{"id":37065959,"url":"https://github.com/datamarkin/agentui","last_synced_at":"2026-01-14T07:44:13.107Z","repository":{"id":318740909,"uuid":"1075733028","full_name":"datamarkin/agentui","owner":"datamarkin","description":null,"archived":false,"fork":false,"pushed_at":"2025-12-12T15:03:36.000Z","size":991,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-12-22T00:37:30.238Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/datamarkin.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-10-13T23:13:47.000Z","updated_at":"2025-12-12T15:03:40.000Z","dependencies_parsed_at":"2025-10-14T21:12:28.091Z","dependency_job_id":"58c7fb7b-16c6-42c1-93f0-cf79dec95dc7","html_url":"https://github.com/datamarkin/agentui","commit_stats":null,"previous_names":["datamarkin/agentui"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/datamarkin/agentui","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datamarkin%2Fagentui","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datamarkin%2Fagentui/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datamarkin%2Fagentui/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datamarkin%2Fagentui/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/datamarkin","download_url":"https://codeload.github.com/datamarkin/agentui/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datamarkin%2Fagentui/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28413479,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-14T05:26:33.345Z","status":"ssl_error","status_checked_at":"2026-01-14T05:21:57.251Z","response_time":107,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2026-01-14T07:44:12.392Z","updated_at":"2026-01-14T07:44:13.094Z","avatar_url":"https://github.com/datamarkin.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AgentUI\n\nVisual workflow builder for computer vision and AI. Create image processing pipelines by connecting tools in a drag-and-drop interface, then export and run them programmatically.\n\n**Part of the [Datamarkin](https://datamarkin.com) ecosystem** - Built on [PixelFlow](https://pixelflow.datamarkin.com) and [Mozo](https://mozo.datamarkin.com) for production-ready computer vision.\n\n## What It Is\n\nAgentUI is a web-first tool builder that lets you:\n- **Build visually**: Drag and drop tools on a canvas to create workflows\n- **Connect tools**: Wire outputs to inputs with type-safe connections\n- **Execute**: Run workflows in the browser and see results instantly\n- **Export**: Save workflows as JSON for version control and programmatic execution\n- **Integrate**: Use as a Python library in your own applications\n\nThink of it as a visual programming environment for computer vision tasks.\n\n## Quick Start\n\n```bash\n# Install\npip install agentui\n\n# Start the server\nagentui start\n\n# Open http://localhost:8000 in your browser\n```\n\nThat's it. The UI is already bundled - no separate build step needed.\n\n## What You Can Build\n\n### ML-Powered Tools\n- **Object Detection**: Detect objects using YOLOv8 or Detectron2 (80 COCO classes)\n- **Instance Segmentation**: Get pixel-level masks for detected objects\n- **Depth Estimation**: Generate depth maps from single images (Depth Anything)\n\n### Image Processing\n- **Transforms**: Rotate, flip, crop images with automatic detection coordinate updates\n- **Enhancement**: CLAHE enhancement, auto-contrast, gamma correction, image normalization\n- **Analysis**: Color analysis, quality metrics, dominant color extraction\n- **Blending**: Combine multiple images with alpha blending\n\n### Annotation \u0026 Privacy\n- **Draw Detections**: Bounding boxes, labels, masks, polygons\n- **Privacy Protection**: Blur or pixelate regions automatically\n- **Object Tracking**: Track objects across video frames\n- **Zone Analysis**: Monitor object presence in defined areas\n\n### Input/Output\n- **Load**: Images from files or base64 data\n- **Save**: Export processed images to disk\n- **Web Display**: Convert images to base64 for browser display\n\n## Usage\n\n### Web Interface\n\n1. **Add tools**: Drag tools from the left palette onto the canvas\n2. **Connect**: Click and drag from output ports to input ports\n3. **Configure**: