Fala, pessoal. Compartilhando um projeto open-source que uso muito a partir do Python (disclosure: sou o mantenedor; é grátis/MIT). Como ele expõe um endpoint compatível com OpenAI, dá pra usar direto do openai do Python só trocando o base_url:

from openai import OpenAI
client = OpenAI(base_url="http://localhost:20128/v1", api_key="...")

E aí seu código Python herda:

Combos de fallback — pra nunca parar no meio da tarefa. Um "combo" é uma escada de modelos que o roteador percorre sozinho: primeiro sua assinatura, depois chaves de API, depois modelos baratos, depois os grátis. Quando um provedor devolve 500 ou você bate no rate limit, ele desliza para o próximo alvo em milissegundos, no meio da requisição, e sua ferramenta nem vê o erro. São 17 estratégias de roteamento mais três camadas de resiliência — circuit breaker por provedor, cooldown por chave e lockout por modelo — então uma chave morta não derruba o provedor inteiro.

Um endpoint, 237 provedores — 90+ deles grátis. Você aponta qualquer ferramenta ou agente para um único endpoint compatível com OpenAI (localhost:20128/v1) e ele alcança 237 provedores de LLM sem reescrever nada. 90+ têm free tier e 11 são grátis pra sempre (sem cartão), somando ~1,6B de tokens grátis/mês documentados — e é uma conta honesta, deduplicada por pool (contamos cada pool compartilhado uma vez, sem inflar; a metodologia está no repositório). Tem setup-* de um comando para 13+ ferramentas (Claude Code, Codex, Cursor, Cline, Roo, Kilo, Gemini CLI…).

Um pipeline de compressão de 10 engines — a parte que a maioria dos roteadores não tem. Toda requisição passa por uma etapa transparente de compressão que você liga/empilha por combo. Em vez de um truque só, ele junta o melhor do ecossistema open-source: o RTK filtra saída de comando/ferramenta (git diff, logs de teste, builds) em 60–90%, o LLMLingua-2 (Microsoft) faz poda semântica por ML, o Caveman cuida de prosa, e a deduplicação remove repetições entre turnos. O crucial: código, URLs e JSON são preservados byte-a-byte, e um guarda de inflação (ligado por padrão) descarta a versão comprimida e envia o original se comprimir fosse aumentar o prompt — nunca piora. Em sessões cheias de ferramenta isso dá ~89% de redução média de tokens de entrada. Todo o crédito às fontes (RTK, Caveman, LLMLingua-2, Troglodita) está no README.

Pra você avaliar se vale o tempo: o projeto passou de ~9,8 mil estrelas no GitHub, 1.490+ forks e 280+ contribuidores em ~4,5 meses, com 21.000+ testes automatizados e 1.830+ issues fechadas — ou seja, é maduro e validado, não um experimento de fim de semana.

npm install -g omniroute

GitHub: https://github.com/diegosouzapw/OmniRoute

Alguém aqui já usa um gateway assim nos projetos Python? Curioso pra saber como vocês tratam fallback.

Substance over hype (per the rules): as more of the web is driven by AI agents, the boring infra problems bite — agents dying on a provider rate limit, and cost from tool/page content flooding the context. Sharing how I handle both (disclosure: I maintain the open-source tool; link in a comment).

Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider.

A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README.

One endpoint, 237 providers — 90+ of them free. You point any tool or agent at a single OpenAI-compatible endpoint (localhost:20128/v1) and it can reach 237 LLM providers without you rewriting anything. 90+ have free tiers and 11 are free forever (no card), which aggregates to ~1.6B documented free tokens/month — and that's honest, pool-deduped math (we count each shared pool once instead of inflating it; the methodology is public in the repo). There's a one-command setup-* for 13+ coding tools (Claude Code, Codex, Cursor, Cline, Roo, Kilo, Gemini CLI…), so switching your existing setup over takes seconds.

The 'cheaper-model ladder' idea (keep easy turns on cheap/free models, escalate only when needed) maps directly onto the combo strategies.

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

For people building web agents: where does the cost/reliability hurt most — the model calls, or the tool/browsing layer? Tool link in the first comment.

On-topic for RAG (disclosure: I maintain the open-source tool; per limited-self-promo the link's in a comment). A recurring RAG cost/latency problem is stuffing retrieved chunks + tool output into the window. I built a gateway with a compression pass aimed at exactly that.

