--- title: "amazon-bedrock-samples vs awesome-generative-ai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aws-samples-amazon-bedrock-samples-vs-filipecalegario-awesome-generative-ai" tools: ["aws-samples-amazon-bedrock-samples", "filipecalegario-awesome-generative-ai"] --- # amazon-bedrock-samples vs awesome-generative-ai *GraphCanon updated Aug 22, 2026* ## Verdict Pick amazon-bedrock-samples if amazon-bedrock-samples; pick awesome-generative-ai if awesome-generative-ai offers an extensive directory of resources on generative AI spanning from models to artwork without coding or setup. [amazon-bedrock-samples](https://aws.amazon.com/bedrock/) reports 1.5k GitHub stars, 719 forks, and 133 open issues, last pushed Aug 21, 2026. [awesome-generative-ai](https://github.com/filipecalegario/awesome-generative-ai) has 3.5k stars, 855 forks, and 285 open issues, last pushed Dec 18, 2025. Figures are from public GitHub metadata via [amazon-bedrock-samples's repository](https://github.com/aws-samples/amazon-bedrock-samples) and [awesome-generative-ai's repository](https://github.com/filipecalegario/awesome-generative-ai). | | [amazon-bedrock-samples](/tools/aws-samples-amazon-bedrock-samples.md) | [awesome-generative-ai](/tools/filipecalegario-awesome-generative-ai.md) | | --- | --- | --- | | Tagline | Examples for using Amazon Bedrock Service including embedding and generative AI models | A comprehensive list of generative AI resources | | Stars | 1,493 | 3,524 | | Forks | 719 | 855 | | Open issues | 133 | 285 | | Language | Jupyter Notebook | - | | Adopt for | amazon-bedrock-samples | awesome-generative-ai offers an extensive directory of resources on generative AI spanning from models to artwork without coding or setup. | | Persona | - | - | | Runtime | - | - | | License | Licensed under MIT-0, allowing for use and distribution without attribution but with no warranties. | CC0-1.0 - public domain with no attribution required, ideal for broad distribution and integration in any project without legal constraints. | | Categories | Data & Retrieval, LLM Frameworks | AI Agents, Computer Vision, Data & Retrieval, Developer Tools, LLM Frameworks, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [amazon-bedrock-samples](/tools/aws-samples-amazon-bedrock-samples.md) | [awesome-generative-ai](/tools/filipecalegario-awesome-generative-ai.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 1d | 246d | | Open issues (now) | 133 | 285 | | Open issues delta | +3 (30d) | +24 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/aws-samples-amazon-bedrock-samples/trust.md) | [trust report](/tools/filipecalegario-awesome-generative-ai/trust.md) | ## Decision facts: amazon-bedrock-samples - **Requirements:** Development environment must support Jupyter Notebooks to utilize the repository content effectively. - **Adopt for:** amazon-bedrock-samples - **License detail:** Licensed under MIT-0, allowing for use and distribution without attribution but with no warranties. ## Decision facts: awesome-generative-ai - **Adopt for:** awesome-generative-ai offers an extensive directory of resources on generative AI spanning from models to artwork without coding or setup. - **License detail:** CC0-1.0 - public domain with no attribution required, ideal for broad distribution and integration in any project without legal constraints. ## Choose when ### Choose amazon-bedrock-samples if… - License: amazon-bedrock-samples is MIT-0, awesome-generative-ai is CC0-1.0. - Requirements: Development environment must support Jupyter Notebooks to utilize the repository content effectively.. - Tags unique to amazon-bedrock-samples: amazon-bedrock, amazon-titan, knowledge-base, langchain. - When you need starter code samples for interacting with Amazon Bedrock Service foundational models in Jupyter Notebooks. ### Choose awesome-generative-ai if… - License: awesome-generative-ai is CC0-1.0, amazon-bedrock-samples is MIT-0. - Tags unique to awesome-generative-ai: ai-art, awesome-list, chatgpt, dall-e. - Also covers AI Agents, Computer Vision, Developer Tools, Speech & Audio. - You want a curated list covering a broad range of generative AI tools and models. ## When NOT to use amazon-bedrock-samples - If you are looking for samples or frameworks not hosted in Jupyter Notebook format, as