--- title: "cherche vs typesense" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/raphaelsty-cherche-vs-typesense-typesense" tools: ["raphaelsty-cherche", "typesense-typesense"] --- # cherche vs typesense *GraphCanon updated Aug 23, 2026* ## Verdict Pick cherche if cherche is a Python library for implementing neural search capabilities; pick typesense if typesense is an open-source and type-tolerant fuzzy search engine written in C++, primarily suitable for applications requiring speedy search responses with high tolerance to typos. [cherche](https://github.com/raphaelsty/cherche) reports 332 GitHub stars, 14 forks, and 4 open issues, last pushed Jun 1, 2024. [typesense](https://typesense.org) has 26k stars, 963 forks, and 872 open issues, last pushed Aug 18, 2026. Figures are from public GitHub metadata via [cherche's repository](https://github.com/raphaelsty/cherche) and [typesense's repository](https://github.com/typesense/typesense). | | [cherche](/tools/raphaelsty-cherche.md) | [typesense](/tools/typesense-typesense.md) | | --- | --- | --- | | Tagline | Neural Search | Fast, typo tolerant, in-memory fuzzy Search Engine | | Stars | 332 | 26,475 | | Forks | 14 | 963 | | Open issues | 4 | 872 | | Language | Python | C++ | | Adopt for | Cherche is a Python library for implementing neural search capabilities. | Typesense is an open-source and type-tolerant fuzzy search engine written in C++, primarily suitable for applications requiring speedy search responses with high tolerance to typos. | | Persona | - | - | | Runtime | - | - | | License | MIT | GPL-3.0 License ensures typesense is free to use, modify and distribute as long as those changes are made available under the same licensing terms. | | Categories | Data & Retrieval, Evaluation & Observability, Vector Databases | Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [cherche](/tools/raphaelsty-cherche.md) | [typesense](/tools/typesense-typesense.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 812d | 4d | | Open issues (now) | 4 | 872 | | Stars delta | 0 (30d) | +128 (30d) | | Open issues delta | 0 (30d) | +20 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/raphaelsty-cherche/trust.md) | [trust report](/tools/typesense-typesense/trust.md) | ## Shared compatibility - **Python**: [cherche](/tools/raphaelsty-cherche.md) - Python runtime; [typesense](/tools/typesense-typesense.md) - Python runtime ## Decision facts: cherche - **Adopt for:** Cherche is a Python library for implementing neural search capabilities. ## Decision facts: typesense - **Hosting:** self hosted - Self-hosting on-premises or in-cloud environments, enabling full control over data and infrastructure. - **Adopt for:** Typesense is an open-source and type-tolerant fuzzy search engine written in C++, primarily suitable for applications requiring speedy search responses with high tolerance to typos. - **License detail:** GPL-3.0 License ensures typesense is free to use, modify and distribute as long as those changes are made available under the same licensing terms. ## Choose when ### Choose cherche if… - cherche is primarily Python; typesense is C++. - License: cherche is MIT, typesense is GPL-3.0. - Tags unique to cherche: bm25, flashtext, information-retrieval, machine-learning. - Also covers Evaluation & Observability, Vector Databases. - Cherche is a Python library for implementing neural search capabilities. ### Choose typesense if… - typesense is primarily C++; cherche is Python. - License: typesense is GPL-3.0, cherche is MIT. - Self-hosting on-premises or in-cloud environments, enabling full control over data and infrastructure. - Tags unique to typesense: algolia, datastore, elastic-search, faceting. - When seeking a drop-in replacement or alternative for Algolia, especially if considering an open-source solution. ## 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. ## When NOT to use typesense - If the project is working with a smaller dataset where setting up an additional service could be overkill and simplicity outweighs high performance. - When the team prefers not to use GPL-3.0 licensed software, as this may pose limitations or requirements on how the code can be used or distributed. - In projects requiring complex vector search functionalities that might need more than what Typesense offers in its current feature set. ## Common questions ### What is the difference between cherche and typesense? cherche: Neural Search. typesense: Fast, typo tolerant, in-memory fuzzy Search Engine. See the comparison table for live GitHub stats and shared categories. ### When should I choose cherche over typesense? Choose cherche over typesense when cherche is primarily Python; typesense is C++; License: cherche is MIT, typesense is GPL-3.0; Tags unique to cherche: bm25, flashtext, information-retrieval, machine-learning; Also covers Evaluation & Observability, Vector Databases; Cherche is a Python library for implementing neural search capabilities. ### When should I choose typesense over cherche? Choose typesense over cherche when typesense is primarily C++; cherche is Python; License: typesense is GPL-3.0, cherche is MIT; Self-hosting on-premises or in-cloud environments, enabling full control over data and infrastructure; Tags unique to typesense: algolia, datastore, elastic-search, faceting; When seeking a drop-in replacement or alternative for Algolia, especially if considering an open-source solution. ### 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. ### When should I avoid typesense? If the project is working with a smaller dataset where setting up an additional service could be overkill and simplicity outweighs high performance. When the team prefers not to use GPL-3.0 licensed software, as this may pose limitations or requirements on how the code can be used or distributed. In projects requiring complex vector search functionalities that might need more than what Typesense offers in its current feature set. ### Is cherche or typesense more popular on GitHub? typesense has more GitHub stars (26,475 vs 332). Stars measure visibility, not whether either tool fits your constraints. ### Are cherche and typesense open source? Yes - both are open-source projects on GitHub (cherche: MIT, typesense: GPL-3.0). ### Where can I find alternatives to cherche or typesense? GraphCanon lists graph-backed alternatives at [cherche alternatives](/tools/raphaelsty-cherche/alternatives) and [typesense alternatives](/tools/typesense-typesense/alternatives) ([cherche markdown twin](/tools/raphaelsty-cherche/alternatives.md), [typesense markdown twin](/tools/typesense-typesense/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/raphaelsty-cherche-vs-typesense-typesense.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, cherche or typesense? cherche: Dormant. typesense: Very active. 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 cherche and typesense? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [cherche trust report](/tools/raphaelsty-cherche/trust); [typesense trust report](/tools/typesense-typesense/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=raphaelsty-cherche`](/api/graphcanon/graph?tool=raphaelsty-cherche) - 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/_