I craft unique cereal names, stories, and ridiculously cute Cereal Baby images.

Datenagenten
Datenagenten sind intelligente Assistenten, die von Dateningenieuren erstellt wurden, um Nichtdatenfachleuten zu helfen, die Dateninfrastruktur des Unternehmens zu navigieren
3 years
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🤖 Data Agents Platform
https://github.com/user-attachments/assets/f591bc23-3a19-43eb-9c92-e4b5bb3ba57f
💬 Data Agents, Really!
Data Agent is an agentic AI harnessing GenAI to automate and streamline data engineering workflows. By delivering complete, well-prepared data requests, it saves time and reduces bottlenecks across teams.
✨ Features
- 🤖 Multi-agent collaboration - Engage with specialized data engineering agents
- 🔄 Multiple backend support - Connect to OpenAI, Claude, Gemini or Ollama for private deployments
- 🔗 n8n integration - Use n8n workflows for agent orchestration
- 🎯 Strategy-based approach - Different strategies for various data engineering tasks
- 🌙 Modern dark UI - Beautiful, responsive interface inspired by LobeChat
- 🚀 Docker ready - Easy deployment with Docker Compose
🚀 Quick Start
The fastest way to get started is using Docker Compose:
# Clone the repository
git clone https://github.com/HotTechStack/dataagents.git
cd dataagents
# Start the application
docker-compose up -d
🔧 Setup Steps
-
Once the containers are running, go to n8n at http://localhost:5678
-
Upload the workflow from the
agents/n8n/conversations
directory -
Configure your API keys:
- In Docker Compose: update OpenAI/Claude/Gemini key
- In n8n workflow: click on OpenAI/Claude/Gemini model block and add your key
- See n8n documentation for more details
-
Visit http://localhost:3000 and start interacting with your agents!
🧩 Running Locally
If you prefer running the application without Docker:
# Clone the repository
git clone https://github.com/HotTechStack/dataagents.git
cd dataagents
# Install dependencies
pnpm install
# Start the development server
pnpm run dev
You can still use your own hosted n8n instance or the Docker integrated version while running the frontend locally.
🧠 Available Agents
- Data Architect - Designs data infrastructure and systems
- Pipeline Engineer - Builds efficient data pipelines
- Data Analyst - Analyzes and interprets complex data
- Data Scientist - Applies statistical models and machine learning
- Governance Specialist - Ensures data quality and compliance
🎯 Strategy Types
🔮 Upcoming Features
We're actively working on the following enhancements:
- 🎯 Strategy Types - More Strategy Types backend for debate and Continuous Discussion
- 📝 Code Execution - Run and test code snippets directly in the chat
- 🔄 Workflow Builder - Create custom agent workflows with a visual editor
- 🌐 Multi-source Data Connectors - Connect to various data sources
- 🏗️ Data Engineering Specific MCP Server - Optimized for data engineering workflows
- 🧠 Deep Thinking for Data Engineering - Enhanced reasoning capabilities for complex data problems
- 💾 Database with histories - Persistent conversation storage with vectordbs for semantic search and caching
🧩 Architecture
The application is built with a modern stack:
- Frontend: Next.js 14 with App Router, TypeScript, Tailwind CSS, Shadcn UI
- State Management: Zustand for global state
- Orchestration: n8n for workflow management
- AI Integration: OpenAI, Claude, Gemini and Ollama support
🤝 Contributing
Contributions are always welcome! Here's how you can help:
- Fork the repository
- Create a new branch:
git checkout -b feature/amazing-feature
- Make your changes and commit them:
git commit -m 'Add amazing feature'
- Push to the branch:
git push origin feature/amazing-feature
- Open a pull request
🐛 Bug Reports
If you encounter any issues, please help us improve by creating a bug report.
Include as much information as possible:
- Steps to reproduce
- Expected behavior
- Actual behavior
- Screenshots if applicable
- Environment details (browser, OS, etc.)
📜 License
This project is licensed under the MIT License - see the LICENSE file for details.
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