# AbstractFlow
**Diagram-Based AI Workflow Generation**
> 🚧 **Coming Soon** - This project is currently in early development. We're reserving the PyPI name for the upcoming release.
AbstractFlow is an innovative Python library that enables visual, diagram-based creation and execution of AI workflows. Built on top of [AbstractCore](https://github.com/lpalbou/AbstractCore), it provides an intuitive interface for designing complex AI pipelines through interactive diagrams.
## 🎯 Vision
AbstractFlow aims to democratize AI workflow creation by providing:
- **Visual Workflow Design**: Create AI workflows using intuitive drag-and-drop diagrams
- **Multi-Provider Support**: Leverage any LLM provider through AbstractCore's unified interface
- **Real-time Execution**: Watch your workflows execute in real-time with live feedback
- **Collaborative Development**: Share and collaborate on workflow designs
- **Production Ready**: Deploy workflows to production with built-in monitoring and scaling
## 🚀 Planned Features
### Core Capabilities
- **Diagram Editor**: Web-based visual editor for workflow creation
- **Node Library**: Pre-built nodes for common AI operations (text generation, analysis, transformation)
- **Custom Nodes**: Create custom nodes with your own logic and AI models
- **Flow Control**: Conditional branching, loops, and parallel execution
- **Data Transformation**: Built-in data processing and transformation capabilities
### AI Integration
- **Universal LLM Support**: Works with OpenAI, Anthropic, Ollama, and all AbstractCore providers
- **Tool Calling**: Seamless integration with external APIs and services
- **Structured Output**: Type-safe data flow between workflow nodes
- **Streaming Support**: Real-time processing for interactive applications
### Deployment & Monitoring
- **Cloud Deployment**: One-click deployment to major cloud platforms
- **Monitoring Dashboard**: Real-time workflow execution monitoring
- **Version Control**: Git-based workflow versioning and collaboration
- **API Generation**: Automatic REST API generation from workflows
## 🏗️ Architecture
AbstractFlow is built on a robust foundation:
```
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Diagram UI │ │ Workflow Engine │ │ AbstractCore │
│ │────│ │────│ │
│ Visual Editor │ │ Execution Logic │ │ LLM Providers │
└─────────────────┘ └─────────────────┘ └─────────────────┘
```
- **Frontend**: React-based diagram editor with real-time collaboration
- **Backend**: Python workflow execution engine with FastAPI
- **AI Layer**: AbstractCore for unified LLM provider access
- **Storage**: Workflow definitions, execution history, and metadata
## 🎨 Use Cases
### Business Process Automation
- Customer support ticket routing and response generation
- Document analysis and summarization pipelines
- Content creation and review workflows
### Data Processing
- Multi-step data analysis with AI insights
- Automated report generation from raw data
- Real-time data enrichment and validation
### Creative Workflows
- Multi-stage content creation (research → draft → review → publish)
- Interactive storytelling and narrative generation
- Collaborative writing and editing processes
### Research & Development
- Hypothesis generation and testing workflows
- Literature review and synthesis automation
- Experimental design and analysis pipelines
## 🛠️ Technology Stack
- **Core**: Python 3.8+ with AsyncIO support
- **AI Integration**: [AbstractCore](https://github.com/lpalbou/AbstractCore) for LLM provider abstraction
- **Web Framework**: FastAPI for high-performance API server
- **Frontend**: React with TypeScript for the diagram editor
- **Database**: PostgreSQL for workflow storage, Redis for caching
- **Deployment**: Docker containers with Kubernetes support
## 📦 Installation (Coming Soon)
```bash
# Install AbstractFlow
pip install abstractflow
# Or with all optional dependencies
pip install abstractflow[all]
# Development installation
pip install abstractflow[dev]
```
## 🚀 Quick Start (Preview)
```python
from abstractflow import WorkflowBuilder, TextNode, LLMNode
# Create a simple workflow
workflow = WorkflowBuilder()
# Add nodes
input_node = workflow.add_node(TextNode("user_input"))
llm_node = workflow.add_node(LLMNode(
provider="openai",
model="gpt-4o-mini",
prompt="Analyze this text: {user_input}"
))
output_node = workflow.add_node(TextNode("analysis_result"))
# Connect nodes
workflow.connect(input_node, llm_node)
workflow.connect(llm_node, output_node)
# Execute workflow
result = await workflow.execute({
"user_input": "The future of AI is bright and full of possibilities."
