--- title: "aquila vs grepai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aquila-network-aquila-vs-yoanbernabeu-grepai" tools: ["aquila-network-aquila", "yoanbernabeu-grepai"] --- # aquila vs grepai *GraphCanon updated Aug 22, 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 grepai if grepai is designed for developing local semantic search and call graphs for AI agents with an emphasis on privacy. [aquila](https://aquila.network) reports 379 GitHub stars, 26 forks, and 13 open issues, last pushed May 6, 2024. [grepai](https://yoanbernabeu.github.io/grepai/) has 1.8k stars, 152 forks, and 97 open issues, last pushed Jun 22, 2026. Figures are from public GitHub metadata via [aquila's repository](https://github.com/Aquila-Network/aquila) and [grepai's repository](https://github.com/yoanbernabeu/grepai). | | [aquila](/tools/aquila-network-aquila.md) | [grepai](/tools/yoanbernabeu-grepai.md) | | --- | --- | --- | | Tagline | Efficient Neural Search Engine | Semantic Search & Call Graphs for AI Agents (100% Local) | | Stars | 379 | 1,825 | | Forks | 26 | 152 | | Open issues | 13 | 97 | | Language | HTML | C | | Adopt for | Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches. | grepai is designed for developing local semantic search and call graphs for AI agents with an emphasis on privacy. | | Persona | - | - | | Runtime | - | - | | License | - | MIT license, allowing for wide usage in both open-source and proprietary applications without restrictions on redistribution. | | Categories | Data & Retrieval, Vector Databases | Developer Tools, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aquila](/tools/aquila-network-aquila.md) | [grepai](/tools/yoanbernabeu-grepai.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 817d | 61d | | Open issues (now) | 13 | 97 | | Stars delta | Unknown | +36 (30d) | | Open issues delta | Unknown | +1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/aquila-network-aquila/trust.md) | [trust report](/tools/yoanbernabeu-grepai/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: grepai - **Pricing:** freemium - Available at no cost due to its MIT licensing, with the scope of use limited only by any dependencies it requires. - **Adopt for:** grepai is designed for developing local semantic search and call graphs for AI agents with an emphasis on privacy. - **License detail:** MIT license, allowing for wide usage in both open-source and proprietary applications without restrictions on redistribution. ## Choose when ### Choose aquila if… - aquila is primarily HTML; grepai is C. - Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors. - Also covers Data & Retrieval. - 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 grepai if… - grepai is primarily C; aquila is HTML. - Pricing: Available at no cost due to its MIT licensing, with the scope of use limited only by any dependencies it requires.. - Tags unique to grepai: ai, claude-code, cli, code-search. - Also covers Developer Tools. - When you need to develop a system that prioritizes local processing without cloud dependencies, which is essential in highly regulated environments where data privacy and security are paramount. ## 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 grepai - Not suitable if your application requires real-time updates from cloud-based services, as grepai operates entirely locally without any online components for updating its functionality. - If the development team prefers not to deal with C language implementation details,grepai might be less attractive given that other tools may support more modern or higher-level languages like Python. ## Common questions ### What is the difference between aquila and grepai? aquila: Efficient Neural Search Engine. grepai: Semantic Search & Call Graphs for AI Agents (100% Local). See the comparison table for live GitHub stats and shared categories. ### When should I choose aquila over grepai? Choose aquila over grepai when aquila is primarily HTML; grepai is C; Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors; Also covers Data & Retrieval; 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 grepai over aquila? Choose grepai over aquila when grepai is primarily C; aquila is HTML; Pricing: Available at no cost due to its MIT licensing, with the scope of use limited only by any dependencies it requires.; Tags unique to grepai: ai, claude-code, cli, code-search; Also covers Developer Tools; When you need to develop a system that prioritizes local processing without cloud dependencies, which is essential in highly regulated environments where data privacy and security are paramount. ### 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 grepai? Not suitable if your application requires real-time updates from cloud-based services, as grepai operates entirely locally without any online components for updating its functionality. If the development team prefers not to deal with C language implementation details,grepai might be less attractive given that other tools may support more modern or higher-level languages like Python. ### Is aquila or grepai more popular on GitHub? grepai has more GitHub stars (1,825 vs 379). Stars measure visibility, not whether either tool fits your constraints. ### Are aquila and grepai open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to aquila or grepai? GraphCanon lists graph-backed alternatives at [aquila alternatives](/tools/aquila-network-aquila/alternatives) and [grepai alternatives](/tools/yoanbernabeu-grepai/alternatives) ([aquila markdown twin](/tools/aquila-network-aquila/alternatives.md), [grepai markdown twin](/tools/yoanbernabeu-grepai/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-yoanbernabeu-grepai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aquila or grepai? aquila: Dormant. grepai: Steady. 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 grepai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aquila trust report](/tools/aquila-network-aquila/trust); [grepai trust report](/tools/yoanbernabeu-grepai/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/_