Select a tool to edit its parameters in the right panel\n4. **Execute**: Click \"Run Workflow\" to process\n5. **View Results**: See outputs in the results panel\n6. **Export**: Save your workflow as JSON\n\n### Programmatic Usage\n\n```python\nfrom agentui import Workflow\n\n# Load a workflow created in the UI\nworkflow = Workflow.load('my_workflow.json')\n\n# Run with an image\nresult = workflow.run(image='test.jpg')\n\n# Access outputs\ndetections = result['detections']  # PixelFlow Detections object\nprint(f\"Found {len(detections)} objects\")\n\n# Batch processing (automatic)\nresult = workflow.run(image=['img1.jpg', 'img2.jpg', 'img3.jpg'])\nfor i, dets in enumerate(result['detections']):\n    print(f\"Image {i}: {len(dets)} objects\")\n```\n\n### Workflow Design Philosophy\n\n**AgentUI is designed for visual workflow creation:**\n- Create workflows using the drag-and-drop UI\n- Export as JSON for version control\n- Load and execute programmatically with the Python API\n\n**Why not build workflows in code?** The visual interface is the fastest way to prototype CV pipelines. The Python API focuses on *execution* (loading and running workflows), not construction. This separation keeps the codebase simple and the workflow format UI-native.\n\n## The Datamarkin Ecosystem\n\nAgentUI integrates two powerful libraries:\n\n- **[PixelFlow](https://pixelflow.datamarkin.com)**: Computer vision primitives (annotation, tracking, spatial analysis)\n- **[Mozo](https://mozo.datamarkin.com)**: Universal model serving (object detection, segmentation, depth estimation)\n\nThese libraries are maintained by the same team and designed to work together seamlessly.\n\n## Development\n\n### UI Development\n\nOnly needed if you're modifying the UI:\n\n```bash\ncd ui\nnpm install\nnpm run dev  # Development server with hot reload at http://localhost:5173\n\n# When done\nnpm run build  # Builds to ../agentui/static/\n```\n\n### Adding Custom Tools\n\nTools are Python classes that inherit from `Tool`:\n\n```python\nfrom agentui.core.tool import Tool, ToolOutput, Port, PortType\n\nclass MyCustomTool(Tool):\n    @property\n    def tool_type(self) -\u003e str:\n        return \"MyCustomTool\"\n\n    @property\n    def input_ports(self) -\u003e Dict[str, Port]:\n        return {\"image\": Port(\"image\", PortType.IMAGE, \"Input image\")}\n\n    @property\n    def output_ports(self) -\u003e Dict[str, Port]:\n        return {\"image\": Port(\"image\", PortType.IMAGE, \"Output image\")}\n\n    def process(self) -\u003e bool:\n        image = self.inputs[\"image\"].data\n        # Do something with the image\n        self.outputs[\"image\"] = ToolOutput(processed_image, PortType.IMAGE)\n        return True\n```\n\nTools are automatically discovered by the registry. See `CLAUDE.md` for detailed development guidance.\n\n## Installation for Development\n\n```bash\ngit clone \u003crepository-url\u003e\ncd agentui\n\n# Python setup\npython -m venv venv\nsource venv/bin/activate  # Windows: venv\\Scripts\\activate\npip install -e .\n\n# Start server\nagentui start\n\n# Optional: UI development (only if modifying Svelte code)\ncd ui\nnpm install\nnpm run build\n```\n\n## Roadmap\n\nFuture additions will focus on:\n- Additional ML models (OCR, classification, keypoint detection)\n- Vision-language models (GPT-4V, Claude, Gemini, Qwen-VL)\n- Cloud storage integrations (S3, GCS, Azure)\n- Advanced tracking and analytics\n- Real-time streaming workflows\n\n\n## Documentation\n\n- **[CLAUDE.md](CLAUDE.md)**: Complete developer guide and architecture documentation\n\n## Requirements\n\n- Python 3.9+\n- Optional: Node.js 18+ (only for UI development)\n\n## License\n\nMIT License - see LICENSE file for details\n\n## Contributing\n\nContributions welcome! Please check `CLAUDE.md` for development guidelines and architecture overview.\n\n---\n\n**Built by [Datamarkin](https://datamarkin.com)** - Making computer vision accessible through visual workflows.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatamarkin%2Fagentui","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdatamarkin%2Fagentui","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatamarkin%2Fagentui/lists"}