Comparison
aquila vs orama
Verdict
Pick aquila if aquila is a neural search engine that indexes latent vectors with JSON metadata, enabling efficient k-NN search operations; 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.
Markdown twin · aquila alternatives · orama alternatives
GraphCanon updated Sep 20, 2026
11views this month
Trust & integrity
| Signal | aquila | orama |
|---|---|---|
| Maintenance | Dormant (864d since push) As of Sep 18, 2026 · github_public_v1 | Active (9d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- aquila
- An easy-to-use Neural Search Engine for indexing latent vectors with JSON metadata and performing efficient k-NN search.
- orama
- A complete search engine and RAG pipeline with support for full-text, vector, and hybrid search.
Stars
- aquila
- 379
- orama
- 11k
Forks
- aquila
- 26
- orama
- 402
Open issues
- aquila
- 13
- orama
- 23
Language
- aquila
- HTML
- orama
- TypeScript
Adopt for
- aquila
- Aquila is a neural search engine that indexes latent vectors with JSON metadata, enabling efficient k-NN search operations.
- orama
- 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
- aquila
- -
- orama
- -
Runtime
- aquila
- -
- orama
- -
License
- aquila
- Aquila is licensed under the Apache License 2.0.
- orama
- Other
Last pushed
- aquila
- May 6, 2024
- orama
- Sep 11, 2026
Categories
- aquila
- Data & Retrieval, Vector Databases
- orama
- Data & Retrieval, Vector Databases
Trust and health
Maintenance
- aquila
- Dormant (18%)
- orama
- Active (82%)
Days since push
- aquila
- 864d
- orama
- 9d
Open issues (now)
- aquila
- 13
- orama
- 23
Stars delta
- aquila
- 0 (30d)
- orama
- +60 (30d)
Open issues delta
- aquila
- 0 (30d)
- orama
- +5 (30d)
Full report
- aquila
- Trust report
- orama
- Trust report
Choose aquila if…
- aquila is primarily HTML; orama is TypeScript.
- Requirements: Requires Docker; Aquila requires Docker for deployment, with options for both lite and big data versions..
- Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors.
- When you need to perform efficient k-NN search operations on latent vectors with associated JSON metadata.
When NOT to use aquila
- If your project requires a specific license that is not Apache License 2.0, Aquila may not be suitable.
- If your application does not require indexing of latent vectors with JSON metadata, Aquila might be overkill.
- If you are looking for a solution that does not require Docker for deployment, Aquila might not be the best fit.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Aquila-Network/aquila) · observed Sep 20, 2026
- GitHub forks (Aquila-Network/aquila) · observed Sep 20, 2026
- Last push (Aquila-Network/aquila) · observed May 6, 2024
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (oramasearch/orama) · observed Sep 20, 2026
- GitHub forks (oramasearch/orama) · observed Sep 20, 2026
- Last push (oramasearch/orama) · observed Sep 11, 2026
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: aquila 379 · orama 11k (synced Sep 20, 2026).
Common questions
- What is the difference between aquila and orama?
- aquila: An easy-to-use Neural Search Engine for indexing latent vectors with JSON metadata and performing efficient k-NN search.. 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; Requirements: Requires Docker; Aquila requires Docker for deployment, with options for both lite and big data versions.; Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors; When you need to perform efficient k-NN search operations on latent vectors with associated JSON metadata.
- 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 your project requires a specific license that is not Apache License 2.0, Aquila may not be suitable. If your application does not require indexing of latent vectors with JSON metadata, Aquila might be overkill. If you are looking for a solution that does not require Docker for deployment, Aquila might not be the best fit.
- 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 (10,557 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 and orama alternatives (aquila markdown twin, orama markdown twin), 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 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; orama trust report.