{"data":{"slug":"lance-format-lance","name":"lance","tagline":"Open Lakehouse Format for Multimodal AI","github_url":"https://github.com/lance-format/lance","owner":"lance-format","repo":"lance","owner_avatar_url":"https://avatars.githubusercontent.com/u/243204601?v=4","primary_language":"Rust","stars":7120,"forks":867,"topics":["apache-arrow","computer-vision","data-analysis","data-analytics","data-centric","data-format","data-science","dataops","deep-learning","duckdb","embeddings","llms","machine-learning","mlops","python","rust"],"archived":false,"github_pushed_at":"2026-09-27T17:26:44+00:00","maintenance_label":"Very active","stars_delta_30d":220,"url":"https://www.graphcanon.com/tools/lance-format-lance","markdown_url":"https://www.graphcanon.com/tools/lance-format-lance.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/lance-format-lance","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=lance-format-lance","description":"Open Lakehouse Format for Multimodal AI. Convert from Parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming..","homepage_url":"https://lance.org","license":"Apache-2.0","open_issues":1279,"watchers":53,"ai_summary":"Facilitates fast random access, vector index creation, and data versioning with compatibility across Pandas, DuckDB, Polars, Pyarrow, PyTorch.","readme_excerpt":"## Quick Start\n\n**Installation**\n\n```shell\npip install pylance\n```\n\nTo install a preview release:\n\n```shell\npip install --pre --extra-index-url https://pypi.fury.io/lance-format pylance\n```\n\n> [!TIP]\n> Preview releases are released more often than full releases and contain the\n> latest features and bug fixes. They receive the same level of testing as full releases.\n> We guarantee they will remain published and available for download for at\n> least 6 months. When you want to pin to a specific version, prefer a stable release.\n\n**Converting to Lance**\n\n```python\nimport lance\n\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.dataset\n\ndf = pd.DataFrame({\"a\": [5], \"b\": [10]})\nuri = \"/tmp/test.parquet\"\ntbl = pa.Table.from_pandas(df)\npa.dataset.write_dataset(tbl, uri, format='parquet')\n\nparquet = pa.dataset.dataset(uri, format='parquet')\nlance.write_dataset(parquet, \"/tmp/test.lance\")\n```\n\n**Reading Lance data**\n```python\ndataset = lance.dataset(\"/tmp/test.lance\")\nassert isinstance(dataset, pa.dataset.Dataset)\n```\n\n**Pandas**\n```python\ndf = dataset.to_table().to_pandas()\ndf\n```\n\n**DuckDB**\n```python\nimport duckdb","github_created_at":"2022-07-07T22:29:29+00:00","created_at":"2026-07-11T11:28:30.276898+00:00","updated_at":"2026-09-28T06:00:34.154895+00:00","categories":[{"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"}],"tags":[{"slug":"apache-arrow","name":"apache-arrow"},{"slug":"computer-vision","name":"computer-vision"},{"slug":"data-analysis","name":"data-analysis"},{"slug":"data-analytics","name":"data-analytics"},{"slug":"data-centric","name":"data-centric"},{"slug":"data-format","name":"data-format"},{"slug":"data-science","name":"data-science"},{"slug":"dataops","name":"dataops"}],"trust":{"provenance":{"is_fork":false,"github_id":511691380,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-28T06:00:32.939Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":30,"days_since_push":0,"last_release_at":"2026-09-26T03:16:16Z","stars_delta_30d":220,"open_issues_delta_30d":249},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:27:55.876Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-28T06:00:33.720Z"},"deploy":{"source":"dockerfile:docker-compose.yml","self_host":true,"observed_at":"2026-09-28T06:00:33.720Z","managed_saas":false},"languages":{"value":["rust"],"source":"github.language","observed_at":"2026-09-28T06:00:33.720Z"},"has_docker":{"value":true,"source":"dockerfile:docker-compose.yml","observed_at":"2026-09-28T06:00:33.720Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-09-28T06:00:33.720Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["Use Lance when you need fast random access to datasets formatted in a way that supports multimodal AI workloads.","Use if your project requires the ability to quickly convert Parquet files into Lance without significant changes to your codebase."],"when_not_to_use":["Do not use Lance if your application strictly depends on a specific format other than those compatible with it, such as HDF5 or non-supported SQL databases.","Avoid using Lance if real-time performance is critical for all operations and you do not require vector indexing capabilities."],"source":"enrich:decision_facts","observed_at":"2026-07-16T21:23:28.629Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Lance is an open lakehouse format built for multimodal AI, offering fast random access and vector index creation with extensive language compatibility."}]}}