mcpField
๐Ÿค–
๐Ÿค– Data Agents Platform

๐Ÿค– Data Agents Platform

Local
hottechstack.comData & Analytics25Updated 81w ago

About ๐Ÿค– Data Agents Platform

Data Agents Platform is an agentic AI platform for data engineering, built by data engineers. It helps non-data staff navigate organizational data infrastructure, with orchestration handled through n8n.

Key features

  1. 01Multi-agent collaboration with specialized data roles
  2. 02Support for OpenAI, Claude, Gemini and Ollama
  3. 03n8n integration for agent orchestration
  4. 04Strategy-based handling of data engineering tasks
  5. 05Docker Compose deployment

Use cases

  1. 01Non-data staff submitting data requests for prepared results
  2. 02Private deployments using a local Ollama model
  3. 03Customizing agent conversation flows with n8n workflows

Configuration

Local server config is the same for every client. Copy it into your clientโ€™s MCP config file.

No install method listed yet

We havenโ€™t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

๐Ÿค– Data Agents Platform

Alpha Status Next.js 14 TypeScript UI

https://github.com/user-attachments/assets/f591bc23-3a19-43eb-9c92-e4b5bb3ba57f

๐Ÿ’ฌ 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.

โœจ Features

  • ๐Ÿค– Multi-agent collaboration - Engage with specialized data engineering agents
  • ๐Ÿ”„ Multiple backend support - Connect to OpenAI, Claude, Gemini or Ollama for private deployments
  • ๐Ÿ”— n8n integration - Use n8n workflows for agent orchestration
  • ๐ŸŽฏ Strategy-based approach - Different strategies for various data engineering tasks
  • ๐ŸŒ™ Modern dark UI - Beautiful, responsive interface inspired by LobeChat
  • ๐Ÿš€ Docker ready - Easy deployment with Docker Compose

๐Ÿš€ 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
๐Ÿ”ง Setup Steps
  1. Once the containers are running, go to n8n at http://localhost:5678

  2. Upload the workflow from the agents/n8n/conversations directory

  3. 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
  4. Visit http://localhost:3000 and start interacting with your agents!

๐Ÿงฉ 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.

๐Ÿง  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

๐ŸŽฏ Strategy Types

๐Ÿ”ฎ Upcoming Features

We're actively working on the following enhancements:

  • ๐ŸŽฏ Strategy Types - More Strategy Types backend for debate and Continuous Discussion
  • ๐Ÿ“ Code Execution - Run and test code snippets directly in the chat
  • ๐Ÿ”„ Workflow Builder - Create custom agent workflows with a visual editor
  • ๐ŸŒ Multi-source Data Connectors - Connect to various data sources
  • ๐Ÿ—๏ธ Data Engineering Specific MCP Server - Optimized for data engineering workflows
  • ๐Ÿง  Deep Thinking for Data Engineering - Enhanced reasoning capabilities for complex data problems
  • ๐Ÿ’พ Database with histories - Persistent conversation storage with vectordbs for semantic search and caching

๐Ÿงฉ 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

๐Ÿค Contributing

Contributions are always welcome! Here's how you can help:

  1. Fork the repository
  2. Create a new branch: git checkout -b feature/amazing-feature
  3. Make your changes and commit them: git commit -m 'Add amazing feature'
  4. Push to the branch: git push origin feature/amazing-feature
  5. Open a pull request
๐Ÿ› 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.)

๐Ÿ“œ License

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

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