--- title: "awesome-automl-papers vs weaviate" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hibayesian-awesome-automl-papers-vs-weaviate-weaviate" tools: ["hibayesian-awesome-automl-papers", "weaviate-weaviate"] --- # awesome-automl-papers vs weaviate *GraphCanon updated Jul 11, 2026* ## Verdict Pick awesome-automl-papers when license: awesome-automl-papers is Apache-2.0, weaviate is BSD-3-Clause; pick weaviate when license: weaviate is BSD-3-Clause, awesome-automl-papers is Apache-2.0. [awesome-automl-papers](https://github.com/hibayesian/awesome-automl-papers) reports 4.2k GitHub stars, 680 forks, and 2 open issues, last pushed Jun 11, 2024. [weaviate](https://weaviate.io/developers/weaviate/) has 17k stars, 1.3k forks, and 596 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [awesome-automl-papers's repository](https://github.com/hibayesian/awesome-automl-papers) and [weaviate's repository](https://github.com/weaviate/weaviate). | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [weaviate](/tools/weaviate-weaviate.md) | | --- | --- | --- | | Tagline | A curated list of automated machine learning papers, articles, tutorials, slides and projects | Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a c | | Stars | 4,152 | 16,572 | | Forks | 680 | 1,343 | | Open issues | 2 | 596 | | Language | - | Go | | Adopt for | - | Weaviate is an open-source vector database that supports both object and vector storage with robust deployment options, making it suitable for applications requiring seamless integration of approximate nearest neighbor ( | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | BSD-3-Clause | | Categories | Vector Databases, Computer Vision | Vector Databases, Computer Vision, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [weaviate](/tools/weaviate-weaviate.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 760d | 0d | | Open issues (now) | 2 | 596 | | Owner type | User | Organization | | Security scan | No lockfile | 12 low (12 low) | | Full report | [trust report](/tools/hibayesian-awesome-automl-papers/trust.md) | [trust report](/tools/weaviate-weaviate/trust.md) | ## Decision facts: weaviate - **Requirements:** Requires Docker; Support for Kubernetes, AWS Marketplace, GCP Marketplace; Availability of Python client - **Adopt for:** Weaviate is an open-source vector database that supports both object and vector storage with robust deployment options, making it suitable for applications requiring seamless integration of approximate nearest neighbor ( ## Choose when ### Choose awesome-automl-papers if… - License: awesome-automl-papers is Apache-2.0, weaviate is BSD-3-Clause. - Tags unique to awesome-automl-papers: automl, neural-architecture-search, automated-feature-engineering, hyperparameter-optimization. - Leaner open-issue backlog (2). ### Choose weaviate if… - License: weaviate is BSD-3-Clause, awesome-automl-papers is Apache-2.0. - Requirements: Requires Docker; Support for Kubernetes, AWS Marketplace, GCP Marketplace; Availability of Python client. - Tags unique to weaviate: grpc, information-retrieval, mlops, approximate-nearest-neighbor-search. - Also covers Inference & Serving. - weaviate ships Docker support for self-hosted deployment. - * When you need to integrate vector search capabilities with structured data filtering within a single system. ## When NOT to use awesome-automl-papers - Last GitHub push was 760 days ago (dormant maintenance, Jun 11, 2024). Validate activity before betting a new project on awesome-automl-papers. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use weaviate - * Avoid using when low-level customization of the underlying vector indexing mechanisms is required beyond what current configuration options offer. - * Not recommended if your application does not benefit from cloud-native fault tolerance and scalability features. - * If real-time data import with automatic vector generation through lightweight models is non-essential for your workflow. ## Common questions ### What is the difference between awesome-automl-papers and weaviate? awesome-automl-papers: A curated list of automated machine learning papers, articles, tutorials, slides and projects. weaviate: Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a c. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-automl-papers over weaviate? Choose awesome-automl-papers over weaviate when License: awesome-automl-papers is Apache-2.0, weaviate is BSD-3-Clause; Tags unique to awesome-automl-papers: automl, neural-architecture-search, automated-feature-engineering, hyperparameter-optimization; Leaner open-issue backlog (2). ### When should I choose weaviate over awesome-automl-papers? Choose weaviate over awesome-automl-papers when License: weaviate is BSD-3-Clause, awesome-automl-papers is Apache-2.0; Requirements: Requires Docker; Support for Kubernetes, AWS Marketplace, GCP Marketplace; Availability of Python client; Tags unique to weaviate: grpc, information-retrieval, mlops, approximate-nearest-neighbor-search; Also covers Inference & Serving; weaviate ships Docker support for self-hosted deployment; * When you need to integrate vector search capabilities with structured data filtering within a single system. ### When should I avoid awesome-automl-papers? Last GitHub push was 760 days ago (dormant maintenance, Jun 11, 2024). Validate activity before betting a new project on awesome-automl-papers. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid weaviate? * Avoid using when low-level customization of the underlying vector indexing mechanisms is required beyond what current configuration options offer. * Not recommended if your application does not benefit from cloud-native fault tolerance and scalability features. * If real-time data import with automatic vector generation through lightweight models is non-essential for your workflow. ### Is awesome-automl-papers or weaviate more popular on GitHub? weaviate has more GitHub stars (16,572 vs 4,152). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-automl-papers and weaviate open source? Yes - both are open-source projects on GitHub (awesome-automl-papers: Apache-2.0, weaviate: BSD-3-Clause). ### Where can I find alternatives to awesome-automl-papers or weaviate? GraphCanon lists graph-backed alternatives at [awesome-automl-papers alternatives](/tools/hibayesian-awesome-automl-papers/alternatives) and [weaviate alternatives](/tools/weaviate-weaviate/alternatives) ([awesome-automl-papers markdown twin](/tools/hibayesian-awesome-automl-papers/alternatives.md), [weaviate markdown twin](/tools/weaviate-weaviate/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/hibayesian-awesome-automl-papers-vs-weaviate-weaviate.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-automl-papers or weaviate? awesome-automl-papers: Dormant. weaviate: Very 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 awesome-automl-papers and weaviate? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-automl-papers trust report](/tools/hibayesian-awesome-automl-papers/trust); [weaviate trust report](/tools/weaviate-weaviate/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers`](/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers) - 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/_