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OpenSquilla: @hasantoxr ran a 10+ turn research workflow (published 2026-05-12)…

2026-05-12 · 10:53 UTC ·@hasantoxr ·1 min read

Brief

OpenSquilla: @hasantoxr ran a 10+ turn research workflow (published 2026-05-12) and says smart routing routed simple prompts to cheaper models and complex ones to full power, cutting his token bill by ~67% versus his usual agent while preserving memory and output quality. OpenSquilla (open-source, Python, local‑first) claims 60–80% cost reduction and launched the #10MTokenChallenge (30 winners × 10M OpenRouter credits).

Source evidence

Just put @OpenSquilla through a real long-running workflow drafting and iterating on a full research analysis with 10+ back-and-forth steps.

The smart routing was insane: simple prompts went to cheap models, complex ones still got full power, and my token bill came in ~67% lower than my usual agent setup while keeping memory sharp and outputs clean.

This is exactly what I’ve been wanting for practical daily agent work. Open-source, Python, local-first game changer.

10MTokenChallenge

Video

OpenSquilla (@OpenSquilla)

Long-running agents shouldn’t pay frontier-model prices for every turn.
We‘ve been quietly building our agent with content-aware model routing, memory consolidation, and adaptive token compression. Today, it goes public as OpenSquilla — an open-source Python agent.
Public benchmark: up to 60%-80% lower model cost on mixed long-running tasks.
Open source. Local first. Python based.
opensquilla.ai/

Don’t take our word for it — Verify the savings yourself.

10M Token Bill Challenge:

post side-by-side bills vs. any agent (the best performing models).
30 winners × 10M OpenRouter credits each,

Three categories:
🥇 10 Faithful Reproduction ·
💰 10 Best Savings Case ·
🐛 10 Quality Bug Report

10MTokenChallenge

— https://nitter.net/OpenSquilla/status/2052599949544849757#m