--- title: "awesome-ai-apps vs amazon-bedrock-samples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/arindam200-awesome-ai-apps-vs-aws-samples-amazon-bedrock-samples" tools: ["arindam200-awesome-ai-apps", "aws-samples-amazon-bedrock-samples"] --- # awesome-ai-apps vs amazon-bedrock-samples *GraphCanon updated Aug 26, 2026* ## Verdict Pick awesome-ai-apps if awesome-ai-apps is a curated list of projects focusing on AI applications and innovations such as RAG technologies, AI agents, and workflows, emphasizing large language models using Python; pick amazon-bedrock-samples if amazon-bedrock-samples. [awesome-ai-apps](https://dub.sh/nebius) reports 13k GitHub stars, 1.8k forks, and 65 open issues, last pushed Aug 19, 2026. [amazon-bedrock-samples](https://aws.amazon.com/bedrock/) has 1.5k stars, 719 forks, and 133 open issues, last pushed Aug 21, 2026. Figures are from public GitHub metadata via [awesome-ai-apps's repository](https://github.com/Arindam200/awesome-ai-apps) and [amazon-bedrock-samples's repository](https://github.com/aws-samples/amazon-bedrock-samples). | | [awesome-ai-apps](/tools/arindam200-awesome-ai-apps.md) | [amazon-bedrock-samples](/tools/aws-samples-amazon-bedrock-samples.md) | | --- | --- | --- | | Tagline | A curated list of AI applications showcasing RAG, agents, and workflows. | Examples for using Amazon Bedrock Service including embedding and generative AI models | | Stars | 13,494 | 1,493 | | Forks | 1,760 | 719 | | Open issues | 65 | 133 | | Language | Python | Jupyter Notebook | | Adopt for | awesome-ai-apps is a curated list of projects focusing on AI applications and innovations such as RAG technologies, AI agents, and workflows, emphasizing large language models using Python. | amazon-bedrock-samples | | Persona | - | - | | Runtime | - | - | | License | MIT License ensures easy integration into both open source and proprietary projects without restrictions. | Licensed under MIT-0, allowing for use and distribution without attribution but with no warranties. | | Categories | AI Agents, LLM Frameworks | Data & Retrieval, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-ai-apps](/tools/arindam200-awesome-ai-apps.md) | [amazon-bedrock-samples](/tools/aws-samples-amazon-bedrock-samples.md) | | --- | --- | --- | | Days since push | 6d | 1d | | Open issues (now) | 65 | 133 | | Stars delta | +226 (30d) | +16 (30d) | | Open issues delta | -24 (30d) | +3 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/arindam200-awesome-ai-apps/trust.md) | [trust report](/tools/aws-samples-amazon-bedrock-samples/trust.md) | ## Decision facts: awesome-ai-apps - **Pricing:** freemium - As an open-source project under the MIT License, awesome-ai-apps is free to use. There are no paid plans beyond potential third-party service integrations or support contracts. - **Requirements:** Requires understanding of Python and familiarity with large language models and RAG technologies to benefit fully from the projects listed. - **Adopt for:** awesome-ai-apps is a curated list of projects focusing on AI applications and innovations such as RAG technologies, AI agents, and workflows, emphasizing large language models using Python. - **License detail:** MIT License ensures easy integration into both open source and proprietary projects without restrictions. ## 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. ## Choose when ### Choose awesome-ai-apps if… - awesome-ai-apps is primarily Python; amazon-bedrock-samples is Jupyter Notebook. - License: awesome-ai-apps is MIT, amazon-bedrock-samples is MIT-0. - Pricing: As an open-source project under the MIT License, awesome-ai-apps is free to use. There are no paid plans beyond potential third-party service integrations or support contracts.. - Requirements: Requires understanding of Python and familiarity with large language models and RAG technologies to benefit fully from the projects listed.. - Tags unique to awesome-ai-apps: agents, ai, hacktoberfest, llm. - Also covers AI Agents. - Use awesome-ai-apps when looking to explore or implement Retrieval-Augmented Generation (RAG) in Python projects focused on enhancing search-based question answering. ### Choose amazon-bedrock-samples if… - amazon-bedrock-samples is primarily Jupyter Notebook; awesome-ai-apps is Python. - License: amazon-bedrock-samples is MIT-0, awesome-ai-apps is MIT. - Requirements: Development