Building an Agent-Aware LLM Gateway from First Principles in Node.js

🔗 GitHub Profile: github.com/sawastik7-bit

🚀 What I’m Building Right Now

Instead of relying on out-of-the-box abstractions, I’m building AgentGate—a production-grade, agent-aware LLM Gateway from scratch in Node.js.

Most AI implementations fall short when scaling to production because of unhandled API rate limits, provider outages, high token latency, and security leaks. My goal is to handle these infrastructure challenges right at the gateway level.

🛠️ Core Features & Architecture

1. Semantic Caching: Utilizing vector embeddings (Xenova/all-MiniLM-L6-v2) and Cosine Similarity (>0.8 threshold) to bypass LLM calls entirely for similar incoming prompts—reducing latency and API costs.
2. Multi-Model Dynamic Routing & Fallbacks: Automatically tracking model usage limits and gracefully switching providers (OpenRouter, Nvidia Nemotron, OpenAI) when rate limits or quotas are hit.
3. Guardrails & Security: Dual-pass input and output validation layers evaluating prompts for malicious injections and data leaks before hitting downstream agents.
4. Sliding-Window Rate Limiting: Global request tracking to enforce traffic constraints and prevent gateway overload.
5. Interactive Terminal CLI Loop: Built using node:readline/promises for real-time testing, session token verification, and output tracking.

💡 My Approach: First Principles First

Instead of stitching together high-level black-box tools, I approach engineering from first principles:

• Build the core low-level mechanics first: (Handling cosine similarity math, manual sliding windows, and custom fallback maps).
• Measure & Debug: Validating behavior through raw CLI output logs, tracking cache hits vs. misses, and timing provider switches.
• Production Hardening: Incrementally layering resilience patterns—exponential backoffs with jitter, circuit breakers, and stateful tracking.

📌 Other Systems & Infra Projects I'm Working On

If you enjoy backend infrastructure and systems engineering, check out my other open-source projects on GitHub:

• Redis from Scratch: Implementing the RESP protocol and TCP sockets in Node.js.
• Custom HTTP Load Balancer: Round-robin routing, health checks, and automatic backend failovers.
• Distributed API Gateway Analytics: Real-time metrics dashboard with Redis, JWT, and BullMQ logging.

💬 I’d love to hear your thoughts on LLM gateway design patterns! How are you handling model fallbacks and semantic caching in your production AI agents? Drop a comment below!