--- title: "awesome-vector-database vs cherche" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dangkhoasdc-awesome-vector-database-vs-raphaelsty-cherche" tools: ["dangkhoasdc-awesome-vector-database", "raphaelsty-cherche"] --- # awesome-vector-database vs cherche *GraphCanon updated Aug 23, 2026* ## Verdict Pick awesome-vector-database if a curated list of works on vector databases and high-dimensional structure searching without any implementation details; pick cherche if cherche is a Python library for implementing neural search capabilities. [awesome-vector-database](https://github.com/dangkhoasdc/awesome-vector-database) reports 359 GitHub stars, 31 forks, and 10 open issues, last pushed Jul 20, 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-database's repository](https://github.com/dangkhoasdc/awesome-vector-database) and [cherche's repository](https://github.com/raphaelsty/cherche). | | [awesome-vector-database](/tools/dangkhoasdc-awesome-vector-database.md) | [cherche](/tools/raphaelsty-cherche.md) | | --- | --- | --- | | Tagline | A curated list of works on high dimensional structure/vector search and databases | Neural Search | | Stars | 359 | 332 | | Forks | 31 | 14 | | Open issues | 10 | 4 | | Language | - | Python | | Adopt for | A curated list of works on vector databases and high-dimensional structure searching without any implementation details. | Cherche is a Python library for implementing neural search capabilities. | | Persona | - | - | | Runtime | - | - | | License | CC0-1.0 | MIT | | Categories | Vector Databases | Data & Retrieval, Evaluation & Observability, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-vector-database](/tools/dangkhoasdc-awesome-vector-database.md) | [cherche](/tools/raphaelsty-cherche.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 33d | 812d | | Open issues (now) | 10 | 4 | | Stars delta | +4 (30d) | 0 (30d) | | Open issues delta | +4 (30d) | 0 (30d) | | Full report | [trust report](/tools/dangkhoasdc-awesome-vector-database/trust.md) | [trust report](/tools/raphaelsty-cherche/trust.md) | ## Decision facts: awesome-vector-database - **Adopt for:** A curated list of works on vector databases and high-dimensional structure searching without any implementation details. ## Decision facts: cherche - **Adopt for:** Cherche is a Python library for implementing neural search capabilities. ## Choose when ### Choose awesome-vector-database if… - License: awesome-vector-database is CC0-1.0, cherche is MIT. - Tags unique to awesome-vector-database: approximate-nearest-neighbor-search, embedding-similarity, embeddings-similarity, nearest-neighbor-search. - If you require a comprehensive overview of vector database projects and research papers, as it aggregates information from various sources across the field. ### Choose cherche if… - License: cherche is MIT, awesome-vector-database is CC0-1.0. - Tags unique to cherche: bm25, flashtext, information-retrieval, machine-learning. - Also covers Data & Retrieval, Evaluation & Observability. - Cherche is a Python library for implementing neural search capabilities. ## When NOT to use awesome-vector-database - To find ready-to-use implementations or specific product releases; this repository serves more as a collection of references rather than real-world tools. - If you are looking for direct integration code snippets or detailed tutorials, since the tool is centered on listing and curating resources without delving into practical guides. ## 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-database and cherche? awesome-vector-database: A curated list of works on high dimensional structure/vector search and databases. cherche: Neural Search. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-vector-database over cherche? Choose awesome-vector-database over cherche when License: awesome-vector-database is CC0-1.0, cherche is MIT; Tags unique to awesome-vector-database: approximate-nearest-neighbor-search, embedding-similarity, embeddings-similarity, nearest-neighbor-search; If you require a comprehensive overview of vector database projects and research papers, as it aggregates information from various sources across the field. ### When should I choose cherche over awesome-vector-database? Choose cherche over awesome-vector-database when License: cherche is MIT, awesome-vector-database is CC0-1.0; Tags unique to cherche: bm25, flashtext, information-retrieval, machine-learning; Also covers Data & Retrieval, Evaluation & Observability; Cherche is a Python library for implementing neural search capabilities. ### When should I avoid awesome-vector-database? To find ready-to-use implementations or specific product releases; this repository serves more as a collection of references rather than real-world tools. If you are looking for direct integration code snippets or detailed tutorials, since the tool is centered on listing and curating resources without delving into practical guides. ### 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-database or cherche more popular on GitHub? awesome-vector-database has more GitHub stars (359 vs 332). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-vector-database and cherche open source? Yes - both are open-source projects on GitHub (awesome-vector-database: CC0-1.0, cherche: MIT). ### Where can I find alternatives to awesome-vector-database or cherche? GraphCanon lists graph-backed alternatives at [awesome-vector-database alternatives](/tools/dangkhoasdc-awesome-vector-database/alternatives) and [cherche alternatives](/tools/raphaelsty-cherche/alternatives) ([awesome-vector-database markdown twin](/tools/dangkhoasdc-awesome-vector-database/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/dangkhoasdc-awesome-vector-database-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-database or cherche? awesome-vector-database: 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-database and cherche? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-vector-database trust report](/tools/dangkhoasdc-awesome-vector-database/trust); [cherche trust report](/tools/raphaelsty-cherche/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dangkhoasdc-awesome-vector-database`](/api/graphcanon/graph?tool=dangkhoasdc-awesome-vector-database) - 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/_