environment must support Jupyter Notebooks to utilize the repository content effectively.. - Tags unique to amazon-bedrock-samples: amazon-bedrock, amazon-titan, embeddings, generative-ai. - Also covers Data & Retrieval. - When you need starter code samples for interacting with Amazon Bedrock Service foundational models in Jupyter Notebooks. ## When NOT to use awesome-ai-apps - Avoid awesome-ai-apps if your project requires non-Python support, as all the included applications are built using Python. - Do not use this repository if your focus is on backend-only AI services that do not involve RAG technologies or AI agents. ## 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. ## Common questions ### What is the difference between awesome-ai-apps and amazon-bedrock-samples? awesome-ai-apps: A curated list of AI applications showcasing RAG, agents, and workflows.. amazon-bedrock-samples: Examples for using Amazon Bedrock Service including embedding and generative AI models. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-ai-apps over amazon-bedrock-samples? Choose awesome-ai-apps over amazon-bedrock-samples when awesome-ai-apps is primarily Python; amazon-bedrock-samples is Jupyter Notebook; License: awesome-ai-apps is MIT, amazon-bedrock-samples is MIT-0; Pricing: As an open-source project under the MIT License, awesome-ai-apps is free to use. There are no paid plans beyond potential third-party service integrations or support contracts.; Requirements: Requires understanding of Python and familiarity with large language models and RAG technologies to benefit fully from the projects listed.; Tags unique to awesome-ai-apps: agents, ai, hacktoberfest, llm; Also covers AI Agents; Use awesome-ai-apps when looking to explore or implement Retrieval-Augmented Generation (RAG) in Python projects focused on enhancing search-based question answering. ### When should I choose amazon-bedrock-samples over awesome-ai-apps? Choose amazon-bedrock-samples over awesome-ai-apps when amazon-bedrock-samples is primarily Jupyter Notebook; awesome-ai-apps is Python; License: amazon-bedrock-samples is MIT-0, awesome-ai-apps is MIT; Requirements: Development environment must support Jupyter Notebooks to utilize the repository content effectively.; Tags unique to amazon-bedrock-samples: amazon-bedrock, amazon-titan, embeddings, generative-ai; Also covers Data & Retrieval; When you need starter code samples for interacting with Amazon Bedrock Service foundational models in Jupyter Notebooks. ### When should I avoid awesome-ai-apps? Avoid awesome-ai-apps if your project requires non-Python support, as all the included applications are built using Python. Do not use this repository if your focus is on backend-only AI services that do not involve RAG technologies or AI agents. ### 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. ### Is awesome-ai-apps or amazon-bedrock-samples more popular on GitHub? awesome-ai-apps has more GitHub stars (13,494 vs 1,493). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-ai-apps and amazon-bedrock-samples open source? Yes - both are open-source projects on GitHub (awesome-ai-apps: MIT, amazon-bedrock-samples: MIT-0). ### Where can I find alternatives to awesome-ai-apps or amazon-bedrock-samples? GraphCanon lists graph-backed alternatives at [awesome-ai-apps alternatives](/tools/arindam200-awesome-ai-apps/alternatives) and [amazon-bedrock-samples alternatives](/tools/aws-samples-amazon-bedrock-samples/alternatives) ([awesome-ai-apps markdown twin](/tools/arindam200-awesome-ai-apps/alternatives.md), [amazon-bedrock-samples markdown twin](/tools/aws-samples-amazon-bedrock-samples/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/arindam200-awesome-ai-apps-vs-aws-samples-amazon-bedrock-samples.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-ai-apps or amazon-bedrock-samples? awesome-ai-apps: Very active. amazon-bedrock-samples: 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-ai-apps and amazon-bedrock-samples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-ai-apps trust report](/tools/arindam200-awesome-ai-apps/trust); [amazon-bedrock-samples trust report](/tools/aws-samples-amazon-bedrock-samples/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=arindam200-awesome-ai-apps`](/api/graphcanon/graph?tool=arindam200-awesome-ai-apps) - 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/_