Openderisk

What is Openderisk

OpenDerisk is an AI-Native Risk Intelligence System designed to manage and mitigate risks in application systems. It provides comprehensive and in-depth protection 24/7.

Use cases

Use cases for OpenDerisk include monitoring application vulnerabilities, managing compliance risks, and providing real-time alerts for potential threats.

How to use

To use OpenDerisk, install the required packages using the provided commands, start the server with the specified configuration, and access the application through your web browser at ‘http://localhost:7777’.

Key features

Key features of OpenDerisk include AI-driven risk assessment, 24/7 monitoring, comprehensive protection mechanisms, and a user-friendly interface for managing risks.

Where to use

OpenDerisk can be utilized in various fields including software development, cybersecurity, and any application environment where risk management is crucial.

OpenDeRisk

OpenDeRisk AI-Native Risk Intelligence Systems —— Your application system risk intelligent manager provides 7 * 24-hour comprehensive and in-depth protection.

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.

Architure

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. 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

Quick Start

Install uv

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 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

Execution Results

As shown in the figure below, this demonstrates a scenario where multiple agents collaborate to handle a complex operational diagnostic task.

Acknowledgement

• DB-GPT
• GPT-Vis
• MetaGPT
• 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. 🤝

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