by clearloop · Codex Skill · ★ 97
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
[!NOTE] This repo is now public archive. The project is now called openwalrus/walrus. Walrus Run autonomous agents with built-in LLM inference. No API keys. No cloud. Just one binary. Or install with Cargo: What It Does Local inference — runs LLMs on your machine (Metal on macOS, CUDA on Linux) Persistent memory — agents remember across sessions (SQLite + FTS5) Built-in tools — file I/O, shell, MCP servers, cron scheduling Multi-channel — talk to your agents from the terminal, Telegram, or Discord Skills — extend agents with Markdown prompt files, no code needed Quick Start Models are configured in . Point it at a local model or any OpenAI-compatible API: Cloud Providers walrus works with OpenAI, Anthropic, DeepSeek, and other OpenAI-compatible APIs. Local inference is the default — cloud is opt-in. License GPL-3.0
| Stars | 97 |
| Forks | 14 |
| Language | Rust |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 65.3719313242768/100 |
| Open Issues | 1 |
| Last Updated | 2026-03-11 |
| Created | 2025-01-01 |
| Platforms | rust |
| Est. Tokens | ~146k |
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cydonia is Core abstractions for your agentic workflow. It is categorized as a Codex Skill with 97 GitHub stars.
cydonia is primarily written in Rust. It covers topics such as agent, agentic, agentic-workflow.
You can find installation instructions and usage details in the cydonia GitHub repository at github.com/clearloop/cydonia. The project has 97 stars and 14 forks, indicating an active community.
cydonia is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to cydonia on Agent Skills Hub include SimpleLLMFunc, agent-builder, agents. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.