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

datagents
Los agentes de datos son asistentes inteligentes construidos por ingenieros de datos para ayudar a los profesionales que no son de datos a navegar por la infraestructura de datos de la organización
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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