Comparison
deeplake vs llm-app
Verdict
Pick deeplake if deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities; 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 · deeplake alternatives · llm-app alternatives
GraphCanon updated 3d
vs
Trust & integrity
| Signal | deeplake | llm-app |
|---|---|---|
| Maintenance | Steady (87d since push) As of 3d · github_public_v1 | Steady (41d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3d · github_public_v1 | Not a fork · Organization account As of 4d · 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
- deeplake
- AI Data Runtime for Agents with scalable retrieval and training features
- llm-app
- Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.
Stars
- deeplake
- 9.2k
- llm-app
- 59k
Forks
- deeplake
- 721
- llm-app
- 1.5k
Open issues
- deeplake
- 63
- llm-app
- 8
Language
- deeplake
- C++
- llm-app
- Jupyter Notebook
Adopt for
- deeplake
- Deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities.
- 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
- deeplake
- -
- llm-app
- -
Runtime
- deeplake
- -
- llm-app
- -
License
- deeplake
- Deeplake uses the Apache-2.0 license, allowing free use in both open source and commercial projects with attribution.
- llm-app
- MIT
Last pushed
- deeplake
- May 21, 2026
- llm-app
- Jul 5, 2026
Categories
- deeplake
- Data & Retrieval, Model Training, Vector Databases
- llm-app
- Data & Retrieval, LLM Frameworks, Vector Databases
Trust and health
Days since push
- deeplake
- 87d
- llm-app
- 41d
Open issues (now)
- deeplake
- 63
- llm-app
- 8
Stars delta
- deeplake
- +16 (30d)
- llm-app
- +11 (30d)
Open issues delta
- deeplake
- -6 (30d)
- llm-app
- -2 (30d)
Full report
- deeplake
- Trust report
- llm-app
- Trust report
Choose deeplake if…
- deeplake is primarily C++; llm-app is Jupyter Notebook.
- License: deeplake is Apache-2.0, llm-app is MIT.
- Pricing: Pricing details are not specified for Deeplake's public repository..
- Requirements: Deeplake can be installed using pip, making it accessible via the command `pip install deeplake`..
- Tags unique to deeplake: agent, agentic-rag, ai, computer-vision.
- Also covers Model Training.
- When you are developing applications that require seamless integration with AI agents, as Deeplake supports agent-centric design.
When NOT to use deeplake
- If your project does not benefit from an agent-centric architecture and you primarily require traditional database operations without multimodal features.
- When cost control is critical and serverless PostgreSQL might introduce variable costs compared to on-premises solutions for data retrieval and training.
Choose llm-app if…
- llm-app is primarily Jupyter Notebook; deeplake is C++.
- License: llm-app is MIT, deeplake is Apache-2.0.
- 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, retrieval-augmented-generation, vector-database.
- 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 (activeloopai/deeplake) · observed Aug 17, 2026
- GitHub forks (activeloopai/deeplake) · observed Aug 17, 2026
- Last push (activeloopai/deeplake) · observed May 21, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 9, 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: deeplake 9.2k · llm-app 59k (synced Aug 17, 2026).
Common questions
- What is the difference between deeplake and llm-app?
- deeplake: AI Data Runtime for Agents with scalable retrieval and training features. 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 deeplake over llm-app?
- Choose deeplake over llm-app when deeplake is primarily C++; llm-app is Jupyter Notebook; License: deeplake is Apache-2.0, llm-app is MIT; Pricing: Pricing details are not specified for Deeplake's public repository.; Requirements: Deeplake can be installed using pip, making it accessible via the command
pip install deeplake.; Tags unique to deeplake: agent, agentic-rag, ai, computer-vision; Also covers Model Training; When you are developing applications that require seamless integration with AI agents, as Deeplake supports agent-centric design. - When should I choose llm-app over deeplake?
- Choose llm-app over deeplake when llm-app is primarily Jupyter Notebook; deeplake is C++; License: llm-app is MIT, deeplake is Apache-2.0; 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, retrieval-augmented-generation, vector-database; 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 deeplake?
- If your project does not benefit from an agent-centric architecture and you primarily require traditional database operations without multimodal features. When cost control is critical and serverless PostgreSQL might introduce variable costs compared to on-premises solutions for data retrieval and training.
- 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 deeplake or llm-app more popular on GitHub?
- llm-app has more GitHub stars (59,037 vs 9,224). Stars measure visibility, not whether either tool fits your constraints.
- Are deeplake and llm-app open source?
- Yes - both are open-source projects on GitHub (deeplake: Apache-2.0, llm-app: MIT).
- Where can I find alternatives to deeplake or llm-app?
- GraphCanon lists graph-backed alternatives at deeplake alternatives and llm-app alternatives (deeplake 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, deeplake or llm-app?
- deeplake: Steady. 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 deeplake and llm-app?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: deeplake trust report; llm-app trust report.