this may require manual conversion or scripting. - When your project's requirements do not include using Amazon Bedrock Service models, favoring other cloud service providers' foundational models instead. - For scenarios involving proprietary or closed-source AI model integrations incompatible with the MIT-0 license under which these samples are available. ## When NOT to use awesome-generative-ai - Seeking direct tool functionality or hands-on code implementation support. - Looking for resources focused on specific frameworks like TensorFlow or PyTorch exclusively. ## Common questions ### What is the difference between amazon-bedrock-samples and awesome-generative-ai? amazon-bedrock-samples: Examples for using Amazon Bedrock Service including embedding and generative AI models. awesome-generative-ai: A comprehensive list of generative AI resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose amazon-bedrock-samples over awesome-generative-ai? Choose amazon-bedrock-samples over awesome-generative-ai when License: amazon-bedrock-samples is MIT-0, awesome-generative-ai is CC0-1.0; Requirements: Development environment must support Jupyter Notebooks to utilize the repository content effectively.; Tags unique to amazon-bedrock-samples: amazon-bedrock, amazon-titan, knowledge-base, langchain; When you need starter code samples for interacting with Amazon Bedrock Service foundational models in Jupyter Notebooks. ### When should I choose awesome-generative-ai over amazon-bedrock-samples? Choose awesome-generative-ai over amazon-bedrock-samples when License: awesome-generative-ai is CC0-1.0, amazon-bedrock-samples is MIT-0; Tags unique to awesome-generative-ai: ai-art, awesome-list, chatgpt, dall-e; Also covers AI Agents, Computer Vision, Developer Tools, Speech & Audio; You want a curated list covering a broad range of generative AI tools and models. ### When should I avoid amazon-bedrock-samples? If you are looking for samples or frameworks not hosted in Jupyter Notebook format, as this may require manual conversion or scripting. When your project's requirements do not include using Amazon Bedrock Service models, favoring other cloud service providers' foundational models instead. For scenarios involving proprietary or closed-source AI model integrations incompatible with the MIT-0 license under which these samples are available. ### When should I avoid awesome-generative-ai? Seeking direct tool functionality or hands-on code implementation support. Looking for resources focused on specific frameworks like TensorFlow or PyTorch exclusively. ### Is amazon-bedrock-samples or awesome-generative-ai more popular on GitHub? awesome-generative-ai has more GitHub stars (3,524 vs 1,493). Stars measure visibility, not whether either tool fits your constraints. ### Are amazon-bedrock-samples and awesome-generative-ai open source? Yes - both are open-source projects on GitHub (amazon-bedrock-samples: MIT-0, awesome-generative-ai: CC0-1.0). ### Where can I find alternatives to amazon-bedrock-samples or awesome-generative-ai? GraphCanon lists graph-backed alternatives at [amazon-bedrock-samples alternatives](/tools/aws-samples-amazon-bedrock-samples/alternatives) and [awesome-generative-ai alternatives](/tools/filipecalegario-awesome-generative-ai/alternatives) ([amazon-bedrock-samples markdown twin](/tools/aws-samples-amazon-bedrock-samples/alternatives.md), [awesome-generative-ai markdown twin](/tools/filipecalegario-awesome-generative-ai/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/aws-samples-amazon-bedrock-samples-vs-filipecalegario-awesome-generative-ai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, amazon-bedrock-samples or awesome-generative-ai? amazon-bedrock-samples: Very active. awesome-generative-ai: Slowing. 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 amazon-bedrock-samples and awesome-generative-ai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [amazon-bedrock-samples trust report](/tools/aws-samples-amazon-bedrock-samples/trust); [awesome-generative-ai trust report](/tools/filipecalegario-awesome-generative-ai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aws-samples-amazon-bedrock-samples`](/api/graphcanon/graph?tool=aws-samples-amazon-bedrock-samples) - 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/_