--- title: "aquila vs orama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aquila-network-aquila-vs-oramasearch-orama" tools: ["aquila-network-aquila", "oramasearch-orama"] --- # aquila vs orama *GraphCanon updated Aug 21, 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 orama if orama is a compact (<2kb) full-text, vector, and hybrid search engine supporting RAG pipelines that can be deployed in browsers, servers, or edge networks. [aquila](https://aquila.network) reports 379 GitHub stars, 26 forks, and 13 open issues, last pushed May 6, 2024. [orama](https://docs.orama.com/docs/orama-js) has 11k stars, 398 forks, and 21 open issues, last pushed Aug 4, 2026. Figures are from public GitHub metadata via [aquila's repository](https://github.com/Aquila-Network/aquila) and [orama's repository](https://github.com/oramasearch/orama). | | [aquila](/tools/aquila-network-aquila.md) | [orama](/tools/oramasearch-orama.md) | | --- | --- | --- | | Tagline | Efficient Neural Search Engine | A complete search engine and RAG pipeline with support for full-text, vector, and hybrid search. | | Stars | 379 | 10,523 | | Forks | 26 | 398 | | Open issues | 13 | 21 | | 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. | Orama is a compact (<2kb) full-text, vector, and hybrid search engine supporting RAG pipelines that can be deployed in browsers, servers, or edge networks. | | Persona | - | - | | Runtime | - | - | | License | - | Other | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aquila](/tools/aquila-network-aquila.md) | [orama](/tools/oramasearch-orama.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 817d | 17d | | Open issues (now) | 13 | 21 | | Stars delta | Unknown | +26 (30d) | | Open issues delta | Unknown | +3 (30d) | | Full report | [trust report](/tools/aquila-network-aquila/trust.md) | [trust report](/tools/oramasearch-orama/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: orama - **Adopt for:** Orama is a compact (<2kb) full-text, vector, and hybrid search engine supporting RAG pipelines that can be deployed in browsers, servers, or edge networks. ## Choose when ### Choose aquila if… - aquila is primarily HTML; orama is TypeScript. - 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 orama if… - orama is primarily TypeScript; aquila is HTML. - Tags unique to orama: full-text, hybrid-search, search-engine, vector-database-embedding. - - When you need a lightweight (~2kb) solution for integrating robust search capabilities into web applications. ## 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 orama - - If your project demands high-throughput and low-latency text search in large document sets, as others may offer more optimized backend solutions. - - For situations requiring scalability to handle very large datasets; Orama's compact nature might restrict its performance with extensive data. - - In instances where a rich set of administrative tools or built-in storage solutions are necessary, as Orama focuses on lightweight search functionality. ## Common questions ### What is the difference between aquila and orama? aquila: Efficient Neural Search Engine. orama: A complete search engine and RAG pipeline with support for full-text, vector, and hybrid search.. See the comparison table for live GitHub stats and shared categories. ### When should I choose aquila over orama? Choose aquila over orama when aquila is primarily HTML; orama is TypeScript; 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 orama over aquila? Choose orama over aquila when orama is primarily TypeScript; aquila is HTML; Tags unique to orama: full-text, hybrid-search, search-engine, vector-database-embedding; - When you need a lightweight (~2kb) solution for integrating robust search capabilities into web applications. ### 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 orama? - If your project demands high-throughput and low-latency text search in large document sets, as others may offer more optimized backend solutions. - For situations requiring scalability to handle very large datasets; Orama's compact nature might restrict its performance with extensive data. - In instances where a rich set of administrative tools or built-in storage solutions are necessary, as Orama focuses on lightweight search functionality. ### Is aquila or orama more popular on GitHub? orama has more GitHub stars (10,523 vs 379). Stars measure visibility, not whether either tool fits your constraints. ### Are aquila and orama open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to aquila or orama? GraphCanon lists graph-backed alternatives at [aquila alternatives](/tools/aquila-network-aquila/alternatives) and [orama alternatives](/tools/oramasearch-orama/alternatives) ([aquila markdown twin](/tools/aquila-network-aquila/alternatives.md), [orama markdown twin](/tools/oramasearch-orama/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-oramasearch-orama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aquila or orama? aquila: Dormant. orama: 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 aquila and orama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aquila trust report](/tools/aquila-network-aquila/trust); [orama trust report](/tools/oramasearch-orama/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/_