diff --git a/README.md b/README.md
index f57b4367..cff0e6ca 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
### OpenDeRisk
-OpenDeRisk AI-Native Risk Intelligence Systems —— Your application system risk intelligent manager provides 7 * 24-hour comprehensive and in-depth protection.
+OpenDeRisk is an AI-Native Risk Intelligence System designed as your application system's intelligent manager, providing 7×24 hour comprehensive and in-depth protection.
@@ -27,149 +27,116 @@ OpenDeRisk AI-Native Risk Intelligence Systems —— Your application system ri
-[**English**](README.md) | [**简体中文**](README.zh.md) | [**日本語**](README.ja.md) | [**视频教程**](https://www.youtube.com/watch?v=1qDIu-Jwdf0)
+[**English**](README.md) | [**简体中文**](README.zh.md) | [**日本語**](README.ja.md) | [**Video Tutorial**](https://www.youtube.com/watch?v=1qDIu-Jwdf0)
-
-### News
-- [2025/10] 🔥 We released OpenDerisk v0.2. [OpenDerisk V0.2 ReleaseNote](./docs/docs/OpenDerisk_v0.2.md)
-
-
### 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.
+1. **DeepResearch RCA:** Quickly locate root causes through in-depth analysis of logs, traces, and code.
+2. **Visualized Evidence Chain:** Fully visualize diagnostic processes and evidence chains for clear, accurate judgment.
+3. **Multi-Agent Collaboration:** SRE-Agent, Code-Agent, ReportAgent, Vis-Agent, and Data-Agent working in coordination.
+4. **Open-Source Architecture:** Built with a completely open architecture, enabling framework and code reuse in open-source projects.
-### Architure
+### Architecture
#### Introduction
-
-- [OpenDerisk Documents](https://deepwiki.com/derisk-ai/OpenDerisk)
-
-- [OpenDerisk DeepWiki](https://deepwiki.com/derisk-ai/OpenDerisk)
-
-
-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.
+The system employs a multi-agent architecture. Currently, the code primarily implements the highlighted components. Alert awareness is based on Microsoft's open-source [OpenRCA dataset](https://github.com/microsoft/OpenRCA). The decompressed dataset is approximately 26GB. On this dataset, we achieve root cause analysis through multi-agent collaboration, with Code-Agent dynamically writing 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.
+**Data Layer:** Pull the large-scale OpenRCA dataset (20GB) from GitHub, decompress locally, and process 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).
+**Logic Layer:** Multi-agent architecture with SRE-Agent, Code-Agent, ReportAgent, Vis-Agent, and Data-Agent collaborating for deep 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.
+**Visualization Layer:** Use the Vis protocol to dynamically render the entire processing flow and evidence chain, as well as the multi-role collaboration and switching process.
Digital Employees (Agents) in OpenDeRisk
-### Quick Start
+### Install (recommended)
-Install uv
+#### Install via curl
```shell
-curl -LsSf https://astral.sh/uv/install.sh | sh
+# Download and install latest version
+curl -fsSL https://raw.githubusercontent.com/derisk-ai/OpenDerisk/main/install.sh | bash
```
+#### Configuration File
+After installation, you need to configure the system. Create a configuration file:
+Edit `~/.openderisk/derisk-proxy-aliyun.toml` and set your API keys.
-#### Install Packages
-
+#### Start
```
-uv sync --all-packages --frozen \
---extra "base" \
---extra "proxy_openai" \
---extra "rag" \
---extra "storage_chromadb" \
---extra "derisks" \
---extra "storage_oss2" \
---extra "client" \
---extra "ext_base"
+openderisk-server
```
-#### Start
+### From source(development)
-Configure the API_KEY in the `derisk-proxy-aliyun.toml` file, then run the following command to start.
+Install uv (required):
+```shell
+git clone https://github.com/derisk-ai/OpenDerisk.git
-> Note: By default, we use the Telecom dataset from the OpenRCA dataset. You can download it via the link or the following command:
+cd OpenDerisk
+
+# Install Dependencies
+sh scripts/prepare_release.sh
+```
-> gdown https://drive.google.com/uc?id=1cyOKpqyAP4fy-QiJ6a_cKuwR7D46zyVe
+#### Start Server
-After downloading, move the datasets to the path `pilot/datasets/`
+Configure the API_KEY in `derisk-proxy-aliyun.toml`, then run:
+
+> Note: By default, we use the Telecom dataset from OpenRCA. Download via:
+> `gdown https://drive.google.com/uc?id=1cyOKpqyAP4fy-QiJ6a_cKuwR7D46zyVe`
+
+After downloading, move datasets to `pilot/datasets/`
Run the startup command:
-```
+```bash
uv run python packages/derisk-app/src/derisk_app/derisk_server.py --config configs/derisk-proxy-aliyun.toml
```
-#### Visit Website
+#### Access Web UI
Open your browser and visit [`http://localhost:7777`](http://localhost:7777)
-### How to Use?
