--- title: "EmbedAnything vs weaviate-examples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/starlightsearch-embedanything-vs-weaviate-weaviate-examples" tools: ["starlightsearch-embedanything", "weaviate-weaviate-examples"] --- # EmbedAnything vs weaviate-examples *GraphCanon updated Aug 23, 2026* ## Verdict Pick EmbedAnything if embedAnything is a Rust-based tool focused on highly performant and modular operations for inference, ingestion, and indexing of large language models, designed with memory safety and production-readiness in mind; pick weaviate-examples if weaviate-examples aids developers by providing practical usage scenarios for Weaviate vector database, optimizing deep learning applications. [EmbedAnything](https://embed-anything.com/) reports 1.3k GitHub stars, 143 forks, and 21 open issues, last pushed Aug 12, 2026. [weaviate-examples](https://github.com/weaviate/weaviate-examples) has 331 stars, 86 forks, and 12 open issues, last pushed Aug 7, 2025. Figures are from public GitHub metadata via [EmbedAnything's repository](https://github.com/StarlightSearch/EmbedAnything) and [weaviate-examples's repository](https://github.com/weaviate/weaviate-examples). | | [EmbedAnything](/tools/starlightsearch-embedanything.md) | [weaviate-examples](/tools/weaviate-weaviate-examples.md) | | --- | --- | --- | | Tagline | Highly Performant, Modular, Memory Safe and Production-ready Inference, Ingestion and Indexing built in Rust | Weaviate vector database – examples | | Stars | 1,304 | 331 | | Forks | 143 | 86 | | Open issues | 21 | 12 | | Language | Rust | HTML | | Adopt for | EmbedAnything is a Rust-based tool focused on highly performant and modular operations for inference, ingestion, and indexing of large language models, designed with memory safety and production-readiness in mind. | weaviate-examples aids developers by providing practical usage scenarios for Weaviate vector database, optimizing deep learning applications. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Data & Retrieval, Inference & Serving, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [EmbedAnything](/tools/starlightsearch-embedanything.md) | [weaviate-examples](/tools/weaviate-weaviate-examples.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 9d | 380d | | Open issues (now) | 21 | 12 | | Stars delta | +18 (30d) | -1 (30d) | | Open issues delta | -2 (30d) | 0 (30d) | | Full report | [trust report](/tools/starlightsearch-embedanything/trust.md) | [trust report](/tools/weaviate-weaviate-examples/trust.md) | ## Shared compatibility - **Python**: [EmbedAnything](/tools/starlightsearch-embedanything.md) - Python runtime; [weaviate-examples](/tools/weaviate-weaviate-examples.md) - Python runtime ## Decision facts: EmbedAnything - **Adopt for:** EmbedAnything is a Rust-based tool focused on highly performant and modular operations for inference, ingestion, and indexing of large language models, designed with memory safety and production-readiness in mind. ## Decision facts: weaviate-examples - **Adopt for:** weaviate-examples aids developers by providing practical usage scenarios for Weaviate vector database, optimizing deep learning applications. ## Choose when ### Choose EmbedAnything if… - EmbedAnything is primarily Rust; weaviate-examples is HTML. - License: EmbedAnything is Apache-2.0, weaviate-examples is MIT. - Tags unique to EmbedAnything: ai, cloud, generative-ai, hacktoberfest. - Also covers Inference & Serving. - EmbedAnything ships Docker support for self-hosted deployment. - - When you require high performance and memory safety for inference tasks due to its Rust foundation. ### Choose weaviate-examples if… - weaviate-examples is primarily HTML; EmbedAnything is Rust. - License: weaviate-examples is MIT, EmbedAnything is Apache-2.0. - Tags unique to weaviate-examples: deep-learning, examples, vector-database, vector-search. - You aim to integrate vector search capabilities into your deep-learning projects and need hands-on examples to understand functionality. ## When NOT to use EmbedAnything - - In scenarios requiring direct Python support without additional bridging tools, since EmbedAnything's primary language is Rust. - - If you need a tool heavily optimized for edge computing where minimal memory usage trumps safety and performance considerations. ## When NOT to use weaviate-examples - Your project utilizes a different vector database that aligns better with its specific requirements, such as more customizability in indexing. - You seek general tutorial material on deep learning without the context of Weaviate's implementation specifics. ## Common questions ### What is the difference between EmbedAnything and weaviate-examples? EmbedAnything: Highly Performant, Modular, Memory Safe and Production-ready Inference, Ingestion and Indexing built in Rust. weaviate-examples: Weaviate vector database – examples. See the comparison table for live GitHub stats and shared categories. ### When should I choose EmbedAnything over weaviate-examples? Choose EmbedAnything over weaviate-examples when EmbedAnything is primarily Rust; weaviate-examples is HTML; License: EmbedAnything is Apache-2.0, weaviate-examples is MIT; Tags unique to EmbedAnything: ai, cloud, generative-ai, hacktoberfest; Also covers Inference & Serving; EmbedAnything ships Docker support for self-hosted deployment; - When you require high performance and memory safety for inference tasks due to its Rust foundation. ### When should I choose weaviate-examples over EmbedAnything? Choose weaviate-examples over EmbedAnything when weaviate-examples is primarily HTML; EmbedAnything is Rust; License: weaviate-examples is MIT, EmbedAnything is Apache-2.0; Tags unique to weaviate-examples: deep-learning, examples, vector-database, vector-search; You aim to integrate vector search capabilities into your deep-learning projects and need hands-on examples to understand functionality. ### When should I avoid EmbedAnything? - In scenarios requiring direct Python support without additional bridging tools, since EmbedAnything's primary language is Rust. - If you need a tool heavily optimized for edge computing where minimal memory usage trumps safety and performance considerations. ### When should I avoid weaviate-examples? Your project utilizes a different vector database that aligns better with its specific requirements, such as more customizability in indexing. You seek general tutorial material on deep learning without the context of Weaviate's implementation specifics. ### Is EmbedAnything or weaviate-examples more popular on GitHub? EmbedAnything has more GitHub stars (1,304 vs 331). Stars measure visibility, not whether either tool fits your constraints. ### Are EmbedAnything and weaviate-examples open source? Yes - both are open-source projects on GitHub (EmbedAnything: Apache-2.0, weaviate-examples: MIT). ### Where can I find alternatives to EmbedAnything or weaviate-examples? GraphCanon lists graph-backed alternatives at [EmbedAnything alternatives](/tools/starlightsearch-embedanything/alternatives) and [weaviate-examples alternatives](/tools/weaviate-weaviate-examples/alternatives) ([EmbedAnything markdown twin](/tools/starlightsearch-embedanything/alternatives.md), [weaviate-examples markdown twin](/tools/weaviate-weaviate-examples/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/starlightsearch-embedanything-vs-weaviate-weaviate-examples.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, EmbedAnything or weaviate-examples? EmbedAnything: Active. weaviate-examples: Dormant. 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 EmbedAnything and weaviate-examples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [EmbedAnything trust report](/tools/starlightsearch-embedanything/trust); [weaviate-examples trust report](/tools/weaviate-weaviate-examples/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=starlightsearch-embedanything`](/api/graphcanon/graph?tool=starlightsearch-embedanything) - 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/_