--- title: "orama vs cherche" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/oramasearch-orama-vs-raphaelsty-cherche" tools: ["oramasearch-orama", "raphaelsty-cherche"] --- # orama vs cherche *GraphCanon updated Aug 21, 2026* ## Verdict Pick orama if 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; pick cherche if cherche is a Python library for implementing neural search capabilities. [orama](https://docs.orama.com/docs/orama-js) reports 11k GitHub stars, 398 forks, and 21 open issues, last pushed Aug 4, 2026. [cherche](https://github.com/raphaelsty/cherche) has 332 stars, 14 forks, and 4 open issues, last pushed Jun 1, 2024. Figures are from public GitHub metadata via [orama's repository](https://github.com/oramasearch/orama) and [cherche's repository](https://github.com/raphaelsty/cherche). | | [orama](/tools/oramasearch-orama.md) | [cherche](/tools/raphaelsty-cherche.md) | | --- | --- | --- | | Tagline | A complete search engine and RAG pipeline with support for full-text, vector, and hybrid search. | Neural Search | | Stars | 10,523 | 332 | | Forks | 398 | 14 | | Open issues | 21 | 4 | | Language | TypeScript | Python | | Adopt for | 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. | Cherche is a Python library for implementing neural search capabilities. | | Persona | - | - | | Runtime | - | - | | License | Other | MIT | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Evaluation & Observability, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [orama](/tools/oramasearch-orama.md) | [cherche](/tools/raphaelsty-cherche.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 17d | 782d | | Open issues (now) | 21 | 4 | | Stars delta | +26 (30d) | Unknown | | Open issues delta | +3 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/oramasearch-orama/trust.md) | [trust report](/tools/raphaelsty-cherche/trust.md) | ## Decision facts: orama - **Adopt for:** 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. ## Decision facts: cherche - **Adopt for:** Cherche is a Python library for implementing neural search capabilities. ## Choose when ### Choose orama if… - orama is primarily TypeScript; cherche is Python. - License: orama is Other, cherche is MIT. - Tags unique to orama: full-text, hybrid-search, search-engine, vector-database-embedding. - - When you need a lightweight (~2kb) solution for integrating robust search capabilities into web applications. ### Choose cherche if… - cherche is primarily Python; orama is TypeScript. - License: cherche is MIT, orama is Other. - Tags unique to cherche: bm25, flashtext, information-retrieval, machine-learning. - Also covers Evaluation & Observability. - Cherche is a Python library for implementing neural search capabilities. ## When NOT to use orama - - 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. ## When NOT to use cherche - Last GitHub push was 811 days ago (dormant maintenance, Jun 1, 2024). Validate activity before betting a new project on cherche. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between orama and cherche? orama: A complete search engine and RAG pipeline with support for full-text, vector, and hybrid search.. cherche: Neural Search. See the comparison table for live GitHub stats and shared categories. ### When should I choose orama over cherche? Choose orama over cherche when orama is primarily TypeScript; cherche is Python; License: orama is Other, cherche is MIT; Tags unique to orama: full-text, hybrid-search, search-engine, vector-database-embedding; - When you need a lightweight (~2kb) solution for integrating robust search capabilities into web applications. ### When should I choose cherche over orama? Choose cherche over orama when cherche is primarily Python; orama is TypeScript; License: cherche is MIT, orama is Other; Tags unique to cherche: bm25, flashtext, information-retrieval, machine-learning; Also covers Evaluation & Observability; Cherche is a Python library for implementing neural search capabilities. ### When should I avoid orama? - 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. ### When should I avoid cherche? Last GitHub push was 811 days ago (dormant maintenance, Jun 1, 2024). Validate activity before betting a new project on cherche. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is orama or cherche more popular on GitHub? orama has more GitHub stars (10,523 vs 332). Stars measure visibility, not whether either tool fits your constraints. ### Are orama and cherche open source? Yes - both are open-source projects on GitHub (orama: Other, cherche: MIT). ### Where can I find alternatives to orama or cherche? GraphCanon lists graph-backed alternatives at [orama alternatives](/tools/oramasearch-orama/alternatives) and [cherche alternatives](/tools/raphaelsty-cherche/alternatives) ([orama markdown twin](/tools/oramasearch-orama/alternatives.md), [cherche markdown twin](/tools/raphaelsty-cherche/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/oramasearch-orama-vs-raphaelsty-cherche.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, orama or cherche? orama: Active. cherche: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for orama and cherche? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [orama trust report](/tools/oramasearch-orama/trust); [cherche trust report](/tools/raphaelsty-cherche/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=oramasearch-orama`](/api/graphcanon/graph?tool=oramasearch-orama) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_