How I Built a Production AI Agent for $5/month Using Open Source + OpenRouter

I spent months watching my cloud bill climb as I experimented with AI agents. Claude API calls were adding up fast, GPT-4 wasn't cheap, and I kept wondering if there was a better way. Then I discovered the combination of open-source models and OpenRouter's pay-per-use pricing, and everything changed.

Today, I'm running a production AI agent that handles customer support tickets, generates reports, and processes data—all for around $5 per month. Not a typo. This is the complete breakdown of how I did it, with the actual code and architecture decisions that made it possible.

Why Open Models + OpenRouter?

Before diving into the technical setup, let me explain the economics. Traditional AI API pricing works like this: you pay per token, and premium models like Claude 3.5 Sonnet cost $3 per million input tokens and $15 per million output tokens. If you're processing hundreds of requests daily, that adds up quickly.

OpenRouter is a routing API that aggregates access to dozens of models—both premium ones and open-source alternatives. The key insight is that open-source models like Mistral, Llama 2, and Qwen are significantly cheaper (sometimes 90% less) while being surprisingly capable for many real-world tasks.

My agent uses Mistral 7B for most tasks ($0.14 per million tokens) and falls back to slightly larger models only when needed. The math: 1 million tokens per month costs $0.14. Even with heavy usage, you're looking at $3-8 monthly.

Architecture Overview

Here's the system I built:

┌─────────────────┐
│  Trigger Event  │
│  (Webhook)      │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Agent Router   │
│  (Determine     │
│   task type)    │
└────────┬────────┘
         │
    ┌────┴────┬─────────┬──────────┐
    │          │         │          │
    ▼          ▼         ▼          ▼
┌────────┐ ┌──────┐ ┌──────┐ ┌──────────┐
│ Support│ │Data  │ │Report│ │Fallback  │
│Handler │ │Parser│ │Gen   │ │(Claude)  │
└────────┘ └──────┘ └──────┘ └──────────┘
    │          │         │          │
    └────┬─────┴────┬────┴──────┬──┘
         │          │           │
         ▼          ▼           ▼
    ┌──────────────────────────────┐
    │  OpenRouter API              │
    │  (Route to cheapest model)   │
    └──────────────────────────────┘

The agent evaluates incoming requests, routes them to the appropriate handler, and uses OpenRouter to call the most cost-effective model that can handle the task.

Setting Up OpenRouter

First, create an account at openrouter.ai. The setup takes two minutes. You'll get an API key and can immediately start making requests.

Here's the basic Python setup:

import os
import requests
from typing import Optional

class OpenRouterClient:
    def __init__(self, api_key: Optional[str] = None):
        self.api_key = api_key or os.getenv("OPENROUTER_API_KEY")
        self.base_url = "https://openrouter.io/api/v1"
        self.headers = {
            "Authorization": f"Bearer {self.api_key}",
            "HTTP-Referer": "https://yourapp.com",
            "X-Title": "Your App Name"
        }

    def create_message(self, model: str, messages: list, temperature: float = 0.7, max_tokens: int = 1000) -> dict:
        """Send a message to OpenRouter and get a response."""
        payload = {
            "model": model,
            "messages": messages,
            "temperature": temperature,
            "max_tokens": max_tokens
        }

        response = requests.post(
            f"{self.base_url}/chat/completions",
            headers=self.headers,
            json=payload
        )

        if response.status_code != 200:
            raise Exception(f"OpenRouter API error: {response.text}")

        return response.json()

# Usage
client = OpenRouterClient()
response = client.create_message(
    model="mistralai/mistral-7b-instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is 2+2?"}
    ]
)

print(response["choices"][0]["message"]["content"])

Building the Intelligent Router

The real magic happens in the agent's decision-making layer. Instead of sending every request to the same model, I built a router that evaluates task complexity and routes accordingly:

from enum import Enum
from dataclasses import dataclass

class TaskComplexity(Enum):
    SIMPLE = "simple"
    MODERATE = "moderate"
    COMPLEX = "complex"

@dataclass
class TaskRoute:
    model: str
    temperature: float
    max_tokens: int
    cost_per_mtok: float

class AgentRouter:
    def __init__(self, client: OpenRouterClient):
        self.client = client
        self.routes = {
            TaskComplexity.SIMPLE: TaskRoute(
                model="mistralai/mistral-7b-instruct",
                temperature=0.3,
                max_tokens=500,
                cost_per_mtok=0.14
            ),
            TaskComplexity.MODERATE: TaskRoute(
                model="mistralai/mistral-medium",
                temperature=0.5,
                max_tokens=2000,
                cost_per_mtok=0.27
            ),
            TaskComplexity.COMPLEX: TaskRoute(
                model="meta-llama/llama-2-70b-chat",
                temperature=0.7,
                max_tokens=4000,
                cost_per_mtok=0.63
            )
        }

    def evaluate_complexity(self, query: str) -> TaskComplexity:
        """Determine task complexity from the query."""
        # Simple heuristics - you could use ML here
        if len(query) < 50 and query.count("?") == 1:
            return TaskComplexity.SIMPLE
        elif len(query) < 300:
            return TaskComplexity.MODERATE
        else:
            return TaskComplexity.COMPLEX

    def process(self, query: str, system_prompt: str) -> dict:
        """Route and process a query."""
        complexity = self.evaluate_complexity(query)
        route = self.routes[complexity]

        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": query}
        ]

        response = self.client.create_message(
            model=route.model,
            messages=messages,
            temperature=route.temperature,
            max_tokens=route.max_tokens
        )

        # Extract usage data for cost tracking
        usage = response.get("usage", {})
        estimated_cost = (
            (usage.get("prompt_tokens", 0) / 1_000_000 * route.cost_per_mtok) +
            (usage.get("completion_tokens", 0) / 1_000_000 * route.cost_per_mtok * 3)
        )

        return {
            "content": response["choices"][0]["message"]["content"],
            "model": route.model,
            "tokens_used": usage,
            "estimated_cost": estimated_cost
        }

Real-World Implementation: Support Ticket Handler

Here's how I

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