-* AI-SRE(OpenRCA)
- - !Notice, We Use the OpenRCA Datasets[Bank Dataset](https://drive.usercontent.google.com/download?id=1enBrdPT3wLG94ITGbSOwUFg9fkLR-16R&export=download&confirm=t&uuid=42621058-41af-45bf-88a6-64c00bfd2f2e),
- - You can download the dataset using next link:
- ```
- gdown https://drive.google.com/uc?id=1enBrdPT3wLG94ITGbSOwUFg9fkLR-16R
- ```
- - Put the datasets to the path ${derisk}/pilot/datasets。
-* Flame Graph Assistant
- - Upload the flame graph (Java/Python) of your local application service process to the assistant for analysis and inquiries.
-* DataExpert
- - Upload your metrics, logs, traces, or various Excel data sheets for conversational analysis.
-
-
-### Rapid Development
-* Agent Development
- Refer to the implementation logic under `derisk-ext.agent.agents`.
-* Tool Development
- * Local tool
- * MCP
-* Other Development
- Documentation is under preparation...
-
-* DeRisk-Skills Development
+### Usage Modes
+* **AI-SRE (OpenRCA)**
+ - Notice: We use the OpenRCA Dataset [Bank Dataset](https://drive.usercontent.google.com/download?id=1enBrdPT3wLG94ITGbSOwUFg9fkLR-16R&export=download&confirm=t&uuid=42621058-41af-45bf-88a6-64c00bfd2f2e)
+ - Download: `gdown https://drive.google.com/uc?id=1enBrdPT3wLG94ITGbSOwUFg9fkLR-16R`
+ - Place datasets in `${derisk}/pilot/datasets`
+* **Flame Graph Assistant**
+ - Upload flame graphs (Java/Python) from your local application for analysis
+* **DataExpert**
+ - Upload metrics, logs, traces, or Excel data for conversational analysis
+
+### Development
+* **Agent Development**
+ - Refer to implementations under `derisk-ext.agent.agents`
+* **Tool Development**
+ - Skills
+ - MCP (Model Context Protocol)
+* **DeRisk-Skills**
- [derisk-skills](https://github.com/derisk-ai/derisk_skills)
#### Execution Results
-As shown in the figure below, this demonstrates a scenario where multiple agents collaborate to handle a complex operational diagnostic task.
-
-### RoadMap
-- [x] 0530 V0.1 Version: Based on domain knowledge and MCP services, achieving anomaly awareness -> autonomous decision-making -> adaptive execution and issue resolution.
- - [x] Domain knowledge engine for technical risks
- - [x] Reasoning engine driven by large models for anomaly awareness -> decision-making -> execution
- - [x] Automated troubleshooting and fixes
-
-- [x] 0830 V0.2 Version
- - [x] MCP services and management for technical risks
- - [x] Support for custom binding of knowledge and MCP tools
- - [x] 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
-
### Citation
-The code (training, serving, and evaluation) in this repository is mostly developed for or derived from the paper below. Please cite it if you find the repository helpful.
+If you find this repository helpful, please cite:
```
@misc{di2025openderiskindustrialframeworkaidriven,
title={OpenDerisk: An Industrial Framework for AI-Driven SRE, with Design, Implementation, and Case Studies},
@@ -190,14 +157,13 @@ The code (training, serving, and evaluation) in this repository is mostly develo
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!
+Join our DingTalk group and share your experience with other developers!
-
-
+
+
\ No newline at end of file
diff --git a/README.zh.md b/README.zh.md
index 071a7ed6..ec78af23 100644
--- a/README.zh.md
+++ b/README.zh.md
@@ -1,6 +1,6 @@
### OpenDeRisk
-OpenDeRisk AI 原生风险智能系统 —— 7\*24H 应用系统AI数字运维助手(AI-SRE), 我们的愿景是, 为每个应用系统提供一个7\*24H的AI系统数字管家，并能与真人进行协同，7\*24H处理业务问题，形成7\*24H得深度护航与防护网。
+OpenDeRisk AI 原生风险智能系统 —— 7×24 小时应用系统 AI 数字运维助手 (AI-SRE)。我们的愿景是为每个应用系统提供一个 7×24 小时的 AI 系统数字管家，能够与真人协同工作，7×24 小时处理业务问题，构建深度护航与防护网。