A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README.

For RAG specifically: it trims retrieved context and tool output before the model while hard-preserving code, URLs and JSON, and an adaptive dial only compresses as far as needed to fit the window. There's an offline eval harness to score fidelity-vs-savings before you enable a setting.

It also aggregates 237 providers with automatic fallback, so long indexing/query jobs don't die when one provider rate-limits, and opt-in memory (FTS5 + Qdrant/sqlite-vec) if you want persistent recall.

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

How do you all keep RAG context cost down — rerank/trim before the model, or rely on bigger windows? Sources for the compression engines (RTK, LLMLingua-2, Caveman) and the repo are in the first comment.

Submitting a free, open-source tool that's been genuinely useful (disclosure: I'm the maintainer). OmniRoute lets you use 237 AI providers from one place — 90+ have free tiers — and it auto-switches when one hits a limit so you don't get stuck.

• Many AI models, one place; 90+ free (no card for a lot of them).
• Auto-fallback so it doesn't stop mid-task.
• Runs on your own computer (free, MIT, no tracking); has a desktop app and a dashboard.

For peace of mind: it's one of the more popular open-source AI projects on GitHub (~9.8K stars, 280+ contributors) — so it's well-tested and actively maintained, not a random weekend project.

Site: https://omniroute.online · GitHub: https://github.com/diegosouzapw/OmniRoute

Useful if you use AI a lot and hate juggling accounts/limits.

A concrete use case (per 'show real value', no bare self-promo — disclosure: I maintain the tool, link in a comment). Running OpenClaw agents on a VPS 24/7, two things bit me: hitting provider limits (agents stall) and the token bill from long tool output. Fronting OpenClaw with a self-hosted gateway fixed both.

OmniRoute exposes both an OpenAI-compatible endpoint (/v1) and an Anthropic-compatible one (/v1/messages), so you can point the tool at whichever protocol it speaks.

Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider.

A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README.

Security note (per the sub's emphasis): it's self-hosted with keys encrypted at rest (AES-256-GCM), and process-spawning routes are loopback-only by design — worth checking if you expose anything on a VPS.

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

For VPS agent runners: how do you keep cost + uptime under control? Tool link in the first comment.

Since you're already self-hosting n8n, here's a self-hosted gateway that pairs with it (disclosure: I'm the maintainer, free/MIT). Set your n8n OpenAI-compatible node's base URL to it (http://localhost:20128/v1) and the workflow inherits:

OmniRoute exposes both an OpenAI-compatible endpoint (/v1) and an Anthropic-compatible one (/v1/messages), so you can point the tool at whichever protocol it speaks.

Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider.

One endpoint, 237 providers — 90+ of them free. You point any tool or agent at a single OpenAI-compatible endpoint (localhost:20128/v1) and it can reach 237 LLM providers without you rewriting anything. 90+ have free tiers and 11 are free forever (no card), which aggregates to ~1.6B documented free tokens/month — and that's honest, pool-deduped math (we count each shared pool once instead of inflating it; the methodology is public in the repo). There's a one-command setup-* for 13+ coding tools (Claude Code, Codex, Cursor, Cline, Roo, Kilo, Gemini CLI…), so switching your existing setup over takes seconds.

A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README.

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

npm install -g omniroute

GitHub: https://github.com/diegosouzapw/OmniRoute

Anyone here already routing n8n's AI nodes through a gateway? Curious what breaks in long automations.

Since this sub is partly about Opus cost and workflows, sharing something on-topic (disclosure: I'm the maintainer of the open-source tool; keeping the link in a comment and giving context first, per the self-promo rule). Opus is great but expensive and rate-limited, so I built a gateway that attacks both.

A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README.

Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider.

Net effect for Opus specifically: the compression pass means a tool-heavy Opus session sends far fewer input tokens (an 8k-token git diff becomes a few hundred), and the fallback ladder means when you hit the Opus cap you keep going on a backup instead of stopping — you keep Opus for the hard reasoning and let cheaper models take the easy turns.

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

For heavy Opus users: what's your split between "must be Opus" and "any model would do"? Repo + install in the first comment.

