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| title: EurekaAgent | |
| emoji: 🤖 | |
| colorFrom: blue | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 5.43.1 | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| # Eureka Agent | |
| An AI-powered research automation system that can execute Python code, analyze data, and generate insights through an interactive Jupyter-like interface. | |
| <img width="1936" height="855" alt="Screenshot 2025-08-22 at 11 45 12 PM" src="https://github.com/user-attachments/assets/8d4ea793-4027-4aa3-8d6f-cbebbbd6e0c2" /> | |
| ## 🎯 What it does | |
| Eureka Agent automates research workflows by: | |
| - **Executing Python code** in a secure containerized environment | |
| - **Analyzing data** with full context awareness across conversations | |
| - **Generating visualizations** and interactive outputs | |
| - **Iterative development** - builds upon previous code and results | |
| - **Error recovery** - learns from execution failures and improves | |
| ## ⚡ Key Features | |
| - **Stateful Jupyter Environment**: Variables and imports persist across all code executions | |
| - **GPU/CPU Support**: Configurable hardware (CPU, T4, L4, A100, H100) | |
| - **Interactive Development**: Build complex solutions incrementally | |
| - **Rich Output Support**: Plots, tables, HTML, and multimedia content | |
| - **Error Handling**: Intelligent error recovery and debugging assistance | |
| - **File Upload**: Process your own datasets and documents | |
| ## 🚀 Quick Start | |
| ### Prerequisites | |
| - Python 3.8+ | |
| - Modal account (for containerized execution) | |
| - OpenAI API key or compatible LLM provider | |
| ### Installation | |
| 1. Clone the repository: | |
| ```bash | |
| git clone https://github.com/adithya-s-k/EurekaAgent | |
| cd EurekaAgent | |
| ``` | |
| 2. Install dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 3. Set up environment variables: | |
| ```bash | |
| export OPENAI_API_KEY="your-api-key" | |
| export MODAL_TOKEN_ID="your-modal-token-id" | |
| export MODAL_TOKEN_SECRET="your-modal-token-secret" | |
| ``` | |
| ### Running the Application | |
| ```bash | |
| python app.py | |
| ``` | |
| The application will launch a Gradio interface accessible via your web browser. | |
| ## 🔧 Configuration | |
| ### Environment Variables | |
| | Variable | Description | Required | Format/Example | | |
| | ---------------------------- | ----------------------------- | -------- | ------------------------------- | | |
| | `MODAL_TOKEN_ID` | Modal token ID | Yes | `ak-...` | | |
| | `MODAL_TOKEN_SECRET` | Modal token secret | Yes | `as-...` | | |
| | `PROVIDER_API_KEY` | AI Provider API key | Yes\* | `sk-...`, `gsk_...`, `csk-...` | | |
| | `PROVIDER_API_ENDPOINT` | AI Provider API endpoint | Yes\* | `https://api.anthropic.com/v1/` | | |
| | `MODEL_NAME` | Model to use | Yes\* | `claude-sonnet-4-20250514` | | |
| | `HF_TOKEN` | Hugging Face token (optional) | No | `hf_...` | | |
| | `TAVILY_API_KEY` | Tavily API key for web search | No | `tvly-...` | | |
| | `PHOENIX_API_KEY` | Phoenix tracing API key | No | - | | |
| | `PHOENIX_COLLECTOR_ENDPOINT` | Phoenix collector endpoint | No | - | | |
| | `ENVIRONMENT` | Environment mode | No | `dev`/`prod` | | |
| \*At least one complete AI provider configuration must be provided | |
| **Legacy OpenAI Support:** | |
| | Variable | Description | Required | | |
| | ----------------------- | ----------------------------- | -------- | | |
| | `OPENAI_API_KEY` | OpenAI API key | No | | |
| | `AZURE_OPENAI_ENDPOINT` | Azure OpenAI endpoint | No | | |
| | `AZURE_OPENAI_API_KEY` | Azure OpenAI API key | No | | |
| ### Hardware Options | |
| - **CPU Only**: Free, suitable for basic tasks | |
| - **NVIDIA T4**: Low-cost GPU for small models | |
| - **NVIDIA L4**: Mid-range GPU for better performance | |
| - **NVIDIA A100**: High-end GPU for large models (40GB/80GB variants) | |
| - **NVIDIA H100**: Latest flagship GPU for maximum performance | |
| ## 💡 Usage Examples | |
| ### Basic Data Analysis | |
| ``` | |
| "Analyze the uploaded CSV file and create visualizations showing key trends" | |
| ``` | |
| ### Machine Learning | |
| ``` | |
| "Train a neural network to classify the iris dataset and evaluate its performance" | |
| ``` | |
| ### Research Tasks | |
| ``` | |
| "Download stock price data for the last year and perform technical analysis" | |
| ``` | |
| ## 🏗️ Architecture | |
| - **Frontend**: Gradio web interface with real-time status updates | |
| - **Backend**: Python application with multi-provider AI integration | |
| - **Execution Environment**: Modal containerized sandboxes with GPU support | |
| - **Code Execution**: Persistent Jupyter-like stateful environment | |
| - **Session Management**: Comprehensive session state tracking with Phoenix tracing | |
| - **Storage**: File-based session persistence with notebook compatibility | |
| - **Web Search**: Integrated Tavily search for current information | |
| - **Hardware Support**: CPU, T4, L4, A100, H100 configurations | |
| ## 📁 Project Structure | |
| ``` | |
| EurekaAgent/ | |
| ├── app.py # Main Gradio application with API key management | |
| ├── jupyter_handler.py # Jupyter notebook management and rendering | |
| ├── jupyter_agent.py # Utility functions, execution logic, and session management | |
| ├── modal_sandbox.py # Modal sandbox configuration with GPU support | |
| ├── system_prompt.txt # System prompt for the AI agent | |
| ├── requirements.txt # Python dependencies | |
| └── temp/ # Temporary files, notebooks, and session states | |
| ├── <session_id>/ | |
| │ ├── session_state.json # Complete session state and history | |
| │ └── jupyter-agent.ipynb # Legacy notebook file for UI compatibility | |
| └── jupyter-agent.ipynb # Default notebook template | |
| ``` | |
| ## 🤝 Contributing | |
| This project is a fork of [Jupyter Agent 2](https://huggingface.co/spaces/lvwerra/jupyter-agent-2) by Hugging Face. Contributions are welcome! | |
| ## 📄 License | |
| See [LICENSE](./LICENSE) file for details. | |