SVEN

Tech professional, practicing Buddhist, a seeker of life's clarity.
AIGateway is a local AI model manager

[SVEN @yusw2013]

@KnoxJulio87411 Exactly. The key is to make the gate operational, not just policy text: define which actions require review, log the reason for each approval/block, and keep enough state to detect risky intent across sessions.

[SVEN @yusw2013]

Before you give an AI agent production access, require 5 control layers.

2026's real problem is no longer “can the model use tools?” It is whether you can verify what the agent is allowed to do, session after session, before one quiet mistake becomes a real incident.

[SVEN @yusw2013]

# How I Use AIGateway as a Local AI Gateway for Multiple AI Coding Tools

If you use more than one AI coding tool or provider, your setup can become messy very quickly.

You may have ChatGPT/Codex configured one way, Claude Code another way, OpenClaw or Harness somewhere else, and then several OpenAI-compatible providers with their own base URLs, API keys, and model names.

AIGateway gives you a cleaner pattern:

AI tools -> local AIGateway endpoint -> cloud providers, OpenAI-compatible APIs, or local Ollama models

Instead of spreading API configuration across every tool, you keep provider and model configuration inside one local desktop gateway.

Official site:
https://t.co/0AylxDRbPp

GitHub:
https://t.co/PxhBvxbS72

## Why a Local AI Gateway Helps

The problem is not just having many models. The real problem is operational drift.

Every tool has its own configuration surface. Every provider has its own base URL and model naming rules. Every time you change a provider, try a cheaper model, or add a backup route, you risk editing several tools by hand.

A local gateway gives you a stable control layer.

Your AI tools keep calling one endpoint. Behind that endpoint, AIGateway can manage providers, models, virtual model groups, token usage, and priority-based failover.

## A Practical Setup

The setup I recommend is simple:

1. Add one primary provider for daily coding.
2. Add one stronger model for review, architecture, and long-context work.
3. Add one cheaper backup provider for routine tasks.
4. Add Ollama if you want local models in the same workflow.
5. Expose stable virtual model names to your tools.

For example:

virtual model: coding-default
- priority 1: primary cloud coding model
- priority 2: cheaper OpenAI-compatible backup model
- priority 3: local Ollama model

Now your tools only need to call `coding-default`.

If you later change the provider order, you do it inside AIGateway instead of editing every AI tool again.

## Connecting Tools

For tools that support OpenAI-compatible endpoints, the pattern is usually:

1. Set the base URL to the local AIGateway endpoint.
2. Set the model name to a model or virtual model registered in AIGateway.
3. Keep provider API keys inside AIGateway.
4. Send a test request.
5. Check token usage and provider routing in AIGateway.

This works well for workflows involving ChatGPT/Codex-style tools, Claude Code, OpenClaw, Harness, and other tools that can call OpenAI-compatible APIs.

## Token Visibility

One of the most useful parts is token visibility.

After a day of real work, you can check which model handled the most requests, which provider consumed the most input or output tokens, and whether expensive models are being used for simple tasks.

This turns model selection from guesswork into something you can tune.

## Failover

Priority-based failover is useful when a provider hits a rate limit, quota limit, outage, or unstable latency.

Keep the chain short:

1. Primary provider
2. Backup provider
3. Local or low-cost fallback

Too many fallback targets make debugging harder. A small, intentional failover chain is easier to reason about.

## Summary

AIGateway is useful when you want a local control layer for a fragmented AI workflow.

The core idea:

- register providers once
- expose stable model or virtual model names
- point AI tools to the local gateway
- track token usage in one place
- use failover when reliability matters

For developers using multiple AI coding tools and OpenAI-compatible providers, this is a practical way to keep the stack manageable.

#AI #LLM #DeveloperTools #ClaudeCode #OpenAI

[SVEN @yusw2013]

@a5577517 hello

[SVEN @yusw2013]

@_som_too hi

[SVEN @yusw2013]

@kim_buduru okyy

[SVEN @yusw2013]

Most AI developers now use more than one model, more than one provider, and more than one coding tool.

That is powerful, but it also creates a practical problem:

API keys are scattered.
Model settings are duplicated.
Token usage is hard to track.
Failover is manual.
Every tool needs its own configuration.

AIGateway is built to solve this as a local-first AI gateway for developers.

It gives you one local entry point for tools such as ChatGPT / Codex, Claude Code, OpenClaw, Harness, and other OpenAI-compatible clients.

Behind that local gateway, you can manage multiple providers and models, including OpenAI-compatible APIs, DeepSeek, Qwen, Moonshot, SiliconFlow, and Ollama.

Why use a local AI gateway?

1. Unified provider management

Add providers with Base URL, API key, model list, proxy mode, and payload format in one place.

Instead of editing every AI tool separately, you point your tools to the local gateway.

2. Model and virtual model routing

AIGateway lets you organize models and create virtual model groups.

This makes it easier to switch between cloud models, local Ollama models, and lower-cost OpenAI-compatible providers without rebuilding your workflow.

3. Token usage visibility

You can see requests, input tokens, output tokens, provider usage, and model usage from the gateway layer.

