_time": self._get_time,
}
def _calculate(self, expression: str) -> str:
"""Execute a mathematical expression safely"""
try:
# Only allow safe operations
allowed_names = {"__builtins__": {}}
result = eval(expression, allowed_names)
return f"Result: {result}"
except Exception as e:
return f"Error: {str(e)}"
def _search(self, query: str) -> str:
"""Simulate a search operation"""
# In production, integrate with a real search API
return f"Search results for '{query}': [Mock data - integrate with actual search API]"
def _get_time(self) -> str:
"""Get current time"""
from datetime import datetime
return datetime.now().isoformat()
def _parse_tool_call(self, response: str) -> tuple[str, dict]:
"""Extract tool calls from model response"""
# Look for tool calls in format: [TOOL: tool_name(param1=value1, param2=value2)]
pattern = r'\[TOOL:\s*(\w+)\((.*?)\)\]'
match = re.search(pattern, response)
if match:
tool_name = match.group(1)
params_str = match.group(2)
# Parse parameters
params = {}
if params_str:
# Simple parameter parsing
for param in params_str.split(','):
if '=' in param:
key, value = param.split('=', 1)
params[key.strip()] = value.strip().strip('"\'')
return tool_name, params
return None, {}
def _execute_tool(self, tool_name: str, params: dict) -> str:
"""Execute a tool and return result"""
if tool_name not in self.tools:
return f"Unknown tool: {tool_name}"
try:
return self.tools[tool_name](**params)
except TypeError as e:
return f"Tool error: {str(e)}"
def think(self, user_message: str, max_iterations: int = 3) -> str:
"""Process a message with agentic loop"""
self.conversation_history.append({
"role": "user",
"content": user_message
})
system_prompt = """You are a helpful AI agent. You have access to the following tools:
- [TOOL: calculate(expression=...)] - Evaluate mathematical expressions
- [TOOL: search(query=...)] - Search for information
- [TOOL: get_time()] - Get current time
When you need to use a tool, format it exactly as shown above within your response.
After using a tool, analyze the result and continue your reasoning.
Be concise and helpful."""
for iteration in range(max_iterations):
# Get model response
response = openai.ChatCompletion.create(
model=self.model,
messages=[{"role": "system", "content": system_prompt}] + self.conversation_history,
temperature=0.7,
max_tokens=500,
)
assistant_message = response.choices[0].message.content
# Check for tool calls
tool_name, params = self._parse_tool_call(assistant_message)
if tool_name:
# Execute tool
tool_result = self._execute_tool(tool_name, params)
# Add to conversation
self.conversation_history.append({
"role": "assistant",
"content": assistant_message
})
self.conversation_history.append({
"role": "user",
"content": f"Tool result: {tool_result}"
})
else:
# No more tools needed, we have the final answer
self.conversation_history.append({
"role": "assistant",
"content": assistant_message
})
return assistant_message
# Max iterations reached
return "Maximum iterations reached. Here's what I found: " + assistant_message
# Example usage
if __name__ == "__main__":
agent = Agent()
# Test the agent
response = agent.think("What is 25 * 4? Then tell me what time it is.")
print("Agent response:", response)
# Continue conversation
response = agent.think("Can you add 100 to the previous result?")
print("Agent response:", response)
Production Considerations
This basic agent works, but production deployments need more. Here's what I added:
Cost Monitoring
python
class CostTracker:
---
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---
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