If you build with Claude Code, you know the main wall isn't ideas — it's hitting your usage limit mid-build and losing momentum. Sharing a free, open-source tool that fixes that (disclosure: I'm the maintainer; per the no-self-promo rule the link is in the first comment, this post is the how).

The trick is a self-hosted gateway that Claude Code points to instead of the API directly. It drains your Claude subscription first, and only when you'd otherwise be blocked does it slide to a backup — so the build keeps going:

Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider.

A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README.

Setup is one command (omniroute setup-claude wires Claude Code to it), and you keep using Claude Code exactly the same — it just doesn't stop when the quota does.

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

For people building here without a heavy coding background: is the usage limit your #1 blocker, or is it something else? Repo + setup in the first comment.

Since this sub is about testing/performance of AI tools, sharing real numbers from an open-source gateway I maintain (disclosure noted — it's at ~9.8K GitHub stars / 21k+ tests, so the numbers aren't from a toy; link in a comment, keeping the post about the data).

Token reduction (input side). A compression pass in front of the model trims command/tool output (git, tests, builds) 60–90% via RTK-style filtering, with ML pruning (LLMLingua-2) on prose. On tool-heavy sessions the average is ~89% input-token reduction, with code/URLs/JSON preserved byte-perfect and a guard that reverts to the original if compression would grow the prompt.

Failover latency. Provider fallback (subscription → API key → cheap → free) triggers in milliseconds on a 5xx/quota error, so throughput doesn't collapse when one provider degrades.

What I'm unsure about is measuring quality impact: token savings are easy to quote, but "did compression change the answer?" is harder. I use an offline eval harness (fidelity vs. savings) but it's still heuristic.

How do you all benchmark this kind of thing — a go-to methodology for "same task, N providers/settings, compare output quality + latency + cost"? Tool link in a comment for anyone who wants to reproduce.

Share-first, per the sub's spirit: the productivity killer in my AI workflows wasn't the models — it was interruptions. A provider rate-limits or goes down and the whole automation stalls. Here's the setup that fixed it (disclosure: I built the open-source tool behind it; link in a comment).

Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider.

One endpoint, 237 providers — 90+ of them free. You point any tool or agent at a single OpenAI-compatible endpoint (localhost:20128/v1) and it can reach 237 LLM providers without you rewriting anything. 90+ have free tiers and 11 are free forever (no card), which aggregates to ~1.6B documented free tokens/month — and that's honest, pool-deduped math (we count each shared pool once instead of inflating it; the methodology is public in the repo). There's a one-command setup-* for 13+ coding tools (Claude Code, Codex, Cursor, Cline, Roo, Kilo, Gemini CLI…), so switching your existing setup over takes seconds.

A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README.

It's 100% local (zero telemetry, AES-256-GCM at rest), MIT-licensed, has a prompt-injection guard on every LLM route, opt-in memory, and runs on npm, Docker, desktop or your phone via Termux.

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

What's the most annoying interruption in your AI workflow right now — limits, cost, or reliability? Tool link in a comment.

A discussion prompt more than a pitch: after getting burned by single-provider rate limits and pricing swings, I stopped betting on one AI vendor and started routing across many. I ended up building an open-source gateway to do it (disclosure: I maintain it, ~9.8K GitHub stars; link in a comment, keeping this post about the idea).

The setup routes across 237 providers behind one endpoint, with automatic fallback (if one rate-limits or goes down, it slides to the next mid-request) and a compression layer that trims tool/log output before it hits the model. In practice it turned "provider X is down, my day is ruined" into a non-event.

What I'm genuinely curious about here:

• Is multi-provider routing the pragmatic future for anyone serious about uptime/cost, or is it over-engineering vs. just paying for one good provider?
• Does provider diversity actually reduce lock-in, or just move the complexity around?
• For those using AI daily — how much does rate-limit/quota anxiety actually shape which tools you pick?

Not trying to sell anything (it's free/MIT/self-hosted). More interested in whether the "don't depend on one model" thesis holds up.

Value-add for no-code builders (not a launch post — disclosure: I'm the maintainer, it's free/MIT; link in a comment). If your no-code tools call AI and you hate juggling API keys or hitting limits, this helps: a gateway that puts many providers behind one endpoint, with a desktop app so you don't have to live in a terminal.

