--- title: "aquila vs cherche" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aquila-network-aquila-vs-raphaelsty-cherche" tools: ["aquila-network-aquila", "raphaelsty-cherche"] --- # aquila vs cherche *GraphCanon updated Aug 23, 2026* ## Verdict Pick aquila if aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches; pick cherche if cherche is a Python library for implementing neural search capabilities. [aquila](https://aquila.network) reports 379 GitHub stars, 26 forks, and 13 open issues, last pushed May 6, 2024. [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 [aquila's repository](https://github.com/Aquila-Network/aquila) and [cherche's repository](https://github.com/raphaelsty/cherche). | | [aquila](/tools/aquila-network-aquila.md) | [cherche](/tools/raphaelsty-cherche.md) | | --- | --- | --- | | Tagline | Efficient Neural Search Engine | Neural Search | | Stars | 379 | 332 | | Forks | 26 | 14 | | Open issues | 13 | 4 | | Language | HTML | Python | | Adopt for | Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches. | Cherche is a Python library for implementing neural search capabilities. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Evaluation & Observability, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aquila](/tools/aquila-network-aquila.md) | [cherche](/tools/raphaelsty-cherche.md) | | --- | --- | --- | | Days since push | 817d | 812d | | Open issues (now) | 13 | 4 | | Stars delta | Unknown | 0 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/aquila-network-aquila/trust.md) | [trust report](/tools/raphaelsty-cherche/trust.md) | ## Decision facts: aquila - **Adopt for:** Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches. ## Decision facts: cherche - **Adopt for:** Cherche is a Python library for implementing neural search capabilities. ## Choose when ### Choose aquila if… - aquila is primarily HTML; cherche is Python. - Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors. - When deploying a solution that requires the processing of feature vectors in image or video search contexts, where efficiency in approximate nearest neighbor search is necessary ### Choose cherche if… - cherche is primarily Python; aquila is HTML. - Tags unique to cherche: bm25, flashtext, machine-learning, natural-language-processing. - Also covers Evaluation & Observability. - Cherche is a Python library for implementing neural search capabilities. ## When NOT to use aquila - If the development team lacks experience with Docker, as Aquila's setup heavily relies on Docker images to run locally or in a big data configuration - In scenarios where strict control over metadata and vector indexing is required beyond what JSON and latent vectors can provide ## 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 aquila and cherche? aquila: Efficient Neural Search Engine. cherche: Neural Search. See the comparison table for live GitHub stats and shared categories. ### When should I choose aquila over cherche? Choose aquila over cherche when aquila is primarily HTML; cherche is Python; Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors; When deploying a solution that requires the processing of feature vectors in image or video search contexts, where efficiency in approximate nearest neighbor search is necessary. ### When should I choose cherche over aquila? Choose cherche over aquila when cherche is primarily Python; aquila is HTML; Tags unique to cherche: bm25, flashtext, machine-learning, natural-language-processing; Also covers Evaluation & Observability; Cherche is a Python library for implementing neural search capabilities. ### When should I avoid aquila? If the development team lacks experience with Docker, as Aquila's setup heavily relies on Docker images to run locally or in a big data configuration In scenarios where strict control over metadata and vector indexing is required beyond what JSON and latent vectors can provide ### 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 aquila or cherche more popular on GitHub? aquila has more GitHub stars (379 vs 332). Stars measure visibility, not whether either tool fits your constraints. ### Are aquila and cherche open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to aquila or cherche? GraphCanon lists graph-backed alternatives at [aquila alternatives](/tools/aquila-network-aquila/alternatives) and [cherche alternatives](/tools/raphaelsty-cherche/alternatives) ([aquila markdown twin](/tools/aquila-network-aquila/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/aquila-network-aquila-vs-raphaelsty-cherche.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aquila or cherche? aquila: Dormant. 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 aquila and cherche? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aquila trust report](/tools/aquila-network-aquila/trust); [cherche trust report](/tools/raphaelsty-cherche/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aquila-network-aquila`](/api/graphcanon/graph?tool=aquila-network-aquila) - 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/_