OpenSquilla

OpenSquilla / opensquilla

Public

OpenSquilla — Token-Efficient AI Agent

opensquilla.ai
46
5
100% credibility
Found May 08, 2026 at 46 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

OpenSquilla is an open-source runtime for efficient AI agents that routes tasks across multiple language models with support for web UI, CLI, and chat integrations.

How It Works

1
🔍 Discover OpenSquilla

You hear about a smart AI helper that saves money on chats while giving better answers.

2
📥 Get it on your computer

Download and run a simple installer that sets everything up quickly.

3
🧙 Connect your AI brain

Follow a friendly guide to link a smart service so your helper can think and respond.

4
🚀 Start your assistant

Launch it with one command and open a web page to see it ready.

5
💬 Start chatting

Talk to it through a web page, command line, or apps like Slack or Discord.

🎉 Smarter help, less cost

Enjoy powerful answers that use fewer resources, saving time and money every day.

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Star Growth

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AI-Generated Review

What is opensquilla?

OpenSquilla is a Python-based, token-efficient AI agent runtime that powers conversations via Web UI, CLI, or channels like Slack, Discord, Telegram, and Teams. It routes prompts to 20+ LLM providers (OpenRouter, OpenAI, Anthropic, Ollama) using on-device models to pick the cheapest capable one, adds persistent memory, web search, local embeddings, and on-demand tools like coding agents or cron jobs. Users get a unified gateway at localhost:18790 with durable sessions and adaptive reasoning that skips heavy prompts for simple replies.

Why is it gaining traction?

Its SquillaRouter slashes token usage—benchmarks show 1.7M input tokens and $0.69 cost vs. competitors' 3M+ and $6+—making AI agents viable on tight budgets. Multi-channel support and easy onboarding (bash install.sh then opensquilla onboard) let devs deploy fast without config hell. The secure sandbox and complaint-based upgrades handle real-world messiness without wasting cycles.

Who should use this?

Backend devs building internal chatbots or automation agents that span Slack, Discord, or terminals. Teams optimizing LLM spend on routine tasks like debugging, research, or scheduling. Python scripters wanting persistent, multi-turn agents without vendor lock-in.

Verdict

Try it if token costs kill your prototypes—installs cleanly and delivers savings immediately. With 46 stars and 1.0% credibility score, it's early but docs and benchmarks are solid; watch for more channel adapters before production.

(187 words)

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