Many AI models from one place. Instead of signing up for one AI and juggling accounts, it connects 237 providers behind a single spot — and 90+ of them have free tiers (11 are free forever, no card needed). So you can try lots of models without paying.

It never gets stuck on a limit. If the model you're using hits its usage cap or goes down, it automatically switches to another one instantly — mid-task — so you don't lose your work or your flow.

It runs on your own computer. Nothing is sent to any OmniRoute server — it's free, open-source (MIT), with no tracking. You only ever pay the providers you choose, and many are free.

Any no-code tool that accepts an OpenAI-compatible endpoint/base URL can point at it.

For peace of mind: it's one of the more popular open-source AI projects on GitHub (~9.8K stars, 280+ contributors) — so it's well-tested and actively maintained, not a random weekend project.

For no-coders: does your platform let you set a custom AI endpoint? That's all it takes. Link in the first comment.

Context first (per the self-promo rule): I build with Gemini a lot and kept hitting rate limits / 'unusual activity' walls, so I built a free, MIT, self-hosted gateway. Disclosure: I'm the maintainer; link's in the first comment. Gemini becomes one target in a resilient ladder:

Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider.

One endpoint, 237 providers — 90+ of them free. You point any tool or agent at a single OpenAI-compatible endpoint (localhost:20128/v1) and it can reach 237 LLM providers without you rewriting anything. 90+ have free tiers and 11 are free forever (no card), which aggregates to ~1.6B documented free tokens/month — and that's honest, pool-deduped math (we count each shared pool once instead of inflating it; the methodology is public in the repo). There's a one-command setup-* for 13+ coding tools (Claude Code, Codex, Cursor, Cline, Roo, Kilo, Gemini CLI…), so switching your existing setup over takes seconds.

A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README.

Keep Gemini primary, use its free tier (plus 90+ others), and fall back automatically when it rate-limits — without juggling keys.

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

How do you deal with Gemini limits today? Repo + install in the first comment.

Sharing an open-source tool for the automation crowd (disclosure: I'm the maintainer; no affiliate/referral anything, and I'll keep the link in a comment per the self-promo rule). The problem it targets: AI automations die when one provider rate-limits or 500s mid-run.

Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider.

One endpoint, 237 providers — 90+ of them free. You point any tool or agent at a single OpenAI-compatible endpoint (localhost:20128/v1) and it can reach 237 LLM providers without you rewriting anything. 90+ have free tiers and 11 are free forever (no card), which aggregates to ~1.6B documented free tokens/month — and that's honest, pool-deduped math (we count each shared pool once instead of inflating it; the methodology is public in the repo). There's a one-command setup-* for 13+ coding tools (Claude Code, Codex, Cursor, Cline, Roo, Kilo, Gemini CLI…), so switching your existing setup over takes seconds.

A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README.

It exposes one OpenAI-compatible endpoint, so it drops into n8n, cron jobs, scripts, or any coding assistant.

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

What's the most fragile external dependency in your automation stack right now? Repo + install in a comment.

Posting this as an LLM tool that's genuinely useful for prompt work (disclosure up front: I'm the maintainer; it's free/MIT, and I'm keeping the link in a comment so the value comes first). If you iterate on prompts, comparing the same prompt across many models is painful — different keys, dashboards, rate limits. This fixes that.

One endpoint, 237 models — 90+ free. Point any tool at a single OpenAI-compatible endpoint and switch models by name, so you can run one prompt across GPT-, Claude-, Gemini-, DeepSeek-class models and compare, without juggling accounts. 90+ have free tiers, so a lot of prompt testing costs nothing.

Output Styles — steer the shape of the output, not just the content. Alongside the prompt, you can apply named styles (e.g. terse-prose, less-code/YAGNI, terse-CJK) at the gateway, which is handy when you're A/B-ing how a model formats answers.

It won't die mid-test. Automatic fallback across providers means a rate limit doesn't interrupt a batch of prompt evals, and an optional compression pass keeps long few-shot prompts cheap (code/URLs/JSON preserved byte-perfect).

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

For prompt engineers here: how do you currently compare a prompt across models — manually, or with tooling? Repo + install in the first comment.

