Enforce LLM Agent Rules at the API Layer: An Open-Source Proxy Playbook

For developers building GPT agents, the wild west is getting expensive. Here's how to enforce rules at the API layer.

The rise of autonomous LLM agents—like those powered by OpenAI's GPT-5.6 Sol—has created a new problem for engineering teams: how do you keep an agent on the rails when it's making thousands of API calls per hour? Prompt engineering itself can only go so far. The real control point is the API layer.

That's why a growing number of teams are building (or adopting) open-source proxies that sit between the agent and the model provider. One such project, which recently hit 700 GitHub stars, enforces LLM agent rules directly at the API layer—before the prompt ever reaches the model. The approach is both simple and transformative.

In this guide, we'll walk through the key design decisions for building a rule-enforcing proxy, why it's a natural fit for GPT-5.6 Sol, and how OneMux can simplify the multi-model infrastructure side of the equation.

Why APIs Are the New Policy Boundary

Traditional applications enforce business logic in the codebase. LLM agents, by contrast, are nondeterministic. They can hallucinate, leak system prompts, or call tools in unexpected ways. Developers need a way to enforce:

• Content filters: Block toxic output or sensitive data.
• Cost controls: Cap token spend per request or per session.
• Tool access: Whitelist which functions an agent may call.
• Model routing: Send simple queries to cheap models, complex ones to GPT-5.6 Sol.
• Audit trails: Log every request for compliance and debugging.

A proxy centralizes these rules. Instead of embedding them in every agent script, you configure them once. That's the core insight behind the open-source proxy projects gaining traction.

"We built an open-source proxy that enforces LLM agent rules at the API layer" — Source

Designing a Rule-Enforcing Proxy

The classic pattern is an OpenAI-compatible reverse proxy. It intercepts incoming API requests, applies rule checks, then forwards them to the upstream provider.

Here's a minimal Node.js snippet using a middleware approach:

import express from 'express';
import { createProxyMiddleware } from 'http-proxy-middleware';

const app = express();

app.use(express.json());

// Rule: block requests with excessive tokens
app.use((req, res, next) => {
  if (req.body?.max_tokens && req.body.max_tokens > 8000) {
    return res.status(400).json({ error: 'max_tokens exceeds allowed threshold' });
  }
  next();
});

// Rule: inject safety system prompt for certain models
app.use((req, res, next) => {
  if (req.body?.model === 'gpt-5.6-sol') {
    req.body.messages.unshift({
      role: 'system',
      content: 'Do not reveal sensitive data. Answer only from supplied context.'
    });
  }
  next();
});

// Forward to the actual LLM provider (or gateway)
app.use('/v1', createProxyMiddleware({
  target: process.env.LLM_BASE_URL,
  changeOrigin: true
}));

app.listen(3000);

This is intentionally simplified, but it illustrates the pattern: the proxy is a gateway with business logic.

Rule Categories That Matter

• Request rewriting: Inject system prompts, trim context.
• Response filtering: Check output for banned phrases or PII.
• Rate limiting: Per-user or per-API-key limits.
• Budget tracking: Deduct from a prepaid balance.

For GPT-5.6 Sol, which costs $2.50 per 1M input tokens and $15 per 1M output tokens, enforcing max_tokens is a practical way to rein in runaway agents.

The Economics of Model Routing

One of the most valuable proxy features is dynamic model routing. Instead of hardcoding a single model, the proxy can decide which model receives a given request—based on cost, latency, or capability.

Consider a typical workload

A proxy can route a sentiment-analysis request to Luna, while sending a complex math proof to Sol. That's what makes an API gateway a cost control center, not just a security appliance.

This is exactly where OneMux shines. OneMux gives you access to leading AI models—including GPT-5.6 Sol and Claude Opus 4.8—through a single OpenAI-compatible API. You don't need to manage separate API keys or contracts. With OneMux, you get routing, spend visibility, and pay-as-you-go pricing. (See OneMux models for the full catalog.)

Going Beyond a Simple Proxy

The open-source proxy we referenced handles rule enforcement, but production-grade implementations need more:

• Authentication: Validate API keys, manage user identities.
• Observability: Stream logs to Loki, dashboards to Grafana.
• Audit compliance: Record immutable request/response pairs.

Here's an example of rule enforcement for sensitive data detection using a regex + allowlist:

import re
import json

SENSITIVE_PATTERN = re.compile(r'\b\d{3}-\d{2}-\d{4}\b')  # SSN

def enforce_response(response, user_id):
    if SENSITIVE_PATTERN.search(response['content']):
        # redact or block
        return {'error': 'response contains PII'}
    return response

Many developers add a pre-request checklist and a post-response checklist, ensuring both input and output conform to policy.

How OneMux Simplifies the Modern LLM Stack

Building a proxy is a solid first step, but you still have to maintain provider integrations, handle failover, and reconcile invoices. That's where a service like OneMux fits. OneMux acts as an aggregator and gateway, offering:

• Unified API: Use the same OpenAI-compatible interface for GPT-5.6 Sol, Claude Opus 4.8, and other models.
• Centralized key management: Rotate keys without touching every service.
• Spend visibility: Track cost per project, per model, per user.
• Pay-as-you-go: Avoid committed-use discounts you don't need.

For teams adopting the proxy pattern, OneMux can be the upstream endpoint that the proxy forwards to. You keep your custom rules, and OneMux handles the model provider chaos. Check the OneMux pricing page for transparent rates, and the docs if you want to dive deep.

If you're ready to try it, the Quickstart guide lets you make your first API call in minutes.

Real-World Adoption: From Reddit to Production

The source literature around this open-source movement often points to community-driven work. The most prominent example is the Reddit thread titled "We built an open-source proxy that enforces LLM agent rules" which describes a project with 700 GitHub stars. While 700 stars is modest by some standards, it signals a real appetite for this infrastructure.

What does that mean for you?

Whether you're a solo founder or a platform team, the proxy pattern is viable. You can start with a weekend prototype and evolve it into a critical piece of your AI stack.

Technical Takeaways

• Start with request/response interception, not full traffic mirroring.
• Use a rules engine (e.g., JSON rules) rather than hardcoding conditions.
• Always log what rule fired and why.
• Consider embedding a "policy ID" in request headers for traceability.

Conclusion: Rules Are the Product

As GPT-5.6 Sol and similar frontier models become the default engine for autonomous agents, the differentiator won't be which model you call—it'll be how safely and efficiently you call it. A rule-enforcing proxy is your control plane. Combined with a multi-model gateway like OneMux, you get a stack that's both powerful and fiscally responsible.

The cost difference between "prompting nicely" and "enforcing via proxy" is staggering. With GPT-5.6 Sol at $15 per 1M output tokens, a single runaway agent could burn through a budget in minutes. Don't let that happen.

Sources

• We built an open-source proxy that enforces LLM agent rules at the API layer (Reddit)