--- title: "awesome-ai-safety vs autogen" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/giskard-ai-awesome-ai-safety-vs-microsoft-autogen" tools: ["giskard-ai-awesome-ai-safety", "microsoft-autogen"] --- # awesome-ai-safety vs autogen *GraphCanon updated Jul 11, 2026* ## Verdict Pick awesome-ai-safety when license: awesome-ai-safety is Apache-2.0, autogen is CC-BY-4.0; pick autogen when license: autogen is CC-BY-4.0, awesome-ai-safety is Apache-2.0. [awesome-ai-safety](https://giskard.ai) reports 218 GitHub stars, 38 forks, and 17 open issues, last pushed Apr 14, 2025. [autogen](https://microsoft.github.io/autogen/) has 60k stars, 9.0k forks, and 945 open issues, last pushed Apr 15, 2026. Figures are from public GitHub metadata via [awesome-ai-safety's repository](https://github.com/Giskard-AI/awesome-ai-safety) and [autogen's repository](https://github.com/microsoft/autogen). | | [awesome-ai-safety](/tools/giskard-ai-awesome-ai-safety.md) | [autogen](/tools/microsoft-autogen.md) | | --- | --- | --- | | Tagline | 📚 A curated list of papers & technical articles on AI Quality & Safety | A programming framework for agentic AI | | Stars | 218 | 59,658 | | Forks | 38 | 8,983 | | Open issues | 17 | 945 | | Language | - | Python | | Adopt for | - | AutoGen is a Python-based framework for developing and managing agentic AI systems. It includes the AutoGen Studio for no-code GUI setup, integrating with various models. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | CC-BY-4.0 | | Categories | Data & Retrieval, LLM Frameworks, Computer Vision | LLM Frameworks, AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-ai-safety](/tools/giskard-ai-awesome-ai-safety.md) | [autogen](/tools/microsoft-autogen.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 452d | 87d | | Open issues (now) | 17 | 945 | | Full report | [trust report](/tools/giskard-ai-awesome-ai-safety/trust.md) | [trust report](/tools/microsoft-autogen/trust.md) | ## Decision facts: autogen - **Requirements:** Min 4 GB RAM; AutoGen requires Python 3.10 or later.; Ensure security when connecting to MCP servers due to the potential for local command execution and sensitive information exposure. - **Adopt for:** AutoGen is a Python-based framework for developing and managing agentic AI systems. It includes the AutoGen Studio for no-code GUI setup, integrating with various models. ## Choose when ### Choose awesome-ai-safety if… - License: awesome-ai-safety is Apache-2.0, autogen is CC-BY-4.0. - Tags unique to awesome-ai-safety: awesome, ai-safety, ai-alignment, artificial-intelligence. - Also covers Data & Retrieval, Computer Vision. ### Choose autogen if… - License: autogen is CC-BY-4.0, awesome-ai-safety is Apache-2.0. - Requirements: Min 4 GB RAM; AutoGen requires Python 3.10 or later.; Ensure security when connecting to MCP servers due to the potential for local command execution and sensitive information exposure.. - Tags unique to autogen: llm-framework, autogen, agents, agentic-agi. - Also covers AI Agents. - You need a framework that supports integration with multiple AI models via OpenAI's chat completion client. ## When NOT to use awesome-ai-safety - Last GitHub push was 453 days ago (dormant maintenance, Apr 14, 2025). Validate activity before betting a new project on awesome-ai-safety. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use autogen - If you require tools supporting multiple programming languages beyond Python, as AutoGen is strictly a Python-based framework. - When deploying in environments where connecting to external servers (like those used by MCP) could pose security risks or is prohibited. - You need solutions which do not involve additional installations for server components such as `playwright/mcp`, as AutoGen requires this setup for certain functionalities. ## Common questions ### What is the difference between awesome-ai-safety and autogen? awesome-ai-safety: 📚 A curated list of papers & technical articles on AI Quality & Safety. autogen: A programming framework for agentic AI. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-ai-safety over autogen? Choose awesome-ai-safety over autogen when License: awesome-ai-safety is Apache-2.0, autogen is CC-BY-4.0; Tags unique to awesome-ai-safety: awesome, ai-safety, ai-alignment, artificial-intelligence; Also covers Data & Retrieval, Computer Vision. ### When should I choose autogen over awesome-ai-safety? Choose autogen over awesome-ai-safety when License: autogen is CC-BY-4.0, awesome-ai-safety is Apache-2.0; Requirements: Min 4 GB RAM; AutoGen requires Python 3.10 or later.; Ensure security when connecting to MCP servers due to the potential for local command execution and sensitive information exposure.; Tags unique to autogen: llm-framework, autogen, agents, agentic-agi; Also covers AI Agents; You need a framework that supports integration with multiple AI models via OpenAI's chat completion client. ### When should I avoid awesome-ai-safety? Last GitHub push was 453 days ago (dormant maintenance, Apr 14, 2025). Validate activity before betting a new project on awesome-ai-safety. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid autogen? If you require tools supporting multiple programming languages beyond Python, as AutoGen is strictly a Python-based framework. When deploying in environments where connecting to external servers (like those used by MCP) could pose security risks or is prohibited. You need solutions which do not involve additional installations for server components such as `playwright/mcp`, as AutoGen requires this setup for certain functionalities. ### Is awesome-ai-safety or autogen more popular on GitHub? autogen has more GitHub stars (59,658 vs 218). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-ai-safety and autogen open source? Yes - both are open-source projects on GitHub (awesome-ai-safety: Apache-2.0, autogen: CC-BY-4.0). ### Where can I find alternatives to awesome-ai-safety or autogen? GraphCanon lists graph-backed alternatives at [awesome-ai-safety alternatives](/tools/giskard-ai-awesome-ai-safety/alternatives) and [autogen alternatives](/tools/microsoft-autogen/alternatives) ([awesome-ai-safety markdown twin](/tools/giskard-ai-awesome-ai-safety/alternatives.md), [autogen markdown twin](/tools/microsoft-autogen/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/giskard-ai-awesome-ai-safety-vs-microsoft-autogen.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-ai-safety or autogen? awesome-ai-safety: Dormant. autogen: Steady. 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-safety and autogen? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-ai-safety trust report](/tools/giskard-ai-awesome-ai-safety/trust); [autogen trust report](/tools/microsoft-autogen/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=giskard-ai-awesome-ai-safety`](/api/graphcanon/graph?tool=giskard-ai-awesome-ai-safety) - 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/_