Sharing an open-source project I've put ~4.5 months into (disclosure: I'm the maintainer; per the self-advertisement rule I'm keeping the link in the first comment and making this post substantive). It started from two problems I hit daily: AI runs dying on a provider rate limit, and burning thousands of tokens dumping tool/log output into the context window.

One endpoint, 237 providers — 90+ of them free. You point any tool or agent at a single OpenAI-compatible endpoint (localhost:20128/v1) and it can reach 237 LLM providers without you rewriting anything. 90+ have free tiers and 11 are free forever (no card), which aggregates to ~1.6B documented free tokens/month — and that's honest, pool-deduped math (we count each shared pool once instead of inflating it; the methodology is public in the repo). There's a one-command setup-* for 13+ coding tools (Claude Code, Codex, Cursor, Cline, Roo, Kilo, Gemini CLI…), so switching your existing setup over takes seconds.

Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider.

A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README.

Agent-native — the agent can drive the router itself. There's a built-in MCP server (95 tools across 30 audited scopes, over stdio / SSE / streamable-HTTP), plus A2A (v0.3, JSON-RPC 2.0) support. That means an agent can query providers, switch combos, read its own remaining quota and manage memory through the gateway — not just consume tokens through it.

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

Happy to go deep on the routing engine, the honest free-tier math, or how the compression pipeline decides what's safe to compress. Repo + install in the first comment.

We've all been there. You're deep in a coding flow — Claude is generating, you're waiting 30 seconds for Gemini to think — and you realize you need coffee. Or lunch. Or the doorbell rings.

Your options? Walk back to your desk every 3 minutes to check if the AI finished. Or just... close the laptop and lose the session.

**I got tired of that.** So I built something.

---

## OmniAntigravity Remote Chat — Your AI session, on your phone

It's a Node.js server that connects to your Antigravity via CDP (Chrome DevTools Protocol) and mirrors the entire chat to your phone browser. Not a screenshot. Not a notification. The **actual live chat** — with full interaction.

**One command to start:**

npx omni-antigravity-remote-chat

Open the URL on your phone. That's it. You're in.

---

## What you can actually do from your phone

**The basics (what you'd expect):**
- 📱 Read AI responses in real-time as they stream
- ✍️ Send follow-up messages and prompts
- 🤖 Switch between Gemini, Claude, and GPT from a dropdown
- 🪟 Manage multiple Antigravity windows from one phone
- 📋 Browse and resume past conversations

**The stuff that actually saves your day:**
- ✅ **Approve/reject CLI actions** — AI wants to run `rm -rf`? Approve or reject from the couch. No more walking back to your desk for every pending action.
- 📊 **Quota monitoring** — see exactly how much of each model you've used. Get warned BEFORE you hit the limit, not after your session dies silently.
- 🧠 **AI Supervisor** — an optional OmniRoute-backed layer that evaluates commands for safety before they execute. Heuristic gate catches dangerous patterns, AI evaluation handles the rest.
- 💬 **Suggest Mode** — suggestions get queued instead of auto-executing. Review them on your phone, approve or reject, one at a time.
- 📱 **Telegram push notifications** — get alerted on your phone when: agent blocks, task completes, action needs approval, quota is running low. Interactive bot with commands like `/status`, `/quota`, `/stats`.

**The workspace (yes, from your phone):**
- 📁 **File browser** — navigate your project, preview files with syntax highlighting
- 💻 **Terminal** — run commands remotely with live output streaming
- 🔀 **Git panel** — status, stage, commit, push — all from mobile
- 💬 **Assist chat** — talk to the AI supervisor about what's happening in your session
- 📈 **Stats panel** — messages sent, actions approved, errors detected, quota warnings
- 🖼️ **Screenshot timeline** — automatic visual history of your IDE states
- 🔴 **Live screencast** — stream your actual IDE screen to your phone via CDP

---

## How it works (for the technical crowd)

- Scans CDP ports **7800-7803** for Antigravity workbench targets
- Captures DOM snapshots via `Runtime.evaluate`, hashes for change detection (djb2), broadcasts via WebSocket
- Phone actions → CDP commands → execute on your desktop. Zero Antigravity modifications.
- **18 ESM modules**, **60+ REST endpoints**, **9 Vitest test suites** with V8 coverage
- Strict **Content Security Policy** — `script-src 'self'`, zero inline JS, enforced via HTTP header + meta tags
- **Multi-tunnel**: Cloudflare Quick Tunnels, Pinggy (SSH-based, zero binary deps), ngrok — with automatic fallback
- **5 mobile themes**: dark, light, slate, pastel, rainbow
- Cookie auth + LAN auto-auth + HTTPS with self-signed or mkcert certificates
- Docker: `node:22-alpine`, ~67MB, health check included

