OpenSquilla Token-Efficient Agent Runtime Model Card

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OpenSquilla Token-Efficient Agent Runtime Model Card

1. Role in Self Evolve

opensquilla/opensquilla is a token-efficient AI agent runtime focused on higher intelligence density with OpenClaw, memory, and MCP integration signals. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.

2. Working Principle

optimize agent intelligence density under fixed token budgets -> combine runtime controls with memory and MCP connectivity -> keep execution quality stable while reducing context waste -> improve long-horizon self-improving loops with explicit efficiency constraints

3. Evidence Path

public GitHub API metadata showed 2,184 stars, 148 forks, 48 commit pages, Apache-2.0 license, and Python-first runtime composition. GitHub metadata was captured via public API in this iteration (without authenticated token); this card marks counts as API-observed with possible rate-limit drift.

4. Teaching Use

Use this card to explain Token-Efficient Agent Runtime with OpenClaw/MCP/Memory Integration: it shows how harness/runtime/benchmark layers convert agent behavior into reproducible and auditable engineering workflows.

5. Limits

The repository was not cloned in this iteration; no benchmark run, plugin install, workflow execution, or agent loop experiment was executed. Counts and claims are visible public-page/search signals unless independently revalidated later.