--- title: "aquila vs edit-mind" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aquila-network-aquila-vs-iliashad-edit-mind" tools: ["aquila-network-aquila", "iliashad-edit-mind"] --- # aquila vs edit-mind *GraphCanon updated Aug 2, 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 edit-mind if edit-mind offers a local-first approach to indexing video libraries with advanced computer vision techniques, allowing for semantic search through natural language queries. [aquila](https://aquila.network) reports 379 GitHub stars, 26 forks, and 13 open issues, last pushed May 6, 2024. [edit-mind](https://edit-mind.com) has 1.8k stars, 121 forks, and 15 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [aquila's repository](https://github.com/Aquila-Network/aquila) and [edit-mind's repository](https://github.com/IliasHad/edit-mind). | | [aquila](/tools/aquila-network-aquila.md) | [edit-mind](/tools/iliashad-edit-mind.md) | | --- | --- | --- | | Tagline | Efficient Neural Search Engine | Local-first Video Knowledge Base with multi-modal analysis for video indexing and semantic search | | Stars | 379 | 1,764 | | Forks | 26 | 121 | | Open issues | 13 | 15 | | Language | HTML | TypeScript | | Adopt for | Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches. | Edit-mind offers a local-first approach to indexing video libraries with advanced computer vision techniques, allowing for semantic search through natural language queries. | | Persona | - | - | | Runtime | - | - | | License | - | Utilizes a specific license named the Edit Mind License, which is documented in the LICENSE.md file. Further details on this licensing must refer to the repository documentation. | | Categories | Data & Retrieval, Vector Databases | Computer Vision, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aquila](/tools/aquila-network-aquila.md) | [edit-mind](/tools/iliashad-edit-mind.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 817d | 31d | | Open issues (now) | 13 | 15 | | Owner type | Organization | User | | Full report | [trust report](/tools/aquila-network-aquila/trust.md) | [trust report](/tools/iliashad-edit-mind/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: edit-mind - **Requirements:** Necessitates Docker for running all components within containers.; Needs manual configuration of Docker file sharing on macOS and Windows systems. - **Adopt for:** Edit-mind offers a local-first approach to indexing video libraries with advanced computer vision techniques, allowing for semantic search through natural language queries. - **License detail:** Utilizes a specific license named the Edit Mind License, which is documented in the LICENSE.md file. Further details on this licensing must refer to the repository documentation. ## Choose when ### Choose aquila if… - aquila is primarily HTML; edit-mind is TypeScript. - Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors. - Also covers Vector Databases. - 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 edit-mind if… - edit-mind is primarily TypeScript; aquila is HTML. - Requirements: Necessitates Docker for running all components within containers.; Needs manual configuration of Docker file sharing on macOS and Windows systems.. - Tags unique to edit-mind: ai, computer-vision, face-recognition, ml. - Also covers Computer Vision. - edit-mind ships Docker support for self-hosted deployment. - The environment requires self-hosted operations without dependence on external servers or APIs. ## 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 edit-mind - The use case requires cloud-based video services or collaborative tools across different locations which a self-hosted solution does not support. - If integration with an existing third-party AI infrastructure is necessary, edit-mind might fall short due to its independence and lack of out-of-the-box integrations. - This tool may not be suitable for users who prefer preconfigured solutions that do not require managing Docker environments or configuring media file sharing. ## Common questions ### What is the difference between aquila and edit-mind? aquila: Efficient Neural Search Engine. edit-mind: Local-first Video Knowledge Base with multi-modal analysis for video indexing and semantic search. See the comparison table for live GitHub stats and shared categories. ### When should I choose aquila over edit-mind? Choose aquila over edit-mind when aquila is primarily HTML; edit-mind is TypeScript; Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors; Also covers Vector Databases; 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 edit-mind over aquila? Choose edit-mind over aquila when edit-mind is primarily TypeScript; aquila is HTML; Requirements: Necessitates Docker for running all components within containers.; Needs manual configuration of Docker file sharing on macOS and Windows systems.; Tags unique to edit-mind: ai, computer-vision, face-recognition, ml; Also covers Computer Vision; edit-mind ships Docker support for self-hosted deployment; The environment requires self-hosted operations without dependence on external servers or APIs. ### 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 edit-mind? The use case requires cloud-based video services or collaborative tools across different locations which a self-hosted solution does not support. If integration with an existing third-party AI infrastructure is necessary, edit-mind might fall short due to its independence and lack of out-of-the-box integrations. This tool may not be suitable for users who prefer preconfigured solutions that do not require managing Docker environments or configuring media file sharing. ### Is aquila or edit-mind more popular on GitHub? edit-mind has more GitHub stars (1,764 vs 379). Stars measure visibility, not whether either tool fits your constraints. ### Are aquila and edit-mind open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to aquila or edit-mind? GraphCanon lists graph-backed alternatives at [aquila alternatives](/tools/aquila-network-aquila/alternatives) and [edit-mind alternatives](/tools/iliashad-edit-mind/alternatives) ([aquila markdown twin](/tools/aquila-network-aquila/alternatives.md), [edit-mind markdown twin](/tools/iliashad-edit-mind/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-iliashad-edit-mind.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aquila or edit-mind? aquila: Dormant. edit-mind: 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 edit-mind? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aquila trust report](/tools/aquila-network-aquila/trust); [edit-mind trust report](/tools/iliashad-edit-mind/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/_