---

## Install

**npm (recommended):**

npx omni-antigravity-remote-chat

**Docker:**

docker run -d --network host \
-e APP_PASSWORD=your_password \
diegosouzapw/omni-antigravity-remote-chat

**Git clone:**

git clone https://github.com/diegosouzapw/OmniAntigravityRemoteChat.git
cd OmniAntigravityRemoteChat
npm install && npm start

---

## Links

- **GitHub**: https://github.com/diegosouzapw/OmniAntigravityRemoteChat
- **npm**: https://www.npmjs.com/package/omni-antigravity-remote-chat
- **Docker Hub**: https://hub.docker.com/r/diegosouzapw/omni-antigravity-remote-chat

---

Open source (GPL-3.0). v1.3.0 with strict CSP, multi-tunnel support, and Pinggy SSH tunneling.

I use this every day. The "approve from the couch" flow alone changed how I work with AG. Would love feedback from this community — especially around CDP quirks you've encountered and features you'd want in a mobile companion.

**Your AI session doesn't have to end when you leave your desk.**

---

*P.S. — Tired of juggling API keys, hitting quota walls, and paying for LLM access? I also built **OmniRoute** — a free AI gateway that aggregates 100+ providers behind one endpoint. Smart routing, automatic fallback, and practically unlimited free-tier LLM usage. One API key to rule them all: https://github.com/diegosouzapw/OmniRoute*

OmniRoute is a free, open-source local AI gateway. You install it once, connect all your AI accounts (free and paid), and it creates a single OpenAI-compatible endpoint at localhost:20128/v1. Every AI tool you use — Cursor, Claude Code, Codex, OpenClaw, Cline, Kilo Code — connects there. OmniRoute decides which provider, which account, which model gets each request based on rules you define in "combos." When one account hits its limit, it instantly falls to the next. When a provider goes down, circuit breakers kick in <1s. You never stop. You never overpay.

11 providers at $0. 60+ total. 13 routing strategies. 25 MCP tools. Desktop app. And it's GPL-3.0.

The problem: every developer using AI tools hits the same walls

1. Quota walls. You pay $20/mo for Claude Pro but the 5-hour window runs out mid-refactor. Codex Plus resets weekly. Gemini CLI has a 180K monthly cap. You're always bumping into some ceiling.
2. Provider silos. Claude Code only talks to Anthropic. Codex only talks to OpenAI. Cursor needs manual reconfiguration when you want a different backend. Each tool lives in its own world with no way to cross-pollinate.
3. Wasted money. You pay for subscriptions you don't fully use every month. And when the quota DOES run out, there's no automatic fallback — you manually switch providers, reconfigure environment variables, lose your session context. Time and money, wasted.
4. Multiple accounts, zero coordination. Maybe you have a personal Kiro account and a work one. Or your team of 3 each has their own Claude Pro. Those accounts sit isolated. Each person's unused quota is wasted while someone else is blocked.
5. Region blocks. Some providers block certain countries. You get unsupported_country_region_territory errors during OAuth. Dead end.
6. Format chaos. OpenAI uses one API format. Anthropic uses another. Gemini yet another. Codex uses the Responses API. If you want to swap between them, you need to deal with incompatible payloads.

OmniRoute solves all of this. One tool. One endpoint. Every provider. Every account. Automatic.

The $0/month stack — 11 providers, zero cost, never stops

This is OmniRoute's flagship setup. You connect these FREE providers, create one combo, and code forever without spending a cent.

