RagNet: Your AI DevRel for Open Source Communities

Ragnet is an AI-powered DevRel that automatically resolve developer support queries across community channels like Discord, Slack, GitHub, and Telegram specifically for your open source community.

Most open-source dev-facing startups struggle with scaling support: time zone delays, scattered knowledge, and growing community questions outpacing the team. Companies hire DevRels and Support Engineers to bridge this, but it's expensive and doesn’t scale well.

Ragnet solves this by ingesting your docs, codebase, GitHub issues, architecture diagrams, and past chat history to build an AI agent that answers dev queries instantly, with full source citations. It learns continuously from new interactions and works 24/7! like a fully trained DevRel who knows everything your team ever shipped, but without the human overhead. It’s baked right inside where your community is there’s 0 friction for them to use it.

Key Features

• Multi-Source Integration

  • Discord communities
  • GitHub repositories
  • Documentation websites

• Intelligent Query Processing

  • RAG-based contextual understanding
  • Vector similarity search
  • Conversation history awareness
  • Source citations

• Community Engagement

  • Discord bot integration
  • Threaded conversations
  • Real-time responses
  • No auth required
  • Stays where your community is

• Analytics & Insights

  • Query patterns
  • User engagement metrics
  • Documentation coverage analysis

Local Setup

Prerequisites

• Node.js 20+
• Docker
• Discord Bot Token (for Discord integration)
• OpenAI API Key (for AI processing)

Docker Compose Deployment

1. Clone the repository

git clone https://github.com/ragnet-in/infra.git
cd ragnet

2. Configure environment variables in docker-compose.yml

3. Start the server

docker compose up -d

Now access the application at http://localhost:3000

🛣️ Roadmap

Phase 1: Core Features (Current)

• Discord integration
• GitHub Repo ingestion
• Basic RAG implementation
• Conversation threading

Phase 2: Lens Integration

• Reputation system
• Content verification
• Token-gated features

Phase 3: Enhanced Features

• Webpage crawling and ingestion
• Advanced analytics dashboard
• Custom training data support
• Multi-language support
• RAG Accuracy improvement with LLM

Phase 4: Enterprise Features

• Custom model fine-tuning
• Team collaboration tools
• API rate limiting
• Custom deployment options

🛠️ Technical Stack

• Backend: Node.js, Express
• Database: PostgreSQL, pgvector
• AI/ML: OpenAI, Embeddings, Mastra
• Web3: Lens Protocol, Bonsai
• Infra: Docker, Docker Compose

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Built with ❤️ by the RagNet Team