# OpenSquilla — Token-Efficient AI Agent with Higher Intelligence Density

> OpenSquilla is an open-source AI agent framework that delivers the same output quality with fewer tokens by compressing context, reusing skills, and optimizing prompt chains.

## Install

Save in your project root:

# OpenSquilla — Token-Efficient AI Agent with Higher Intelligence Density

## Quick Use
```bash
pip install opensquilla
opensquilla init my-project
opensquilla run "refactor the auth module"
```

## Introduction
OpenSquilla is an AI agent framework designed around token efficiency. It compresses context, caches intermediate reasoning, and reuses learned skills across tasks so that every token spent produces more useful work. The goal is higher intelligence density — same budget, better results.

## What OpenSquilla Does
- Runs autonomous coding and research tasks with reduced token consumption
- Caches and reuses skill artifacts across sessions
- Compresses context windows to fit more relevant information per call
- Supports multiple LLM providers including OpenAI, Anthropic, and local models
- Provides a Python SDK for building custom agent workflows

## Architecture Overview
OpenSquilla wraps LLM calls in a token-aware orchestration layer. Before each call, the context manager compresses history, prioritizes relevant files, and injects cached skill definitions. A skill registry stores reusable prompt patterns learned from previous tasks. The agent loop monitors token spend and adjusts strategy when approaching budget limits.

## Self-Hosting & Configuration
- Install via pip and configure with a YAML file
- Set your LLM API keys as environment variables
- Skill cache is stored locally in a .opensquilla directory
- Supports custom tool definitions via a plugin interface
- Works offline with local LLM backends like Ollama

## Key Features
- Token tracking and budget enforcement per task
- Skill registry that learns from completed tasks
- Context compression that preserves semantic relevance
- Multi-provider support with automatic fallback
- Extensible plugin architecture for custom tools

## Comparison with Similar Tools
- **Claude Code** — full-featured agent harness; OpenSquilla focuses specifically on token optimization
- **Aider** — git-aware pair programmer; OpenSquilla adds skill reuse and context compression
- **OpenCode** — terminal-based coding agent; OpenSquilla adds a token-efficiency layer
- **Cline** — VS Code-based agent; OpenSquilla is CLI-first with a Python SDK

## FAQ
**Q: How much token savings does OpenSquilla achieve?**
A: The project reports up to 35% token savings on repeated tasks through skill reuse and context compression. Actual savings vary by workload.

**Q: Can I use it with local models?**
A: Yes. It supports any OpenAI-compatible API endpoint, including Ollama and vLLM.

**Q: Does skill reuse affect output quality?**
A: Skills are validated before caching. Poorly performing skills are deprioritized automatically.

**Q: Is it production-ready?**
A: It is under active development. Evaluate it in a staging environment before production use.

## Sources
- https://github.com/opensquilla/opensquilla

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Source: https://tokrepo.com/en/workflows/asset-d49151bd
Author: AI Open Source