})
print(result["analysis_result"])
```
## 🎯 Roadmap
### Phase 1: Foundation (Q1 2025)
- [ ] Core workflow engine
- [ ] Basic node types (LLM, Transform, Condition)
- [ ] CLI interface for workflow execution
- [ ] AbstractCore integration
### Phase 2: Visual Editor (Q2 2025)
- [ ] Web-based diagram editor
- [ ] Real-time collaboration features
- [ ] Workflow templates and examples
- [ ] Import/export functionality
### Phase 3: Advanced Features (Q3 2025)
- [ ] Custom node development SDK
- [ ] Advanced flow control (loops, parallel execution)
- [ ] Monitoring and analytics dashboard
- [ ] Cloud deployment integration
### Phase 4: Enterprise (Q4 2025)
- [ ] Enterprise security features
- [ ] Advanced monitoring and alerting
- [ ] Multi-tenant support
- [ ] Professional services and support
## 🤝 Contributing
We welcome contributions from the community! Once development begins, you'll be able to:
- Report bugs and request features
- Submit pull requests for improvements
- Create and share workflow templates
- Contribute to documentation
## 📄 License
AbstractFlow will be released under the MIT License, ensuring it remains free and open-source for all users.
## 🔗 Related Projects
- **[AbstractCore](https://github.com/lpalbou/AbstractCore)**: The unified LLM interface powering AbstractFlow
- **[AbstractCore Documentation](http://www.abstractcore.ai/)**: Comprehensive guides and API reference
## 📞 Contact
For early access, partnerships, or questions about AbstractFlow:
- **GitHub**: [Issues and Discussions](https://github.com/lpalbou/AbstractFlow) (coming soon)
- **Email**: Contact through AbstractCore channels
- **Website**: [www.abstractflow.ai](http://www.abstractflow.ai) (coming soon)
---
**AbstractFlow** - Visualize, Create, Execute. The future of AI workflow development is here.
> Built with ❤️ on top of [AbstractCore](https://github.com/lpalbou/AbstractCore)
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"description": "# AbstractFlow\n\n**Diagram-Based AI Workflow Generation**\n\n> \ud83d\udea7 **Coming Soon** - This project is currently in early development. We're reserving the PyPI name for the upcoming release.\n\nAbstractFlow is an innovative Python library that enables visual, diagram-based creation and execution of AI workflows. Built on top of [AbstractCore](https://github.com/lpalbou/AbstractCore), it provides an intuitive interface for designing complex AI pipelines through interactive diagrams.\n\n## \ud83c\udfaf Vision\n\nAbstractFlow aims to democratize AI workflow creation by providing:\n\n- **Visual Workflow Design**: Create AI workflows using intuitive drag-and-drop diagrams\n- **Multi-Provider Support**: Leverage any LLM provider through AbstractCore's unified interface\n- **Real-time Execution**: Watch your workflows execute in real-time with live feedback\n- **Collaborative Development**: Share and collaborate on workflow designs\n- **Production Ready**: Deploy workflows to production with built-in monitoring and scaling\n\n## \ud83d\ude80 Planned Features\n\n### Core Capabilities\n- **Diagram Editor**: Web-based visual editor for workflow creation\n- **Node Library**: Pre-built nodes for common AI operations (text generation, analysis, transformation)\n- **Custom Nodes**: Create custom nodes with your own logic and AI models\n- **Flow Control**: Conditional branching, loops, and parallel execution\n- **Data Transformation**: Built-in data processing and transformation capabilities\n\n### AI Integration\n- **Universal LLM Support**: Works with OpenAI, Anthropic, Ollama, and all AbstractCore providers\n- **Tool Calling**: Seamless integration with external APIs and services\n- **Structured Output**: Type-safe data flow between workflow nodes\n- **Streaming Support**: Real-time processing for interactive applications\n\n### Deployment & Monitoring\n- **Cloud Deployment**: One-click deployment to major cloud platforms\n- **Monitoring Dashboard**: Real-time workflow execution monitoring\n- **Version