README.md

metadata

title: Package Conflict Identifier
emoji: 📦
colorFrom: blue
colorTo: indigo
sdk: docker
pinned: false

Package Conflict Identifier 📦🔍

Use "git" for accessing git and "origin" for accessing the hugging face spaces

git push git main , git push origin main

AI-powered package conflict identifier and resolver using Google's Agent Development Kit (ADK). It leverages a multi-agent architecture with Google Gemini and OpenRouter (Grok) models to diagnose dependency issues, research solutions, and generate fixed configuration files.

🚀 Try it Live!

Web UI	MCP Server Endpoint
	https://yash030-ai-package-doctor.hf.space/mcp/sse

---

🎯 Features

• 🤖 Advanced Multi-Agent Architecture:

  • Context Search Agent: Retrieves insights from past sessions using Pinecone vector memory.
  • Parallel Research Team: Concurrent searching of Official Docs and Community forums.
  • Web Crawl Agent: Uses Firecrawl (via OpenRouter) for deep web scraping of documentation.
  • Code Surgeon: Generates and validates requirements.txt fixes.

• 🧠 Hybrid Model Intelligence:

  • Google Gemini 2.0 Flash Lite: For high-speed reasoning and orchestration.
  • Grok 4.1 Fast (via OpenRouter): For specialized web crawling and context analysis.

• 🔌 Model Context Protocol (MCP) Server:

  • Exposes the agent's capabilities as a standard MCP tool (solve_dependency_issue).
  • Connects seamlessly to MCP clients like Claude Desktop or other AI assistants.

• 💾 Persistent Memory:

  • Short-Term: SQLite/PostgreSQL session storage.
  • Long-Term: Pinecone Vector Database for recalling past solutions.

• 🛠️ Intelligent Tooling:

  • retrieve_memory: Semantic search of previous conversations.
  • google_search: Live web search.
  • firecrawl: Advanced web scraping.

📁 Project Structure

package_conflict_resolver/
├── .env                  # Environment variables (API Keys)
├── requirements.txt      # Dependencies
├── main.py               # CLI Entry Point
├── src/
│   ├── combined_server.py # Combined ADK Web UI + MCP Server
│   ├── config.py         # Configuration & Service Initialization
│   ├── tools.py          # Custom Tools (Search, Memory, Validation)
│   ├── agents.py         # Agent Definitions & Workflow
│   └── utils.py          # Logging & Helpers
└── ...
├── src/
│   ├── combined_server.py # Combined ADK Web UI + MCP Server
│   ├── config.py         # Configuration & Service Initialization
│   ├── tools.py          # Custom Tools (Search, Memory, Validation)
│   ├── agents.py         # Agent Definitions & Workflow
│   └── utils.py          # Logging & Helpers
└── ...

🏗️ Architecture

High-level architecture of the Package Conflict Identifier Agent

🚀 Quick Start

1. Clone & Install

git clone <your-repo-url>
cd package_conflict_resolver
pip install -r requirements.txt

2. Configure Environment

Create a .env file with your API keys:

GOOGLE_API_KEY=your_gemini_key
OPENROUTER_API_KEY=your_openrouter_key
PINECONE_API_KEY=your_pinecone_key
DATABASE_URL=sqlite+aiosqlite:///legacy_solver.db

3. Run the Agent

Option A: Combined Server (Web UI + MCP) - Recommended This runs both the ADK Developer UI and the MCP Server on the same port.

python -m src.combined_server

• Web UI: http://localhost:7860/dev-ui/
• MCP SSE Endpoint: http://localhost:7860/mcp/sse

Option B: CLI Mode Option A: Combined Server (Web UI + MCP) - Recommended This runs both the ADK Developer UI and the MCP Server on the same port.

python -m src.combined_server

• Web UI: http://localhost:7860/dev-ui/
• MCP SSE Endpoint: http://localhost:7860/mcp/sse

Option B: CLI Mode

python main.py

🔌 MCP Server Integration

This agent is deployed as an MCP server, allowing you to use its dependency solving capabilities directly from other AI tools.

Public Endpoint (Hugging Face Spaces)

• SSE URL: https://yash030-ai-package-doctor.hf.space/mcp/sse

Usage with Claude Desktop

Add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "AI Package Doctor": {
      "command": "",
      "url": "https://yash030-ai-package-doctor.hf.space/mcp/sse",
      "transport": "sse"
    }
  }
}

Once connected, you can ask Claude:

"I have a conflict between numpy 1.26.4 and tensorflow 2.10.0. Can you help me fix it?"

Claude will use the solve_dependency_issue tool to analyze the problem using the full power of the agentic workflow.

🔌 MCP Server Integration

This agent is deployed as an MCP server, allowing you to use its dependency solving capabilities directly from other AI tools.

Public Endpoint (Hugging Face Spaces)

• SSE URL: https://yash030-ai-package-doctor.hf.space/mcp/sse

Usage with Claude Desktop

Add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "AI Package Doctor": {
      "command": "",
      "url": "https://yash030-ai-package-doctor.hf.space/mcp/sse",
      "transport": "sse"
    }
  }
}

Once connected, you can ask Claude:

"I have a conflict between numpy 1.26.4 and tensorflow 2.10.0. Can you help me fix it?"

Claude will use the solve_dependency_issue tool to analyze the problem using the full power of the agentic workflow.

🤖 Agent Workflow

1. Query Creator Agent:

   • Analyzes the user's error message.
   • Uses retrieve_memory to check if this issue was solved before.
   • Generates search queries for the research team.

2. Context Search Agent:

   • Specifically looks for relevant context in the project's long-term memory.

3. Parallel Research Team:

   • Docs Search Agent: Searches official documentation.
   • Community Search Agent: Searches StackOverflow/GitHub.
   • Web Crawl Agent: Deep crawls specific documentation pages using Firecrawl.

4. Code Surgeon:

   • Synthesizes all gathered information.
   • Generates a corrected requirements.txt or solution plan.

☁️ Deployment & Persistence

Hugging Face Spaces

The project is configured to run on Hugging Face Spaces (Docker SDK).

• Dockerfile: Included in the root.
• Port: Exposes port 7860.
• Storage: Uses /data directory for persistent storage (if configured with persistent volume).

Hugging Face Spaces

The project is configured to run on Hugging Face Spaces (Docker SDK).

• Dockerfile: Included in the root.
• Port: Exposes port 7860.
• Storage: Uses /data directory for persistent storage (if configured with persistent volume).

Database

For production (e.g., Hugging Face Spaces), use a PostgreSQL database:

DATABASE_URL=postgresql+asyncpg://user:password@host/dbname

Long-Term Memory (Pinecone)

To enable persistent memory across restarts:

1. Get a free API key from Pinecone.io.
2. Set PINECONE_API_KEY in .env.
3. The agent will automatically index and retrieve past sessions.

📝 License

MIT License.

🙏 Credits

Built with:

• Google Agent Development Kit (ADK)
• Google Gemini
• OpenRouter
• Pinecone
• Model Context Protocol
• Model Context Protocol