--- title: "autogen vs garak" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/microsoft-autogen-vs-nvidia-garak" tools: ["microsoft-autogen", "nvidia-garak"] --- # autogen vs garak *GraphCanon updated Jul 11, 2026* ## Verdict Pick autogen when license: autogen is CC-BY-4.0, garak is Apache-2.0; pick garak when license: garak is Apache-2.0, autogen is CC-BY-4.0. [autogen](https://microsoft.github.io/autogen/) reports 60k GitHub stars, 9.0k forks, and 945 open issues, last pushed Apr 15, 2026. [garak](https://discord.gg/uVch4puUCs) has 8.4k stars, 1.1k forks, and 367 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [autogen's repository](https://github.com/microsoft/autogen) and [garak's repository](https://github.com/NVIDIA/garak). | | [autogen](/tools/microsoft-autogen.md) | [garak](/tools/nvidia-garak.md) | | --- | --- | --- | | Tagline | A programming framework for agentic AI | the LLM vulnerability scanner | | Stars | 59,658 | 8,400 | | Forks | 8,983 | 1,079 | | Open issues | 945 | 367 | | Language | Python | 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 | CC-BY-4.0 | Apache-2.0 | | Categories | LLM Frameworks, AI Agents | LLM Frameworks, Vector Databases, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autogen](/tools/microsoft-autogen.md) | [garak](/tools/nvidia-garak.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 87d | 1d | | Open issues (now) | 945 | 367 | | Security scan | No lockfile | 54 low (54 low) | | Full report | [trust report](/tools/microsoft-autogen/trust.md) | [trust report](/tools/nvidia-garak/trust.md) | ## Shared compatibility - **Python**: [autogen](/tools/microsoft-autogen.md) - Python runtime; [garak](/tools/nvidia-garak.md) - Python runtime ## 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 autogen if… - License: autogen is CC-BY-4.0, garak 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. ### Choose garak if… - License: garak is Apache-2.0, autogen is CC-BY-4.0. - Tags unique to garak: python, security-scanners, llm-security, vulnerability-assessment. - Also covers Vector Databases, Evaluation & Observability. ## 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. ## When NOT to use garak - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## Common questions ### What is the difference between autogen and garak? autogen: A programming framework for agentic AI. garak: the LLM vulnerability scanner. See the comparison table for live GitHub stats and shared categories. ### When should I choose autogen over garak? Choose autogen over garak when License: autogen is CC-BY-4.0, garak 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 choose garak over autogen? Choose garak over autogen when License: garak is Apache-2.0, autogen is CC-BY-4.0; Tags unique to garak: python, security-scanners, llm-security, vulnerability-assessment; Also covers Vector Databases, Evaluation & Observability. ### 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. ### When should I avoid garak? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### Is autogen or garak more popular on GitHub? autogen has more GitHub stars (59,658 vs 8,400). Stars measure visibility, not whether either tool fits your constraints. ### Are autogen and garak open source? Yes - both are open-source projects on GitHub (autogen: CC-BY-4.0, garak: Apache-2.0). ### Where can I find alternatives to autogen or garak? GraphCanon lists graph-backed alternatives at [autogen alternatives](/tools/microsoft-autogen/alternatives) and [garak alternatives](/tools/nvidia-garak/alternatives) ([autogen markdown twin](/tools/microsoft-autogen/alternatives.md), [garak markdown twin](/tools/nvidia-garak/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/microsoft-autogen-vs-nvidia-garak.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, autogen or garak? autogen: Steady. garak: 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 autogen and garak? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autogen trust report](/tools/microsoft-autogen/trust); [garak trust report](/tools/nvidia-garak/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=microsoft-autogen`](/api/graphcanon/graph?tool=microsoft-autogen) - 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/_