--- title: "awesome-vector-search vs cherche" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/currentslab-awesome-vector-search-vs-raphaelsty-cherche" tools: ["currentslab-awesome-vector-search", "raphaelsty-cherche"] --- # awesome-vector-search vs cherche *GraphCanon updated Aug 23, 2026* ## Verdict Pick awesome-vector-search if curated collection of vector search-related resources including libraries, services, and research papers; pick cherche if cherche is a Python library for implementing neural search capabilities. [awesome-vector-search](https://github.com/currentslab/awesome-vector-search) reports 1.6k GitHub stars, 127 forks, and 19 open issues, last pushed Jul 6, 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 [awesome-vector-search's repository](https://github.com/currentslab/awesome-vector-search) and [cherche's repository](https://github.com/raphaelsty/cherche). | | [awesome-vector-search](/tools/currentslab-awesome-vector-search.md) | [cherche](/tools/raphaelsty-cherche.md) | | --- | --- | --- | | Tagline | Collections of vector search related libraries, service and research papers | Neural Search | | Stars | 1,581 | 332 | | Forks | 127 | 14 | | Open issues | 19 | 4 | | Language | - | Python | | Adopt for | Curated collection of vector search-related resources including libraries, services, and research papers. | Cherche is a Python library for implementing neural search capabilities. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Vector Databases | Data & Retrieval, Evaluation & Observability, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-vector-search](/tools/currentslab-awesome-vector-search.md) | [cherche](/tools/raphaelsty-cherche.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 48d | 812d | | Open issues (now) | 19 | 4 | | Stars delta | +5 (30d) | 0 (30d) | | Open issues delta | +5 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/currentslab-awesome-vector-search/trust.md) | [trust report](/tools/raphaelsty-cherche/trust.md) | ## Decision facts: awesome-vector-search - **Adopt for:** Curated collection of vector search-related resources including libraries, services, and research papers. ## Decision facts: cherche - **Adopt for:** Cherche is a Python library for implementing neural search capabilities. ## Choose when ### Choose awesome-vector-search if… - Tags unique to awesome-vector-search: awesome, awesome-list, knn-search, nearest-neighbor-search. - You need a comprehensive overview of vector search technology. - More GitHub stars (1.6k vs 332) - visibility, not fit. ### Choose cherche if… - Tags unique to cherche: bm25, flashtext, information-retrieval, natural-language-processing. - Also covers Data & Retrieval, Evaluation & Observability. - Cherche is a Python library for implementing neural search capabilities. ## When NOT to use awesome-vector-search - Require real-time vector search service implementation details outside listed libraries. - Seeking detailed code tutorials rather than a list of resources. ## When NOT to use cherche - Last GitHub push was 815 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 awesome-vector-search and cherche? awesome-vector-search: Collections of vector search related libraries, service and research papers. cherche: Neural Search. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-vector-search over cherche? Choose awesome-vector-search over cherche when Tags unique to awesome-vector-search: awesome, awesome-list, knn-search, nearest-neighbor-search; You need a comprehensive overview of vector search technology; More GitHub stars (1.6k vs 332) - visibility, not fit. ### When should I choose cherche over awesome-vector-search? Choose cherche over awesome-vector-search when Tags unique to cherche: bm25, flashtext, information-retrieval, natural-language-processing; Also covers Data & Retrieval, Evaluation & Observability; Cherche is a Python library for implementing neural search capabilities. ### When should I avoid awesome-vector-search? Require real-time vector search service implementation details outside listed libraries. Seeking detailed code tutorials rather than a list of resources. ### When should I avoid cherche? Last GitHub push was 815 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 awesome-vector-search or cherche more popular on GitHub? awesome-vector-search has more GitHub stars (1,581 vs 332). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-vector-search and cherche open source? Yes - both are open-source projects on GitHub (awesome-vector-search: MIT, cherche: MIT). ### Where can I find alternatives to awesome-vector-search or cherche? GraphCanon lists graph-backed alternatives at [awesome-vector-search alternatives](/tools/currentslab-awesome-vector-search/alternatives) and [cherche alternatives](/tools/raphaelsty-cherche/alternatives) ([awesome-vector-search markdown twin](/tools/currentslab-awesome-vector-search/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/currentslab-awesome-vector-search-vs-raphaelsty-cherche.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-vector-search or cherche? awesome-vector-search: Steady. 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 awesome-vector-search and cherche? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-vector-search trust report](/tools/currentslab-awesome-vector-search/trust); [cherche trust report](/tools/raphaelsty-cherche/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=currentslab-awesome-vector-search`](/api/graphcanon/graph?tool=currentslab-awesome-vector-search) - 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/_