Control**: Git-based workflow versioning and collaboration\n- **API Generation**: Automatic REST API generation from workflows\n\n## \ud83c\udfd7\ufe0f Architecture\n\nAbstractFlow is built on a robust foundation:\n\n```\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Diagram UI \u2502 \u2502 Workflow Engine \u2502 \u2502 AbstractCore \u2502\n\u2502 \u2502\u2500\u2500\u2500\u2500\u2502 \u2502\u2500\u2500\u2500\u2500\u2502 \u2502\n\u2502 Visual Editor \u2502 \u2502 Execution Logic \u2502 \u2502 LLM Providers \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n```\n\n- **Frontend**: React-based diagram editor with real-time collaboration\n- **Backend**: Python workflow execution engine with FastAPI\n- **AI Layer**: AbstractCore for unified LLM provider access\n- **Storage**: Workflow definitions, execution history, and metadata\n\n## \ud83c\udfa8 Use Cases\n\n### Business Process Automation\n- Customer support ticket routing and response generation\n- Document analysis and summarization pipelines\n- Content creation and review workflows\n\n### Data Processing\n- Multi-step data analysis with AI insights\n- Automated report generation from raw data\n- Real-time data enrichment and validation\n\n### Creative Workflows\n- Multi-stage content creation (research \u2192 draft \u2192 review \u2192 publish)\n- Interactive storytelling and narrative generation\n- Collaborative writing and editing processes\n\n### Research & Development\n- Hypothesis generation and testing workflows\n- Literature review and synthesis automation\n- Experimental design and analysis pipelines\n\n## \ud83d\udee0\ufe0f Technology Stack\n\n- **Core**: Python 3.8+ with AsyncIO support\n- **AI Integration**: [AbstractCore](https://github.com/lpalbou/AbstractCore) for LLM provider abstraction\n- **Web Framework**: FastAPI for high-performance API server\n- **Frontend**: React with TypeScript for the diagram editor\n- **Database**: PostgreSQL for workflow storage, Redis for caching\n- **Deployment**: Docker containers with Kubernetes support\n\n## \ud83d\udce6 Installation (Coming Soon)\n\n```bash\n# Install AbstractFlow\npip install abstractflow\n\n# Or with all optional dependencies\npip install abstractflow[all]\n\n# Development installation\npip install abstractflow[dev]\n```\n\n## \ud83d\ude80 Quick Start (Preview)\n\n```python\nfrom abstractflow import WorkflowBuilder, TextNode, LLMNode\n\n# Create a simple workflow\nworkflow = WorkflowBuilder()\n\n# Add nodes\ninput_node = workflow.add_node(TextNode(\"user_input\"))\nllm_node = workflow.add_node(LLMNode(\n 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and analytics dashboard\n- [ ] Cloud deployment integration\n\n### Phase 4: Enterprise (Q4 2025)\n- [ ] Enterprise security features\n- [ ] Advanced monitoring and alerting\n- [ ] Multi-tenant support\n- [ ] Professional services and support\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions from the community! Once development begins, you'll be able to:\n\n- Report bugs and request features\n- Submit pull requests for improvements\n- Create and share workflow templates\n- Contribute to documentation\n\n## \ud83d\udcc4 License\n\nAbstractFlow will be released under the MIT License, ensuring it remains free and open-source for all users.\n\n## \ud83d\udd17 Related Projects\n\n- **[AbstractCore](https://github.com/lpalbou/AbstractCore)**: The unified LLM interface powering AbstractFlow\n- **[AbstractCore Documentation](http://www.abstractcore.ai/)**: Comprehensive guides and API reference\n\n## \ud83d\udcde Contact\n\nFor early access, partnerships, or questions about AbstractFlow:\n\n- **GitHub**: [Issues and Discussions](https://github.com/lpalbou/AbstractFlow) (coming soon)\n- **Email**: Contact through AbstractCore channels\n- **Website**: [www.abstractflow.ai](http://www.abstractflow.ai) (coming soon)\n\n---\n\n**AbstractFlow** - Visualize, Create, Execute. 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