Update README.md

README.md

@@ -10,7 +10,84 @@ tag:
   - agent-demo-track
   - llamaindex
   - agents
+  - modal
+  - nebius
 short_description: Multi agent code analyser using industry standard tools
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Multi-Agent Code Analysis System
+
+**A sophisticated multi-agent system for intelligent, automated code analysis.**
+
+## The Problem We Solve
+
+In today's fast-paced development environments, maintaining high code quality, robust security, and comprehensive documentation is a significant challenge. Manual code reviews are essential but can be:
+
+*   **Time-consuming:** Taking valuable developer time away from feature development.
+*   **Error-prone:** Reviewers can miss subtle bugs or inconsistencies.
+*   **Inconsistent:** The depth and focus of reviews can vary between reviewers and over time.
+*   **A Bottleneck:** Slowing down deployment pipelines.
+
+Neglecting these aspects leads to technical debt, security vulnerabilities, difficult onboarding, and increased maintenance costs.
+
+## This Solution: Intelligent Automated Analysis
+
+The Multi-Agent Code Analysis System leverages a team of specialized AI agents to perform a thorough and consistent analysis of your codebase. It intelligently orchestrates these agents and aggregates their findings to provide actionable insights, helping you build better, safer, and more maintainable software.
+
+Key components include:
+
+*   **Orchestrator:** An intelligent core that assesses the input code and decides the appropriate level of analysis and which specialized agents to deploy.
+*   **DocAgent:** Focuses on analyzing code for documentation quality, ensuring docstrings are present, informative, and up-to-date.
+*   **SecurityAgent:** Scans code for common security vulnerabilities, helping to proactively identify and mitigate risks.
+*   **Aggregation Engine:** Synthesizes the outputs from all active agents into a single, comprehensive report with clear findings, recommendations, and even potential code fixes.
+
+## Key Features
+
+*   **Automated Documentation Analysis:** Ensures code is well-commented and easy to understand.
+*   **Automated Security Vulnerability Detection:** Identifies potential security flaws before they reach production.
+*   **Intelligent Orchestration:** Dynamically determines the required analysis depth and deploys relevant agents.
+*   **Comprehensive & Actionable Reporting:** Provides clear summaries, lists of issues, and practical recommendations.
+*   **LLM-Powered Insights:** Utilizes Large Language Models for nuanced understanding and generation of analysis.
+*   **Scalable Architecture:** Designed to handle diverse code analysis tasks efficiently (with potential for scaling via technologies like Modal).
+*   **User-Friendly Interface:** Presents analysis results through a Gradio-based UI, including a specialized `gradio-codeanalysisviewer`.
+
+## How It Works
+
+The system follows a sophisticated workflow to analyze code:
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f707fdb5e78cc6b0ed31f0a/52keupVy5L3Me98eeOxMg.png)
+(the items in red color are planned to be implemented)
+
+1.  **Code Input:** The system receives the code to be analyzed.
+2.  **Initial Assessment:** An LLM-powered orchestrator evaluates the code and determines the analysis strategy (e.g., depth, which agents to invoke).
+3.  **Agent Dispatch:** Based on the assessment, tasks are dispatched to specialized agents (e.g., `DocAgent`, `SecurityAgent`).
+4.  **Parallel Analysis:** Agents perform their specific analysis tasks on the code.
+5.  **Results Collection:** Findings from all active agents are collected.
+6.  **Final Aggregation:** Another LLM-powered step synthesizes all collected data into a unified, actionable report.
+7.  **Report Output:** The system presents a comprehensive report detailing issues, recommendations, and potentially suggested fixes.
+
+## Benefits for Your Business
+
+*   **Increased Developer Productivity:** Automates routine checks, freeing up developers to focus on complex problem-solving and innovation.
+*   **Enhanced Code Quality & Maintainability:** Enforces coding standards and documentation best practices, leading to cleaner, more understandable, and easier-to-maintain code.
+*   **Improved Security & Compliance:** Proactively identifies and helps remediate security vulnerabilities, reducing risk and aiding compliance efforts.
+*   **Reduced Review Bottlenecks:** Speeds up the code review process, enabling faster development cycles and quicker time-to-market.
+*   **Consistent Standards:** Ensures uniform application of coding and security standards across all projects and teams.
+*   **Better Onboarding:** Well-documented code makes it easier for new developers to get up to speed.
+
+## Technology Stack
+
+*   **Python**
+*   **LlamaIndex:** For LLM integration, agentic workflows, and core AI capabilities.
+*   **Pydantic:** For robust data validation and schema management
+*   **Gradio:** For building the interactive user interface.
+*   **Modal (potential):** For scalable cloud deployment and execution of analysis tasks.
+*   **Nebius:** For LLM endpoint.
+
+## Future Enhancements
+
+*   *Support for more programming languages.*
+*   *Additional specialized agents (e.g., performance profiler, style checker, refactoring agent).*
+*   *Additional specialized tools for these agents via MCP.*
+*   *Customizable analysis rules and policies.*