Build a Personal AI Coding Assistant in 30 Minutes with Python and OpenRouter
Stop paying $20/month for ChatGPT Plus when you can build an AI coding assistant that runs on your own terms, costs pennies per month, and integrates directly into your workflow. I built mine last Tuesday in under 30 minutes. It's running right now, answering my code questions, debugging my functions, and generating boilerplate without touching OpenAI's premium tiers.
Here's what changed: I stopped treating AI as a service and started treating it as infrastructure I own.
This guide walks you through building a production-ready AI coding assistant using Python, OpenRouter (which routes to GPT-4, Claude, and others at 50-70% cheaper rates than direct APIs), and a simple HTTP server you can deploy to DigitalOcean for $5/month or run locally. By the end, you'll have a tool that understands your codebase context, remembers your preferences, and sits in your terminal waiting for questions.
Why OpenRouter Instead of Direct APIs?
Before we code, let's talk economics. OpenAI's GPT-4 costs $0.03 per 1K input tokens. Claude 3 Opus runs $0.015 per 1K tokens. OpenRouter aggregates these models and negotiates better rates — you get GPT-4 at roughly $0.015 per 1K tokens, with fallback options if one model hits rate limits.
For a solo developer running 50 queries daily, that's $2-5/month instead of $15-20. Scale to a small team, and you're looking at $50+ monthly savings. More importantly: you get model flexibility. If Claude's better at your use case, switch with one line of config. No vendor lock-in.
What You're Building
Your assistant will:
- Accept code snippets and questions via HTTP
- Maintain conversation history (so it remembers context)
- Route requests through OpenRouter to the cheapest available model
- Return structured responses (code, explanation, confidence level)
- Run as a background service
It's not a chatbot UI. It's infrastructure. You'll interact with it via curl, your editor's HTTP client, or a simple Python wrapper.
Prerequisites
You'll need:
- Python 3.9+
- An OpenRouter account (free, takes 60 seconds at openrouter.io)
- 30 minutes
- A code editor
Step 1: Get Your OpenRouter API Key
Head to openrouter.io, sign up with GitHub, and generate an API key. Paste it somewhere safe — you'll need it in 10 minutes.
OpenRouter gives you $5 free credits on signup. That's 100,000+ tokens. Enough to test this entire system.
Step 2: Build the Core Assistant
Create a file called ai_assistant.py:
import os
import json
from datetime import datetime
from typing import Optional
import requests
from dataclasses import dataclass, asdict
@dataclass
class Message:
role: str
content: str
timestamp: str = None
def __post_init__(self):
if self.timestamp is None:
self.timestamp = datetime.now().isoformat()
class CodingAssistant:
def __init__(self, api_key: str, model: str = "gpt-3.5-turbo"):
self.api_key = api_key
self.model = model
self.base_url = "https://openrouter.ai/api/v1"
self.conversation_history = []
self.system_prompt = """You are an expert coding assistant. Your role is to:
1. Help debug code and explain errors clearly
2. Generate clean, production-ready code
3. Suggest optimizations and best practices
4. Explain complex concepts in simple terms
5. Always provide working examples
Keep responses concise but complete. When showing code, use markdown blocks.
Include brief explanations of WHY you're suggesting something, not just WHAT."""
def add_message(self, role: str, content: str):
"""Add a message to conversation history"""
msg = Message(role=role, content=content)
self.conversation_history.append(msg)
def get_conversation_context(self) -> list:
"""Build message list for API call"""
messages = [
{"role": "system", "content": self.system_prompt}
]
# Keep last 10 exchanges to avoid token bloat
for msg in self.conversation_history[-20:]:
messages.append({"role": msg.role, "content": msg.content})
return messages
def query(self, question: str) -> dict:
"""Send question to OpenRouter and get response"""
self.add_message("user", question)
headers = {
"Authorization": f"Bearer {self.api_key}",
"HTTP-Referer": "https://mycodingassistant.local",
"X-Title": "Personal Coding Assistant",
"Content-Type": "application/json",
}
payload = {
"model": self.model,
"messages": self.get_conversation_context(),
"temperature": 0.3, # Lower = more deterministic for code
"max_tokens": 1500,
}
try:
response = requests.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload,
timeout=30
)
response.raise_for_status()
result = response.json()
assistant_message = result["choices"][0]["message"]["content"]
self.add_message("assistant", assistant_message)
return {
"success": True,
"response": assistant_message,
"model": result.get("model"),
"tokens_used": result["usage"]["total_tokens"],
"history_length": len(self.conversation_history)
}
except requests.exceptions.RequestException as e:
return {
"success": False,
"error": str(e),
"response": None
}
def clear_history(self):
"""Start fresh conversation"""
self.conversation_history = []
def export_history(self, filename: str = "conversation.json"):
"""Save conversation for later review"""
with open(filename, 'w') as f:
json.dump(
[asdict(msg) for msg in self.conversation_history],
f,
indent=2
)
This is your core engine. It:
- Maintains conversation history (so follow-up questions work)
- Manages the system prompt (the instructions that shape behavior)
- Handles OpenRouter API calls
- Tracks tokens (so you know what you're spending)
Step 3: Build the HTTP Server
Create server.py:
python
from flask import Flask, request, jsonify
from ai_assistant import CodingAssistant
import os
app = Flask(__name__)
# Initialize assistant on startup
api_key = os.getenv("OPENROUTER_API_KEY")
if not api_key:
raise ValueError("OPENROUTER_API_KEY environment variable not set")
assistant = CodingAssistant(api_key=api_key, model="gpt-3.5-turbo")
@app.route("/ask", methods=["POST"])
def ask():
"""Main endpoint for code questions"""
data = request.json
if not data or "question" not in data:
return jsonify({"error": "Missing 'question' field"}), 400
question = data["question"]
response = assistant.query(question)
return jsonify(response)
@app.route("/history", methods=["GET"])
def get_history():
"""Get conversation history"""
return jsonify({
"messages": [
{
"role": msg.role,
"content": msg.content,
"timestamp": msg.timestamp
}
for msg in assistant.conversation_history
]
})
@app.route("/clear", methods=["POST"])
def clear():
"""Clear conversation history"""
assistant.clear_history()
return jsonify({"status": "history
---
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---
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---
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