For developers and small teams, this makes AI cost tracking much more concrete.

4. Priority-based failover

When a provider is rate-limited, overloaded, or out of quota, AIGateway can route to a backup model based on your configured priority.

This keeps AI coding and automation workflows from stopping because one endpoint failed.

5. Local-first control

AIGateway runs locally.

Your configuration and API keys are managed on your own machine. Requests only go to the external providers you configure.

AIGateway is useful if you:

- use Claude Code with multiple models
- connect ChatGPT / Codex-style tools to OpenAI-compatible APIs
- switch between DeepSeek, Qwen, OpenAI-compatible providers, and Ollama
- want token usage by provider and model
- need a simple local failover layer
- want one stable endpoint for your AI development stack

Getting started:

1. Install AIGateway from GitHub Releases
2. Add your AI providers
3. Configure models or virtual model groups
4. Point your AI tools to the local gateway endpoint
5. Monitor token usage and adjust routing as needed

AIGateway is not trying to be a heavy enterprise routing platform.

It is a practical desktop gateway for developers who want more control over their AI tools, models, API keys, cost, and failover behavior.

Official site:
https://t.co/0AylxDRbPp

GitHub:
https://t.co/PxhBvxbS72

Keywords:
local AI gateway, OpenAI-compatible API, Claude Code, ChatGPT Codex, AI model routing, token usage tracking, Ollama gateway, LLM failover, developer AI workflow

[SVEN @yusw2013]

AIGateway — Your AI Models, Your Rules

I built something to solve my own problem. Now I'm putting it out there.

The Problem

Every month I stare at my AI API bill and ask the same question: where did the money go?

I use Claude for complex coding, ChatGPT for research, DeepSeek for quick lookups, Grok
for creative work. Multiply that by dozens of prompts a day, and the math gets ugly fast.

The worst part? I'm paying Claude prices for "what's the weather" queries. Token-based
pricing doesn't care if your task is mission-critical or trivial. It charges the same.

So I looked around. There are tools that route requests between models. But they're built
for enterprises with 100+ model integrations and price tags to match. I needed something
simple — a thin proxy that handles the 5-6 models I actually use, running on my own
machine.

What I Built

AIGateway is a Go binary. Drop it on any server, point your apps at it, and it handles
the rest:

- Smart routing — high-stakes tasks go to Claude/GPT. Everything else hits cheaper
models.
- Single endpoint — your tools talk to one API. AIGateway decides which model gets the
request.
- Cost dashboard — stop guessing where your money goes. Every call, every model, every
cost is visible in real time.

Why Go

Not because it's trendy. Because when you're building a proxy layer, you need something
that:

- Compiles to a single binary (no pip install nightmare)
- Handles thousands of concurrent requests without breaking a sweat
- Runs on a $5 VPS with RAM to spare

Go's concurrency model is made for this.

Where It Stands

This project is early. One GitHub star. Zero marketing budget.

But here's what matters: I use it every day. Every feature I add comes from my own pain.
Every optimization comes from watching my own token burn.

I believe every developer should control their AI infrastructure. No vendor lock-in, no
surprise bills, no black-box routing.

The Tech

Built entirely in Go. Single binary deployment. REST API. Works with any
OpenAI-compatible endpoint — which means Claude, GPT, DeepSeek, Grok, and anything else
that speaks the same protocol.

Licensed under MIT. Fork it, modify it, ship it in your own products. No strings
attached.

Next

I'm adding:
- Weighted cost-based routing (prioritize cheapest model within quality threshold)
- Model failover (if Claude is down, auto-switch to backup)
- Usage alerts (get notified before you blow through your budget)

Try It

https://t.co/tY7fyRHQI5 (https://t.co/PxhBvxbS72)

If you pay your own AI bills, this might save you real money. Try it. Break it. Open an
issue. This project gets better when more people tell me what sucks about it.

[SVEN @yusw2013]

I built something that AI companies probably don't want you to have.

AIGateway — a Go proxy that routes your requests to the cheapest model automatically.

No vendor lock-in. No overpaying. 👇

[SVEN @yusw2013]

今日も幸せな一日です

[SVEN @yusw2013]

@369Serena 人满了

[SVEN @yusw2013]

Why it matters: OpenMontage packages 12 pipelines, 52 tools, 500+ agent skills, 14 video providers, plus QA gates, budget controls, and multi-provider routing. My takeaway: vertical AI winners will own workflows end to end, not just asset generation.

[SVEN @yusw2013]

If you still think AI video is about prompting a model for a 4-second clip, you are watching the wrong layer. The real shift is from video models to agentic production systems. OpenMontage is one of the clearest signals I have seen this week.

[SVEN @yusw2013]

Enterprise AI's next moat may be verification, not raw model quality.

The winners will not be the teams that give agents the most tools. They will be the teams that can prove, constrain, review, and replay agent actions without killing deployment speed.

[SVEN @yusw2013]

My rule of thumb:

If an AI agent can touch code, money, permissions, customer communication, or production systems, tool access + logs is not enough. You need a gating layer between authorization and execution.

Otherwise you are not deploying automation. You are deploying hope.