#	Provider	Prefix	Models	Cost	Auth	Multi-Account
1	Kiro	kr/	claude-sonnet-4.5, claude-haiku-4.5, claude-opus-4.6	$0 UNLIMITED	AWS Builder ID OAuth	✅ up to 10
2	Qoder AI	if/	kimi-k2-thinking, qwen3-coder-plus, deepseek-r1, minimax-m2.1, kimi-k2	$0 UNLIMITED	Google OAuth / PAT	✅ up to 10
3	LongCat	lc/	LongCat-Flash-Lite	$0 (50M tokens/day 🔥)	API Key	—
4	Pollinations	pol/	GPT-5, Claude, DeepSeek, Llama 4, Gemini, Mistral	$0 (no key needed!)	None	—
5	Qwen	qw/	qwen3-coder-plus, qwen3-coder-flash, qwen3-coder-next, vision-model	$0 UNLIMITED	Device Code	✅ up to 10
6	Gemini CLI	gc/	gemini-3-flash, gemini-2.5-pro	$0 (180K/month)	Google OAuth	✅ up to 10
7	Cloudflare AI	cf/	Llama 70B, Gemma 3, Whisper, 50+ models	$0 (10K Neurons/day)	API Token	—
8	Scaleway	scw/	Qwen3 235B(!), Llama 70B, Mistral, DeepSeek	$0 (1M tokens)	API Key	—
9	Groq	groq/	Llama, Gemma, Whisper	$0 (14.4K req/day)	API Key	—
10	NVIDIA NIM	nvidia/	70+ open models	$0 (40 RPM forever)	API Key	—
11	Cerebras	cerebras/	Llama, Qwen, DeepSeek	$0 (1M tokens/day)	API Key	—

Count that. Claude Sonnet/Haiku/Opus for free via Kiro. DeepSeek R1 for free via Qoder. GPT-5 for free via Pollinations. 50M tokens/day via LongCat. Qwen3 235B via Scaleway. 70+ NVIDIA models forever. And all of this is connected into ONE combo that automatically falls through the chain when any single provider is throttled or busy.

Pollinations is insane — no signup, no API key, literally zero friction. You add it as a provider in OmniRoute with an empty key field and it works.

The Combo System — OmniRoute's core innovation

Combos are OmniRoute's killer feature. A combo is a named chain of models from different providers with a routing strategy. When you send a request to OmniRoute using a combo name as the "model" field, OmniRoute walks the chain using the strategy you chose.

How combos work

Combo: "free-forever"
  Strategy: priority
  Nodes:
    1. kr/claude-sonnet-4.5     → Kiro (free Claude, unlimited)
    2. if/kimi-k2-thinking      → Qoder (free, unlimited)
    3. lc/LongCat-Flash-Lite    → LongCat (free, 50M/day)
    4. qw/qwen3-coder-plus      → Qwen (free, unlimited)
    5. groq/llama-3.3-70b       → Groq (free, 14.4K/day)

How it works:
  Request arrives → OmniRoute tries Node 1 (Kiro)
  → If Kiro is throttled/slow → instantly falls to Node 2 (Qoder)
  → If Qoder is somehow saturated → falls to Node 3 (LongCat)
  → And so on, until one succeeds

Your tool sees: a successful response. It has no idea 3 providers were tried.

13 Routing Strategies

Strategy	What It Does	Best For
Priority	Uses nodes in order, falls to next only on failure	Maximizing primary provider usage
Round Robin	Cycles through nodes with configurable sticky limit (default 3)	Even distribution
Fill First	Exhausts one account before moving to next	Making sure you drain free tiers
Least Used	Routes to the account with oldest lastUsedAt	Balanced distribution over time
Cost Optimized	Routes to cheapest available provider	Minimizing spend
P2C	Picks 2 random nodes, routes to the healthier one	Smart load balance with health awareness
Random	Fisher-Yates shuffle, random selection each request	Unpredictability / anti-fingerprinting
Weighted	Assigns percentage weight to each node	Fine-grained traffic shaping (70% Claude / 30% Gemini)
Auto	6-factor scoring (quota, health, cost, latency, task-fit, stability)	Hands-off intelligent routing
LKGP	Last Known Good Provider — sticks to whatever worked last	Session stickiness / consistency
Context Optimized	Routes to maximize context window size	Long-context workflows
Context Relay	Priority routing + session handoff summaries when accounts rotate	Preserving context across provider switches
Strict Random	True random without sticky affinity	Stateless load distribution

Auto-Combo: The AI that routes your AI

• Quota (20%): remaining capacity
• Health (25%): circuit breaker state
• Cost Inverse (20%): cheaper = higher score
• Latency Inverse (15%): faster = higher score (using real p95 latency data)
• Task Fit (10%): model × task type fitness
• Stability (10%): low variance in latency/errors

4 mode packs: Ship Fast, Cost Saver, Quality First, Offline Friendly. Self-heals: providers scoring below 0.2 are auto-excluded for 5 min (progressive backoff up to 30 min).

