{"data":{"slug":"oramasearch-orama","name":"orama","tagline":"A complete search engine and RAG pipeline with support for full-text, vector, and hybrid search.","github_url":"https://github.com/oramasearch/orama","owner":"oramasearch","repo":"orama","owner_avatar_url":"https://avatars.githubusercontent.com/u/123180461?v=4","primary_language":"TypeScript","stars":10523,"forks":398,"topics":["algiorithm","data-structures","full-text","javascript","node","search","search-algorithm","search-engine","typescript","typo-tolerance","vector","vector-database","vector-database-embedding","vector-search","vector-search-engine"],"archived":false,"github_pushed_at":"2026-08-04T00:12:46+00:00","maintenance_label":"Active","stars_delta_30d":26,"url":"https://www.graphcanon.com/tools/oramasearch-orama","markdown_url":"https://www.graphcanon.com/tools/oramasearch-orama.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/oramasearch-orama","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=oramasearch-orama","description":"🌌  A complete search engine and RAG pipeline in your browser, server or edge network with support for full-text, vector, and hybrid search in less than 2kb.","homepage_url":"https://docs.orama.com/docs/orama-js","license":"Other","open_issues":21,"watchers":44,"ai_summary":"OramaSearch/Orama provides a compact (<2kb) solution for integrating search capabilities including full-text, vector, and hybrid searching, designed to work in browsers, servers, or edge networks.","readme_excerpt":"# Installation\n\nYou can install Orama using `npm`, `yarn`, `pnpm`, `bun`:\n\n```sh\nnpm i @orama/orama\n```\n\nOr import it directly in a browser module:\n\n```html\n<html>\n  <body>\n    <script type=\"module\">\n      import { create, insert, search } from 'https://cdn.jsdelivr.net/npm/@orama/orama@latest/+esm'\n    </script>\n  </body>\n</html>\n```\n\nWith Deno, you can just use the same CDN URL or use npm specifiers:\n\n```js\nimport { create, search, insert } from 'npm:@orama/orama'\n```\n\nRead the complete documentation at [https://docs.orama.com](https://docs.orama.com).\n\n---\n\n# License\n\nOrama is licensed under the [Apache 2.0](/LICENSE.md) license.\n\n<img referrerpolicy=\"no-referrer-when-downgrade\" src=\"https://static.scarf.sh/a.png?x-pxid=16782f89-15fb-4e03-8e9c-2a06106542f7\" />","github_created_at":"2022-05-10T12:01:50+00:00","created_at":"2026-07-07T17:43:45.144746+00:00","updated_at":"2026-08-21T06:01:58.723654+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":"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":"full-text","name":"full-text"},{"slug":"hybrid-search","name":"hybrid-search"},{"slug":"search-engine","name":"search-engine"},{"slug":"vector-database-embedding","name":"vector-database-embedding"},{"slug":"vector-search","name":"vector-search"}],"trust":{"provenance":{"is_fork":false,"github_id":490677825,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-21T06:01:57.745Z","maintenance":{"label":"Active","score":82,"methodology":"github_public_v1","releases_90d":0,"days_since_push":17,"last_release_at":"2025-12-19T22:13:02Z","stars_delta_30d":26,"open_issues_delta_30d":3},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:24:13.955Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"mcp":{"source":"repo_scan","observed_at":"2026-08-21T06:01:58.185Z","server_manifest":false},"scan":{"source":"repo_scan","observed_at":"2026-08-21T06:01:58.185Z"},"languages":{"value":["typescript","javascript"],"source":"github.language+package.json","observed_at":"2026-08-21T06:01:58.185Z"},"license_spdx":{"value":"Other","source":"github.license","observed_at":"2026-08-21T06:01:58.185Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["- When you need a lightweight (~2kb) solution for integrating robust search capabilities into web applications.","- For projects requiring both text and vector searches with a small footprint that needs to run efficiently on the client side (browser).","- In environments where minimizing resource consumption is critical, yet there’s a requirement for advanced hybrid searching."],"when_not_to_use":["- If your project demands high-throughput and low-latency text search in large document sets, as others may offer more optimized backend solutions.","- For situations requiring scalability to handle very large datasets; Orama's compact nature might restrict its performance with extensive data.","- In instances where a rich set of administrative tools or built-in storage solutions are necessary, as Orama focuses on lightweight search functionality."],"source":"enrich:decision_facts","observed_at":"2026-07-11T15:46:51.798Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Orama is a compact (<2kb) full-text, vector, and hybrid search engine supporting RAG pipelines that can be deployed in browsers, servers, or edge networks."}]}}