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 toolkit for integrating language model technologies into applications, supporting C#, .NET, Python, and Java.
Markdown twin · awesome-ai-apps alternatives · semantic-kernel alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | awesome-ai-apps | semantic-kernel |
|---|---|---|
| Maintenance | Very active (2d since push) As of 3w · github_public_v1 | Very active (1d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization account As of 1w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- 13k
- semantic-kernel
- 28k
Forks
- awesome-ai-apps
- 1.7k
- semantic-kernel
- 4.7k
Open issues
- awesome-ai-apps
- 89
- semantic-kernel
- 256
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 toolkit for integrating language model technologies into applications, supporting C#, .NET, Python, and Java.
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
- MIT
Last pushed
- awesome-ai-apps
- Jul 23, 2026
- semantic-kernel
- Aug 6, 2026
Categories
- awesome-ai-apps
- AI Agents, LLM Frameworks
- semantic-kernel
- AI Agents, LLM Frameworks
Trust and health
Days since push
- awesome-ai-apps
- 2d
- semantic-kernel
- 1d
Open issues (now)
- awesome-ai-apps
- 89
- semantic-kernel
- 256
Stars delta
- awesome-ai-apps
- Unknown
- semantic-kernel
- +142 (30d)
Open issues delta
- awesome-ai-apps
- Unknown
- semantic-kernel
- -2 (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.
- Tags unique to semantic-kernel: artificial-intelligence, openai, sdk.
- - When you are looking to integrate cutting-edge language models (LLMs) directly from major providers like Azure OpenAI or OpenAI into your application.
When NOT to use semantic-kernel
- - If you require support exclusively in programming languages not currently offered by Semantic Kernel (for example, Ruby, Go).
- - When your project strictly avoids frameworks associated with Microsoft technologies and prefers more independent or community-driven alternatives.
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 Jul 26, 2026
- GitHub forks (Arindam200/awesome-ai-apps) · observed Jul 26, 2026
- Last push (Arindam200/awesome-ai-apps) · observed Jul 23, 2026
- License file (MIT) · observed Jul 26, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (microsoft/semantic-kernel) · observed Aug 7, 2026
- GitHub forks (microsoft/semantic-kernel) · observed Aug 7, 2026
- Last push (microsoft/semantic-kernel) · observed Aug 6, 2026
- License file (MIT) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-ai-apps 13k · semantic-kernel 28k (synced Jul 26, 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; Tags unique to semantic-kernel: artificial-intelligence, openai, sdk; - When you are looking to integrate cutting-edge language models (LLMs) directly from major providers like Azure OpenAI or OpenAI into your application.
- 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 you require support exclusively in programming languages not currently offered by Semantic Kernel (for example, Ruby, Go). - When your project strictly avoids frameworks associated with Microsoft technologies and prefers more independent or community-driven alternatives.
- Is awesome-ai-apps or semantic-kernel more popular on GitHub?
- semantic-kernel has more GitHub stars (28,427 vs 13,268). 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.