AuthGate AI Proxy_

The Problem

Most AI tooling still assumes the client can talk directly to every provider. In practice, the surface area is fragmented. Providers differ on request shapes, auth models, streaming semantics, error contracts, and even the base URLs clients are expected to target.

That becomes painful quickly if you want one local endpoint that works across editors, scripts, internal tools, and agent workflows.

I built AuthGate as a local gateway to remove that friction.

What I Built

AuthGate exposes one consistent endpoint on localhost:4000 and handles the ugly parts behind the scenes:

• translating provider-specific chat payloads into one local interface
• preserving streaming behavior through SSE
• refreshing tokens without breaking long-running sessions
• normalizing error responses into something clients can actually handle

The interesting work was not the web server. It was the compatibility layer. A proxy like this succeeds only if clients can forget which provider sits upstream.

Why It Matters

This project is really about local developer experience and protocol discipline. If a tool has to be reconfigured for every provider, or if streaming behaves differently every time, adoption falls apart. The value of AuthGate is that local tools can treat multiple providers as one stable boundary.

That makes it useful for:

• editor integrations
• local agent runtimes
• internal evaluation scripts
• teams experimenting across multiple model providers

For me, it is a strong case study in abstraction done carefully: simplify the interface, but do not hide the operational realities underneath.