Comparison
aquila vs llm-app
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 llm-app if llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz.
Markdown twin · aquila alternatives · llm-app alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | aquila | llm-app |
|---|---|---|
| Maintenance | Dormant (817d since push) As of 3w · github_public_v1 | Steady (41d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 1w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- Efficient Neural Search Engine
- llm-app
- Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.
Stars
- aquila
- 379
- llm-app
- 59k
Forks
- aquila
- 26
- llm-app
- 1.5k
Open issues
- aquila
- 13
- llm-app
- 8
Language
- aquila
- HTML
- llm-app
- Jupyter Notebook
Adopt for
- aquila
- Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches.
- llm-app
- llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz
Persona
- aquila
- -
- llm-app
- -
Runtime
- aquila
- -
- llm-app
- -
License
- aquila
- -
- llm-app
- MIT
Last pushed
- aquila
- May 6, 2024
- llm-app
- Jul 5, 2026
Categories
- aquila
- Data & Retrieval, Vector Databases
- llm-app
- Data & Retrieval, LLM Frameworks, Vector Databases
Trust and health
Maintenance
- aquila
- Dormant (18%)
- llm-app
- Steady (60%)
Days since push
- aquila
- 817d
- llm-app
- 41d
Open issues (now)
- aquila
- 13
- llm-app
- 8
Stars delta
- aquila
- Unknown
- llm-app
- +11 (30d)
Open issues delta
- aquila
- Unknown
- llm-app
- -2 (30d)
Full report
- aquila
- Trust report
- llm-app
- Trust report
Choose aquila if…
- aquila is primarily HTML; llm-app is Jupyter Notebook.
- 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 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
Choose llm-app if…
- llm-app is primarily Jupyter Notebook; aquila is HTML.
- Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others..
- Tags unique to llm-app: chatbot, hugging-face, llm, retrieval-augmented-generation.
- Also covers LLM Frameworks.
- - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs.
When NOT to use llm-app
- - You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app.
- - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app.
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 Aug 2, 2026
- GitHub forks (Aquila-Network/aquila) · observed Aug 2, 2026
- Last push (Aquila-Network/aquila) · observed May 6, 2024
- License file (unknown) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (pathwaycom/llm-app) · observed Aug 16, 2026
- GitHub forks (pathwaycom/llm-app) · observed Aug 16, 2026
- Last push (pathwaycom/llm-app) · observed Jul 5, 2026
- License file (MIT) · observed Aug 16, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: aquila 379 · llm-app 59k (synced Aug 2, 2026).
Common questions
- What is the difference between aquila and llm-app?
- aquila: Efficient Neural Search Engine. llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. See the comparison table for live GitHub stats and shared categories.
- When should I choose aquila over llm-app?
- Choose aquila over llm-app when aquila is primarily HTML; llm-app is Jupyter Notebook; 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 llm-app over aquila?
- Choose llm-app over aquila when llm-app is primarily Jupyter Notebook; aquila is HTML; Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.; Tags unique to llm-app: chatbot, hugging-face, llm, retrieval-augmented-generation; Also covers LLM Frameworks; - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live 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 llm-app?
- - You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app.
- Is aquila or llm-app more popular on GitHub?
- llm-app has more GitHub stars (59,037 vs 379). Stars measure visibility, not whether either tool fits your constraints.
- Are aquila and llm-app open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to aquila or llm-app?
- GraphCanon lists graph-backed alternatives at aquila alternatives and llm-app alternatives (aquila markdown twin, llm-app 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 llm-app?
- aquila: Dormant. llm-app: 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 llm-app?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: aquila trust report; llm-app trust report.