--- title: "do-not-answer vs autogen" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/libr-ai-do-not-answer-vs-microsoft-autogen" tools: ["libr-ai-do-not-answer", "microsoft-autogen"] --- # do-not-answer vs autogen *GraphCanon updated Jul 11, 2026* ## Verdict Pick do-not-answer when do-not-answer is primarily Jupyter Notebook; autogen is Python; pick autogen when autogen is primarily Python; do-not-answer is Jupyter Notebook. [do-not-answer](https://github.com/Libr-AI/do-not-answer) reports 334 GitHub stars, 29 forks, and 0 open issues, last pushed Jun 7, 2024. [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 [do-not-answer's repository](https://github.com/Libr-AI/do-not-answer) and [autogen's repository](https://github.com/microsoft/autogen). | | [do-not-answer](/tools/libr-ai-do-not-answer.md) | [autogen](/tools/microsoft-autogen.md) | | --- | --- | --- | | Tagline | Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs | A programming framework for agentic AI | | Stars | 334 | 59,658 | | Forks | 29 | 8,983 | | Open issues | 0 | 945 | | Language | Jupyter Notebook | 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 | Evaluation & Observability, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [do-not-answer](/tools/libr-ai-do-not-answer.md) | [autogen](/tools/microsoft-autogen.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 764d | 87d | | Open issues (now) | 0 | 945 | | Full report | [trust report](/tools/libr-ai-do-not-answer/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 do-not-answer if… - do-not-answer is primarily Jupyter Notebook; autogen is Python. - License: do-not-answer is Apache-2.0, autogen is CC-BY-4.0. - Tags unique to do-not-answer: jupyter notebook. - Also covers Evaluation & Observability. ### Choose autogen if… - autogen is primarily Python; do-not-answer is Jupyter Notebook. - License: autogen is CC-BY-4.0, do-not-answer 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: agentic-agi, agents, ai, autogen. - 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 do-not-answer - Last GitHub push was 764 days ago (dormant maintenance, Jun 7, 2024). Validate activity before betting a new project on do-not-answer. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - 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 do-not-answer and autogen? do-not-answer: Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs. autogen: A programming framework for agentic AI. See the comparison table for live GitHub stats and shared categories. ### When should I choose do-not-answer over autogen? Choose do-not-answer over autogen when do-not-answer is primarily Jupyter Notebook; autogen is Python; License: do-not-answer is Apache-2.0, autogen is CC-BY-4.0; Tags unique to do-not-answer: jupyter notebook; Also covers Evaluation & Observability. ### When should I choose autogen over do-not-answer? Choose autogen over do-not-answer when autogen is primarily Python; do-not-answer is Jupyter Notebook; License: autogen is CC-BY-4.0, do-not-answer 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: agentic-agi, agents, ai, autogen; 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 do-not-answer? Last GitHub push was 764 days ago (dormant maintenance, Jun 7, 2024). Validate activity before betting a new project on do-not-answer. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. 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 do-not-answer or autogen more popular on GitHub? autogen has more GitHub stars (59,658 vs 334). Stars measure visibility, not whether either tool fits your constraints. ### Are do-not-answer and autogen open source? Yes - both are open-source projects on GitHub (do-not-answer: Apache-2.0, autogen: CC-BY-4.0). ### Where can I find alternatives to do-not-answer or autogen? GraphCanon lists graph-backed alternatives at [do-not-answer alternatives](/tools/libr-ai-do-not-answer/alternatives) and [autogen alternatives](/tools/microsoft-autogen/alternatives) ([do-not-answer markdown twin](/tools/libr-ai-do-not-answer/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/libr-ai-do-not-answer-vs-microsoft-autogen.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, do-not-answer or autogen? do-not-answer: 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 do-not-answer and autogen? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [do-not-answer trust report](/tools/libr-ai-do-not-answer/trust); [autogen trust report](/tools/microsoft-autogen/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=libr-ai-do-not-answer`](/api/graphcanon/graph?tool=libr-ai-do-not-answer) - 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/_