Comparison
awesome-ai-apps vs semantic-kernel
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 semantic-kernel if semantic-kernel is a framework for integrating large language models into applications using C#, Python, Java, and .NET, offering flexibility across multiple programming environments.
Markdown twin · awesome-ai-apps alternatives · semantic-kernel alternatives
GraphCanon updated Sep 20, 2026
14views this month
vs
Trust & integrity
| Signal | awesome-ai-apps | semantic-kernel |
|---|---|---|
| Maintenance | Very active (1d since push) As of Sep 20, 2026 · github_public_v1 | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 2026 · 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
- awesome-ai-apps
- A curated list of AI applications showcasing RAG, agents, and workflows.
- semantic-kernel
- Integrate cutting-edge LLM technology quickly and easily into your apps
Stars
- awesome-ai-apps
- 16k
- semantic-kernel
- 29k
Forks
- awesome-ai-apps
- 1.8k
- semantic-kernel
- 4.8k
Open issues
- awesome-ai-apps
- 65
- semantic-kernel
- 304
Language
- awesome-ai-apps
- Python
- semantic-kernel
- C#
Adopt for
- awesome-ai-apps
- 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.
- semantic-kernel
- Semantic-kernel is a framework for integrating large language models into applications using C#, Python, Java, and .NET, offering flexibility across multiple programming environments.
Persona
- awesome-ai-apps
- -
- semantic-kernel
- -
Runtime
- awesome-ai-apps
- -
- semantic-kernel
- -
License
- awesome-ai-apps
- MIT License ensures easy integration into both open source and proprietary projects without restrictions.
- semantic-kernel
- Semantic-kernel is licensed under the MIT license, allowing for free use, modification, and distribution of the software.
Last pushed
- awesome-ai-apps
- Sep 18, 2026
- semantic-kernel
- Sep 18, 2026
Categories
- awesome-ai-apps
- AI Agents, LLM Frameworks
- semantic-kernel
- AI Agents, LLM Frameworks
Trust and health
Days since push
- awesome-ai-apps
- 1d
- semantic-kernel
- 0d
Open issues (now)
- awesome-ai-apps
- 65
- semantic-kernel
- 304
Stars delta
- awesome-ai-apps
- +2.4k (30d)
- semantic-kernel
- +148 (30d)
Open issues delta
- awesome-ai-apps
- -24 (30d)
- semantic-kernel
- +48 (30d)
Owner type
- awesome-ai-apps
- User
- semantic-kernel
- Organization
Full report
- awesome-ai-apps
- Trust report
- semantic-kernel
- Trust report
Shared compatibility
- Python · awesome-ai-apps: Python runtime · semantic-kernel: Python runtime
Choose awesome-ai-apps if…
- awesome-ai-apps is primarily Python; semantic-kernel is C#.
- 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, hacktoberfest, mcp.
- 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 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.
Choose semantic-kernel if…
- semantic-kernel is primarily C#; awesome-ai-apps is Python.
- Requirements: System requirements include Python 3.10+, .NET 10.0+, and JDK 17+.; Operating system support includes Windows, macOS, and Linux..
- Tags unique to semantic-kernel: artificial-intelligence, azure-openai, csharp, openai.
- When you need to integrate large language models into applications using C#, Python, Java, or .NET, as semantic-kernel supports these languages natively.
When NOT to use semantic-kernel
- If your project is strictly limited to a single programming language that is not C#, Python, Java, or.NET, as semantic-kernel does not support other languages.
- When your application must exclusively use a specific AI service provider that is not Azure OpenAI or OpenAI, as semantic-kernel's primary integration is with these services.
- If your development environment is not Windows, macOS, or Linux, as semantic-kernel does not support other operating systems.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Arindam200/awesome-ai-apps) · observed Sep 20, 2026
- GitHub forks (Arindam200/awesome-ai-apps) · observed Sep 20, 2026
- Last push (Arindam200/awesome-ai-apps) · observed Sep 18, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (microsoft/semantic-kernel) · observed Sep 20, 2026
- GitHub forks (microsoft/semantic-kernel) · observed Sep 20, 2026
- Last push (microsoft/semantic-kernel) · observed Sep 18, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: awesome-ai-apps 16k · semantic-kernel 29k (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-ai-apps and semantic-kernel?
- awesome-ai-apps: A curated list of AI applications showcasing RAG, agents, and workflows.. semantic-kernel: Integrate cutting-edge LLM technology quickly and easily into your apps. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-ai-apps over semantic-kernel?
- Choose awesome-ai-apps over semantic-kernel when awesome-ai-apps is primarily Python; semantic-kernel is C#; 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, hacktoberfest, mcp; 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 semantic-kernel over awesome-ai-apps?
- Choose semantic-kernel over awesome-ai-apps when semantic-kernel is primarily C#; awesome-ai-apps is Python; Requirements: System requirements include Python 3.10+, .NET 10.0+, and JDK 17+.; Operating system support includes Windows, macOS, and Linux.; Tags unique to semantic-kernel: artificial-intelligence, azure-openai, csharp, openai; When you need to integrate large language models into applications using C#, Python, Java, or .NET, as semantic-kernel supports these languages natively.
- 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 semantic-kernel?
- If your project is strictly limited to a single programming language that is not C#, Python, Java, or.NET, as semantic-kernel does not support other languages. When your application must exclusively use a specific AI service provider that is not Azure OpenAI or OpenAI, as semantic-kernel's primary integration is with these services. If your development environment is not Windows, macOS, or Linux, as semantic-kernel does not support other operating systems.
- Is awesome-ai-apps or semantic-kernel more popular on GitHub?
- semantic-kernel has more GitHub stars (28,575 vs 15,671). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-ai-apps and semantic-kernel open source?
- Yes - both are open-source projects on GitHub (awesome-ai-apps: MIT, semantic-kernel: MIT).
- Where can I find alternatives to awesome-ai-apps or semantic-kernel?
- GraphCanon lists graph-backed alternatives at awesome-ai-apps alternatives and semantic-kernel alternatives (awesome-ai-apps markdown twin, semantic-kernel 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, awesome-ai-apps or semantic-kernel?
- awesome-ai-apps: Very active. semantic-kernel: 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 semantic-kernel?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-ai-apps trust report; semantic-kernel trust report.