{"data":{"slug":"pixeltable-pixeltable","name":"pixeltable","tagline":"Unified multimodal backend for AI data apps","github_url":"https://github.com/pixeltable/pixeltable","owner":"pixeltable","repo":"pixeltable","owner_avatar_url":"https://avatars.githubusercontent.com/u/160283145?v=4","primary_language":"Python","stars":1613,"forks":219,"topics":["ai","computer-vision","data-science","database","feature-engineering","feature-store","genai","llm","machine-learning","ml","multimodal","vector-database"],"archived":false,"github_pushed_at":"2026-08-21T06:36:51+00:00","maintenance_label":"Very active","stars_delta_30d":9,"url":"https://www.graphcanon.com/tools/pixeltable-pixeltable","markdown_url":"https://www.graphcanon.com/tools/pixeltable-pixeltable.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/pixeltable-pixeltable","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=pixeltable-pixeltable","description":"Unified multimodal backend for AI data apps","homepage_url":"https://docs.pixeltable.com","license":"Apache-2.0","open_issues":43,"watchers":21,"ai_summary":"PixelTable is a unified platform supporting AI applications with a focus on multimodality, including computer vision and machine learning features.","readme_excerpt":"## Installation\n\n```bash\npip install pixeltable  # SDK + CLI (pxt ls, rows, errors, …)\n```\n\n---\n\n## Quick Start\n\nDefine schema in Python, routes in TOML: a `pxt.Video` table, frame view, one computed column on the frame view, and a single insert endpoint.\n\n```python\n\n---\n\n## License\n\nPixeltable is licensed under the [Apache 2.0 License](https://opensource.org/licenses/Apache-2.0).","github_created_at":"2023-05-10T18:03:02+00:00","created_at":"2026-07-07T17:44:27.881594+00:00","updated_at":"2026-08-21T12:01:25.33427+00:00","categories":[{"slug":"computer-vision","name":"Computer Vision","url":"https://www.graphcanon.com/categories/computer-vision","markdown_url":"https://www.graphcanon.com/categories/computer-vision.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/computer-vision"},{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"ai","name":"ai"},{"slug":"artificial-intelligence","name":"artificial-intelligence"},{"slug":"chatbot","name":"chatbot"},{"slug":"computer-vision","name":"computer-vision"},{"slug":"data-science","name":"data-science"},{"slug":"database","name":"database"},{"slug":"feature-engineering","name":"feature-engineering"},{"slug":"feature-store","name":"feature-store"}],"trust":{"provenance":{"is_fork":false,"github_id":639079153,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-21T12:01:24.521Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":8,"days_since_push":0,"last_release_at":"2026-08-14T21:33:36Z","stars_delta_30d":9,"open_issues_delta_30d":2},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:25:52.210Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-21T12:01:25.012Z"},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-08-21T12:01:25.012Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-21T12:01:25.012Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-21T12:01:25.012Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When your project requires seamless integration of both image processing and traditional ML tasks under one robust framework.","For developers looking to create complex AI applications that can handle multiple data types like images and text simultaneously without needing to integrate separate systems.","If you prioritize ease-of-use in a feature-rich platform for multimodal projects, where extensive configuration is minimized but comprehensive capabilities are paramount."],"when_not_to_use":["For teams focused solely on monomodal tasks or those who need specialized tools that offer deeper functionality in specific areas such as audio processing alone.","If your development team has a strong preference for languages other than Python, given PixelTable's reliance on the Python ecosystem.","When strict control over every aspect of model training and feature engineering is required, as PixelTable provides a more integrated solution that might limit granular customization."],"source":"enrich:decision_facts","observed_at":"2026-07-14T20:15:50.680Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"PixelTable is a Python-based platform designed for multimodal AI applications, offering integration across vision tasks and machine learning operations."}]}}