Enterprise-Grade Intelligent Multi-Agent Data Analytics Platform Transform natural language into actionable data insights with collaborative AI agents, real-time streaming, and interactive visualizations.
- Overview
- Live Demo & Screenshots
- Quick Start
- Core Features
- Architecture
- Technology Stack
- Usage
- Contributing
- License
- Author
AutoInsight AI democratizes data analytics by enabling anyone to query databases, analyze datasets, and generate visualizations with natural language conversations.
- Multi-agent orchestration with AutoGen
- Real-time Python execution in Docker
- Natural language → SQL conversion
- Interactive visualizations with AI-driven charting
- FastAPI REST API + Streamlit web apps + HTML5 frontend
- 🗄️ Database Analytics:
- 📊 Data Analysis:
git clone https://github.com/akshaykumarbedre/AutoInsight-AI.git
cd AutoInsight-AI
python -m venv venv
source venv/bin/activate # (Windows: venv\Scripts\activate)
pip install -r requirements.txt
cp .env.example .envUpdate .env with your OpenAI API key.
Run:
python app_fastapi.pyVisit: http://localhost:8080
docker build -t autoinsight-ai .
docker run -p 5001:5001 --env-file .env autoinsight-aiVisit: http://localhost:5001
- 🤖 Multi-Agent Teams – SQL, visualization, code execution, and human-in-the-loop agents
- 💬 Natural Language Queries – Convert plain English into SQL and insights
- 📊 Intelligent Visualizations – Auto-generate bar, line, scatter, pie, and histogram charts
- 🐳 Secure Execution – Docker-isolated Python runtime for safe code execution
- 🔄 Real-Time Streaming – Asynchronous communication with progress tracking
- 🌐 Multiple Interfaces – FastAPI APIs, Streamlit apps, modern web UI
AutoInsight-AI/
├── 🌐 Frontend & Templates
│ ├── templates/ # HTML5 web interfaces
│ │ ├── index.html # Professional homepage & portfolio
│ │ ├── dataanalyst.html # Data analysis interface
│ │ ├── database.html # Database analytics interface
│ │ └── visualization.html # Visualization studio
│ └── streamlit/ # Streamlit applications
│ ├── Dataabase_agent_streamlit.py # Database UI
│ └── Data_anaylis_agent_streamlit.py # Analysis UI
├── 🚀 Core Application
│ ├── app_fastapi.py # Main FastAPI server with all APIs
│ ├── requirements.txt # Production dependencies
│ ├── Dockerfile # Container deployment config
│ └── .env.example # Environment configuration template
├── 🤖 Agent System
│ ├── agent/ # Specialized AI agents
│ │ ├── database_agent.py # SQL query generation agent
│ │ ├── visualization_agent.py # Chart creation agent
│ │ ├── dataanalsys_agent.py # Data analysis expert agent
│ │ ├── code_excuter_agent.py # Docker code execution agent
│ │ └── human_agent.py # Human-in-the-loop agent
│ └── teams/ # Multi-agent orchestration
│ └── team_manager.py # RoundRobinGroupChat coordination
├── 🛠️ Tools & Execution
│ ├── tool/ # Specialized tool implementations
│ │ ├── plotting.py # 5+ visualization tools (Matplotlib/Plotly)
│ │ ├── docker_executer.py # Secure containerized code execution
│ │ └── sql_tool_kit.py # LangChain SQL database tools
│ └── util/ # Utility functions & streaming
│ ├── stream_handler.py # Real-time conversation streaming
│ ├── display_helper.py # UI display utilities
│ └── stream_data_anaylisi.py # Data analysis streaming handlers
├── 🗄️ Data & Configuration
│ ├── config/ # Environment & model configuration
│ │ └── settings.py # OpenAI client & model settings
│ ├── database/ # Database management
│ │ ├── db_manager.py # SQLite connection & toolkit
│ │ └── ecommerce.db # Sample e-commerce database
│ ├── coding/ # User uploaded data files
│ │ ├── *.csv, *.json, *.xlsx # Analysis datasets
│ │ └── *.pkl # Trained ML models
│ └── plots/ # Generated visualizations
│ └── *.png # Chart exports & images
├── 🧪 Development & Notebooks
│ ├── notebook/ # Jupyter development notebooks
│ │ ├── *.ipynb # Experimental agent workflows
│ │ └── demo.py # Interactive demonstrations
│ └── __pycache__/ # Compiled Python modules
└── 📄 Documentation
├── README.md # Comprehensive documentation
├── autoinsight_server.log # Application logging
└── .gitignore # Version control configuration<!-- Replace ASCII with a diagram image -->
- AI & Agents: AutoGen, LangChain, OpenAI GPT-4o-mini
- Backend: FastAPI, WebSockets, Uvicorn
- Frontend: Streamlit, HTML5, CSS3, JS
- Visualization: Pandas, Matplotlib, Plotly, Seaborn
- Infrastructure: Docker, SQLite, SQLAlchemy
Once running, access the platform at http://localhost:8080:
- 🗄️ Database Analytics → Query databases in natural language
- 📊 Data Analysis → Upload CSV/Excel/JSON and run Python analysis
- 📈 Visualization Studio → Generate charts and dashboards
Example queries:
"Show top 10 customers by total order value"
"Create a correlation matrix heatmap"
"Predict sales trends with a regression model"
- Fork the repo
- Create a branch:
git checkout -b feature/my-feature - Commit changes:
git commit -m 'Add my feature' - Push:
git push origin feature/my-feature - Open a Pull Request
👉 Follow PEP 8, add docstrings, and include tests.
This project is licensed under the MIT License - see LICENSE.
Akshay Kumar BM Senior Software Engineer & AI/ML Specialist
⭐ If you find this project useful, don’t forget to star the repo!
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