OpenDerisk

### OpenDeRisk OpenDeRisk AI-Native Risk Intelligence Systems —— Your application system risk intelligent manager provides 7 * 24-hour comprehensive and in-depth protection. <div align="center"> <p> <a href="https://github.com/derisk-ai/OpenDerisk"> <img alt="stars" src="https://img.shields.io/github/stars/derisk-ai/OpenDerisk?style=social" /> </a> <a href="https://github.com/derisk-ai/OpenDerisk"> <img alt="forks" src="https://img.shields.io/github/forks/derisk-ai/OpenDerisk?style=social" /> </a> <a href="https://opensource.org/licenses/MIT"> <img alt="License: MIT" src="https://img.shields.io/badge/License-MIT-yellow.svg" /> </a> <a href="https://github.com/derisk-ai/OpenDerisk/releases"> <img alt="Release Notes" src="https://img.shields.io/github/release/derisk-ai/OpenDerisk" /> </a> <a href="https://github.com/derisk-ai/OpenDerisk/issues"> <img alt="Open Issues" src="https://img.shields.io/github/issues-raw/derisk-ai/OpenDerisk" /> </a> <a href="https://codespaces.new/derisk-ai/OpenDerisk"> <img alt="Open in GitHub Codespaces" src="https://github.com/codespaces/badge.svg" /> </a> </p> [**English**](README.md) | [**简体中文**](README.zh.md) | [**视频教程**](https://www.youtube.com/watch?v=1qDIu-Jwdf0) </div> ### Features 1. **DeepResearch RCA:** Quickly locate the root cause of issues through in-depth analysis of logs, traces, and code. 2. **Visualized Evidence Chain:** Fully visualize the diagnostic process and evidence chain, making the diagnosis clear and enabling quick judgment of accuracy. 3. **Multi-Agent Collaboration:** Collaboration among SRE-Agent, Code-Agent, ReportAgent, Vis-Agent, and Data-Agent. 4. **Open and Open-Source Architecture:** OpenDeRisk is built with a completely open and open-source architecture, allowing related frameworks and code to be used out of the box in open-source projects. <p align="left"> <img src="./assets/features.jpg" width="100%" /> </p> ### Architure <p align="left"> <img src="./assets/arch_en.jpg" width="100%" /> </p> The system adopts a multi-agent architecture. Currently, the code mainly implements the green-highlighted parts. Alert awareness is based on Microsoft's open-source [OpenRCA dataset](https://github.com/microsoft/OpenRCA). The dataset size is approximately 26GB after decompression. On this dataset, we achieve root cause analysis and diagnosis through multi-agent collaboration, where the Code-Agent dynamically writes code for final analysis. #### Technical Implementation **Data Layer:** Pull the large-scale OpenRCA dataset (20GB) from GitHub, decompress it locally, and process it for analysis. **Logic Layer:** Multi-agent architecture, with collaboration among SRE-Agent, Code-Agent, ReportAgent, Vis-Agent, and Data-Agent to perform in-depth DeepResearch RCA (Root Cause Analysis). **Visualization Layer:** Use the Vis protocol to dynamically render the entire processing flow and evidence chain, as well as the process of multi-role collaboration and switching. Digital Employees (Agents) in OpenDeRisk <p align="left"> <img src="./assets/ai-agent.png" width="100%" /> </p> ### Quick Start Install uv ```python curl -LsSf https://astral.sh/uv/install.sh | sh ``` #### Install Packages ``` uv sync --all-packages --frozen \ --extra "base" \ --extra "proxy_openai" \ --extra "rag" \ --extra "storage_chromadb" \ --extra "client" ``` #### Start Configure the API_KEY in the derisk-proxy-deepseek.toml file, then run the following command to start. > Note: By default, we use the Telecom dataset from the OpenRCA dataset. You can download it via the link or the following command: > gdown https://drive.google.com/uc?id=1cyOKpqyAP4fy-QiJ6a_cKuwR7D46zyVe After downloading, modify the dataset path in the file [basic_prompt_Telecom.py](https://github.com/derisk-ai/OpenDerisk/blob/main/packages/derisk-ext/src/derisk_ext/ai_sre/resource/basic_prompt_Telecom.py) to the local absolute path. Run the startup command: ``` uv run python packages/derisk-app/src/derisk_app/derisk_server.py --config configs/derisk-proxy-deepseek.toml ``` #### Visit Website Open your browser and visit [`http://localhost:7777`](http://localhost:7777) <p align="left"> <img src="./assets/index.jpg" width="100%" /> </p> #### Execution Results As shown in the figure below, this demonstrates a scenario where multiple agents collaborate to handle a complex operational diagnostic task. <p align="left"> <img src="./assets/scene_demo.png" width="100%" /> </p> ### RoadMap - [x] 0530 V0.1 Version: Based on domain knowledge and MCP services, achieving anomaly awareness -> autonomous decision-making -> adaptive execution and issue resolution. - [ ] Domain knowledge engine for technical risks - [x] Reasoning engine driven by large models for anomaly awareness -> decision-making -> execution - [x] Automated troubleshooting and fixes - [ ] 0730 V0.2 Version - [ ] MCP services and management for technical risks - [ ] Support for custom binding of knowledge and MCP tools - [ ] Support for 3+ DevOps domain MCP services - [ ] 0930 V0.3 Version - [ ] Support for integration with production environments - [ ] Provide a complete production environment deployment solution, supporting production issue diagnosis. - [ ] 1230 V0.4 Version - [ ] End-to-end AIOps online Agentic RL - [ ] End-to-end evaluation capabilities ### Acknowledgement - [DB-GPT](https://github.com/eosphoros-ai/DB-GPT) - [GPT-Vis](https://github.com/antvis/GPT-Vis) - [MetaGPT](https://github.com/FoundationAgents/MetaGPT) - [OpenRCA](https://github.com/microsoft/OpenRCA) The OpenDeRisk-AI community is dedicated to building AI-native risk intelligence systems. 🛡️ We hope our community can provide you with better services, and we also hope that you can join us to create a better future together. 🤝 [](https://star-history.com/#derisk-ai/OpenDerisk) ### Community Group Join our networking group on Dingding and share your experience with other developers! <div align="center" style="display: flex; gap: 20px;"> <img src="assets/derisk-ai.jpg" alt="OpenDeRisk-AI 交流群" width="200" /> </div>