{"data":{"slug":"starlightsearch-embedanything","name":"EmbedAnything","tagline":"Highly Performant, Modular, Memory Safe and Production-ready Inference, Ingestion and Indexing built in Rust","github_url":"https://github.com/StarlightSearch/EmbedAnything","owner":"StarlightSearch","repo":"EmbedAnything","owner_avatar_url":"https://avatars.githubusercontent.com/u/165606246?v=4","primary_language":"Rust","stars":1304,"forks":143,"topics":["ai","cloud","generative-ai","hacktoberfest","high-performance","indexing","inference","information-retrieval","large-language-models","local","machine-learning","onnxruntime","pipeline","production-ready","python","rag","rust","search","server","vector-database"],"archived":false,"github_pushed_at":"2026-08-12T08:56:59+00:00","maintenance_label":"Active","stars_delta_30d":18,"url":"https://www.graphcanon.com/tools/starlightsearch-embedanything","markdown_url":"https://www.graphcanon.com/tools/starlightsearch-embedanything.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/starlightsearch-embedanything","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=starlightsearch-embedanything","description":"Highly Performant, Modular, Memory Safe and Production-ready Inference, Ingestion and Indexing built in Rust 🦀","homepage_url":"https://embed-anything.com/","license":"Apache-2.0","open_issues":21,"watchers":12,"ai_summary":"StarlightSearch/EmbedAnything is a high-performance inference, ingestion, and indexing tool developed in Rust. It supports large language models, information retrieval, and vector database functionalities.","readme_excerpt":"## 💚 Installation\n\n`\npip install embed-anything\n`<br/>\n\nFor GPUs and using special models like ColPali <br/>\n\n`\npip install embed-anything-gpu\n`\n\n🚧❌ If it shows cuda error while running on windowns, run the following command:\n\n```\nos.add_dll_directory(\"C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v12.6/bin\")\n```","github_created_at":"2024-03-31T22:37:47+00:00","created_at":"2026-07-07T17:44:39.250065+00:00","updated_at":"2026-08-21T18:01:50.97268+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"},{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"},{"slug":"vector-databases","name":"Vector Databases","url":"https://www.graphcanon.com/categories/vector-databases","markdown_url":"https://www.graphcanon.com/categories/vector-databases.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/vector-databases"}],"tags":[{"slug":"ai","name":"ai"},{"slug":"cloud","name":"cloud"},{"slug":"generative-ai","name":"generative-ai"},{"slug":"hacktoberfest","name":"hacktoberfest"},{"slug":"high-performance","name":"high-performance"},{"slug":"indexing","name":"indexing"},{"slug":"inference","name":"inference"},{"slug":"information-retrieval","name":"information-retrieval"}],"trust":{"provenance":{"is_fork":false,"github_id":780174294,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-21T18:01:49.379Z","maintenance":{"label":"Active","score":82,"methodology":"github_public_v1","releases_90d":1,"days_since_push":9,"last_release_at":"2026-07-10T21:12:25Z","stars_delta_30d":18,"open_issues_delta_30d":-2},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:26:20.742Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-21T18:01:50.195Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-08-21T18:01:50.195Z","managed_saas":false},"languages":{"value":["rust","python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-21T18:01:50.195Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-08-21T18:01:50.195Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-21T18:01:50.195Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["- When you require high performance and memory safety for inference tasks due to its Rust foundation.","- For production environments where robustness and reliability are key factors, as it is labeled 'production-ready'.","- If your project involves modular design that allows for easy integration with existing applications or services."],"when_not_to_use":["- In scenarios requiring direct Python support without additional bridging tools, since EmbedAnything's primary language is Rust.","- If you need a tool heavily optimized for edge computing where minimal memory usage trumps safety and performance considerations."],"source":"enrich:decision_facts","observed_at":"2026-07-12T01:32:17.355Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"EmbedAnything is a Rust-based tool focused on highly performant and modular operations for inference, ingestion, and indexing of large language models, designed with memory safety and production-readiness in mind."}]}}