eclypsa-ai - An open-source, modular, AI-extensible platform fo

项目说明

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<img src="assets/eclypsa-banner.png" alt="ECLYPSA AI Banner" width="100%">

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🛡️ ECLYPSA AI

Autonomous, Pri & High-Performance AI Platform for Security & Automation

![]() ![Python]() ![Go]() ![Docker]() 

Architecture • Quick Start • Docker Deployment • Plugin De • Contributing

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👁️ O

ECLYPSA AI is a modular, pri open-source AI platform engineered for security automation, high-speed reconnaissance, and autonomous task execution. By coupling a pythonic ReAct (Reasoning + Acting) Agent Loop with a high-performance Go execution engine, ECLYPSA AI bridges the gap between local/cloud LLM intelligence and low-latency system-le.

Designed to operate seamlessly on-premises with local models (Ollama / Llama 3) or pro (OpenAI / Groq), ECLYPSA AI ensures complete data privacy for security research and offensi pipelines.

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✨ Key Features

🧠 Autonomous ReAct Engine: Built-in reasoning loop allowing agents to e tasks, plan multi-step actions, execute tools, and reflect on obser.

⚡ Go-Powered High-Speed Subsystem: Multithreaded TCP port scanner and network banner grabber compiled natively in Go for near-zero latency.

🔌 Dynamic Plugin Architecture: Hot-swappable, isolated plugin loader allowing easy extension with custom security skills and tools.

🔒 Pri & Local LLM Native: First-class support for offline local execution (llama3, mistral, etc.) without telemetry leaking.

🌐 Embedded Control Center & REST API: Native HTTP/REST API gateway and lightweight web dashboard for real-time task monitoring and telemetry.

🐳 Multi-Stage Containerized Architecture: Docker Compose setup pro both the core platform and an isolated local LLM en out of the box.

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🏗️ System Architecture

📁 Repository Structure

🚀 Quick Start

Prerequisites

* Python 3.11+
* Go 1.22+ (Optional, for building nati locally)
* Ollama (Recommended for local model inference)

Local Installation

1. Clone the repository:

2. Run automated setup:

3. Compile the Go engine module:

4. en via test suite:

💻 Usage & CLI Interface

Running Autonomous Security Tasks

Execute tasks using the local Ollama provider:

Execute tasks using OpenAI:

Launching API & Web Dashboard

Start the integrated REST API Gateway and Dashboard Control Center:

Na to in your browser to access the ECLYPSA Operational Dashboard.

🐳 Docker Deployment

The fastest way to deploy ECLYPSA AI along with a fully local Ollama LLM container is using docker-compose:

Access the dashboard at

🔌 De Plugins

Adding custom tools to ECLYPSA AI is straightforward. Create a new .py file in the plugins/ directory extending BasePlugin:

The AgentPluginBridge will automatically discover and register your plugin as an executable tool for the ReAct Agent on startup!

🧪 Testing

Run the full unit test suite using unittest or the included Makefile:

🤝 Contributing

Contributions are welcome! Please follow these steps:
1. Fork the repository.
2. Create your feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.
Please refer to CODE_OF_CONDUCT.md and CONTRIBUTING.md for detailed guidelines.

💖 Sponsor / Donations

If you find ECLYPSA AI useful and want to support its de, you can sponsor via Binance Crypto / Binance Pay:

| Network / Method | Address / ID |
| :--- | :--- |
| Binance Pay ID | 560787034 |
| USDT (TRC20) | 0x2f44EDC56d3c972F49015C0932c1b39A97cB2F7f |
| USDT / BNB (BEP20) | 0x2f44edc56d3c972f49015c0932c1b39a97cb2f7f |

📄 License

Distributed under the MIT License. See LICENSE for more information.
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_{Built with precision for security researchers, penetration testers, and AI engineers.}
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项目信息

开发语言：Python

Star 数：2

技术要点

该项目属于防护思路方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕An open-source, modular, AI-extensible platform for authorized defensive security, automation, monitoring, digital forensics, incident response, education, and research.展开，提供了可运行的实现，对理解相关安全机制、工具原理与攻防技术具有较高的研究价值。

从技术实现角度看，此类项目的核心在于对底层原理的深入理解与工程化落地。开发者在研究过程中可以结合官方文档与社区讨论，梳理项目的初始化流程、核心模块职责与数据流转，逐步掌握其中的技术细节与设计思路。

使用与学习建议

1. 下载源码压缩包，解压到本地环境。

2. 根据项目类型准备对应的运行环境与依赖。

3. 按照 README 中的步骤完成配置与部署。

4. 在隔离的测试环境中运行验证，避免影响生产系统。

免责声明

本资源来源于开源社区，仅供安全研究、技术学习与合法授权范围内的攻防演练使用，请勿用于任何非法或侵害他人权益的用途。使用本项目产生的一切后果由使用者自行承担。