LLM Proxy Server for AI Agents is Here
Building AI agents that juggle multiple API calls and manage different models can get messy fast. You end up writing boilerplate for error handling, retries, and routing—distracting you from the actual agent logic. What if you could offload that infrastructure work to a dedicated component?
Enter archgw, an LLM proxy server designed specifically for agentic workflows. It’s a lightweight gateway that sits between your agents and various LLM providers, giving you a unified interface while handling the complex routing and reliability concerns behind the scenes.
What It Does
Archgw is essentially a smart proxy server that acts as a single entry point for your AI agents to communicate with multiple LLM providers. Instead of your agents making direct API calls to OpenAI, Anthropic, or other services, they talk to archgw, which then handles the complexity of routing requests, managing retries, and dealing with rate limits.
Think of it as a load balancer and circuit breaker specifically designed for LLM API consumption. Your agents get a consistent interface while archgw worries about the infrastructure reliability.
Why It's Cool
The clever part is how archgw abstracts away the pain points of working with multiple LLM APIs. When you're building agents that need to maintain context across multiple exchanges or switch between models based on cost/performance needs, manually managing all those API connections becomes tedious.
Archgw handles automatic retries with exponential backoff when APIs fail, implements circuit breakers to prevent cascading failures, and provides a unified response format regardless of which underlying provider you're using. This means your agent code stays clean and focused on business logic rather than infrastructure concerns.
For teams running AI agents in production, this kind of reliability layer is crucial. Instead of baking retry logic and error handling into every agent, you get it out-of-the-box with a simple proxy configuration.
How to Try It
Getting started is straightforward. The project is on GitHub, and you can run it locally with Docker:
git clone https://github.com/katanemo/archgw
cd archgw
docker-compose up
Once running, you can point your agents to http://localhost:8080 and configure your routing rules through the API. The repository includes example configurations for common setups, so you can be up and running in minutes rather than hours.
Final Thoughts
As AI agents become more sophisticated and handle more complex workflows, tools like archgw that provide foundational infrastructure will become increasingly valuable. It's one of those "why didn't I think of that" solutions that solves a real pain point for developers building agentic systems.
If you're working on AI agents that make multiple LLM calls or use different providers, this is definitely worth checking out. It might just save you from writing yet another retry loop.
— @githubprojects