Context Relay: Session continuity across account rotations

When a combo rotates accounts mid-session, OmniRoute generates a structured handoff summary in the background BEFORE the switch. When the next account takes over, the summary is injected as a system message. You continue exactly where you left off.

The 4-Tier Smart Fallback

TIER 1: SUBSCRIPTION

Claude Pro, Codex Plus, GitHub Copilot → Use your paid quota first

↓ quota exhausted

TIER 2: API KEY

DeepSeek ($0.27/1M), xAI Grok-4 ($0.20/1M) → Cheap pay-per-use

↓ budget limit hit

TIER 3: CHEAP

GLM-5 ($0.50/1M), MiniMax M2.5 ($0.30/1M) → Ultra-cheap backup

↓ budget limit hit

TIER 4: FREE — $0 FOREVER

Kiro, Qoder, LongCat, Pollinations, Qwen, Cloudflare, Scaleway, Groq, NVIDIA, Cerebras → Never stops.

Every tool connects through one endpoint

# Claude Code
ANTHROPIC_BASE_URL=http://localhost:20128 claude

# Codex CLI
OPENAI_BASE_URL=http://localhost:20128/v1 codex

# Cursor IDE
Settings → Models → OpenAI-compatible
Base URL: http://localhost:20128/v1
API Key: [your OmniRoute key]

# Cline / Continue / Kilo Code / OpenClaw / OpenCode
Same pattern — Base URL: http://localhost:20128/v1

14 CLI agents total supported: Claude Code, OpenAI Codex, Antigravity, Cursor IDE, Cline, GitHub Copilot, Continue, Kilo Code, OpenCode, Kiro AI, Factory Droid, OpenClaw, NanoBot, PicoClaw.

MCP Server — 25 tools, 3 transports, 10 scopes

omniroute --mcp

• omniroute_get_health — gateway health, circuit breakers, uptime
• omniroute_switch_combo — switch active combo mid-session
• omniroute_check_quota — remaining quota per provider
• omniroute_cost_report — spending breakdown in real time
• omniroute_simulate_route — dry-run routing simulation with fallback tree
• omniroute_best_combo_for_task — task-fitness recommendation with alternatives
• omniroute_set_budget_guard — session budget with degrade/block/alert actions
• omniroute_explain_route — explain a past routing decision
• + 17 more tools. Memory tools (3). Skill tools (4).

3 Transports: stdio, SSE, Streamable HTTP. 10 Scopes. Full audit trail for every call.

Installation — 30 seconds

npm install -g omniroute
omniroute

Also: Docker (AMD64 + ARM64), Electron Desktop App (Windows/macOS/Linux), Source install.

Real-world playbooks

Playbook A: $0/month — Code forever for free

Combo: "free-forever"
  Strategy: priority
  1. kr/claude-sonnet-4.5     → Kiro (unlimited Claude)
  2. if/kimi-k2-thinking      → Qoder (unlimited)
  3. lc/LongCat-Flash-Lite    → LongCat (50M/day)
  4. pol/openai               → Pollinations (free GPT-5!)
  5. qw/qwen3-coder-plus      → Qwen (unlimited)

Monthly cost: $0

Playbook B: Maximize paid subscription

1. cc/claude-opus-4-6       → Claude Pro (use every token)
2. kr/claude-sonnet-4.5     → Kiro (free Claude when Pro runs out)
3. if/kimi-k2-thinking      → Qoder (unlimited free overflow)

Monthly cost: $20. Zero interruptions.

Playbook D: 7-layer always-on

1. cc/claude-opus-4-6   → Best quality
2. cx/gpt-5.2-codex     → Second best
3. xai/grok-4-fast      → Ultra-fast ($0.20/1M)
4. glm/glm-5            → Cheap ($0.50/1M)
5. minimax/M2.5         → Ultra-cheap ($0.30/1M)
6. kr/claude-sonnet-4.5 → Free Claude
7. if/kimi-k2-thinking  → Free unlimited