--- title: "judgeval vs autogen" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/judgmentlabs-judgeval-vs-microsoft-autogen" tools: ["judgmentlabs-judgeval", "microsoft-autogen"] --- # judgeval vs autogen *GraphCanon updated Jul 12, 2026* ## Verdict Pick judgeval if judgeval is a Python tool that aids in the continuous improvement of AI agents through comprehensive environment data and evaluations, supporting methodologies like reinforcement learning and prompt engineering; pick autogen if 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. [judgeval](https://judgmentlabs.ai/) reports 1.0k GitHub stars, 93 forks, and 18 open issues, last pushed Jul 7, 2026. [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 [judgeval's repository](https://github.com/JudgmentLabs/judgeval) and [autogen's repository](https://github.com/microsoft/autogen). | | [judgeval](/tools/judgmentlabs-judgeval.md) | [autogen](/tools/microsoft-autogen.md) | | --- | --- | --- | | Tagline | The Continuous-Improvement Stack for Agents | A programming framework for agentic AI | | Stars | 1,040 | 59,658 | | Forks | 93 | 8,983 | | Open issues | 18 | 945 | | Language | Python | Python | | Adopt for | Judgeval is a Python tool that aids in the continuous improvement of AI agents through comprehensive environment data and evaluations, supporting methodologies like reinforcement learning and prompt engineering. | 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 | AI Agents, Evaluation & Observability | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [judgeval](/tools/judgmentlabs-judgeval.md) | [autogen](/tools/microsoft-autogen.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 3d | 87d | | Open issues (now) | 18 | 945 | | Full report | [trust report](/tools/judgmentlabs-judgeval/trust.md) | [trust report](/tools/microsoft-autogen/trust.md) | ## Decision facts: judgeval - **Adopt for:** Judgeval is a Python tool that aids in the continuous improvement of AI agents through comprehensive environment data and evaluations, supporting methodologies like reinforcement learning and prompt engineering. ## 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 judgeval if… - License: judgeval is Apache-2.0, autogen is CC-BY-4.0. - Tags unique to judgeval: agent, agentic-ai, grpo, langchain. - Also covers Evaluation & Observability. - You are working on an AI project where continuous monitoring and enhancement of your agent's performance are critical. ### Choose autogen if… - License: autogen is CC-BY-4.0, judgeval 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, ai, autogen, autogen-ecosystem. - Also covers LLM Frameworks. - You need a framework that supports integration with multiple AI models via OpenAI's chat completion client. ## When NOT to use judgeval - If you are looking for a tool focused solely on the theoretical aspects of AI development without practical, continuous improvement methodologies. - You require a solution that only supports evaluation metrics and does not offer integrated environment data support, diverging from Judgeval’s comprehensive approach. ## 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 judgeval and autogen? judgeval: The Continuous-Improvement Stack for Agents. autogen: A programming framework for agentic AI. See the comparison table for live GitHub stats and shared categories. ### When should I choose judgeval over autogen? Choose judgeval over autogen when License: judgeval is Apache-2.0, autogen is CC-BY-4.0; Tags unique to judgeval: agent, agentic-ai, grpo, langchain; Also covers Evaluation & Observability; You are working on an AI project where continuous monitoring and enhancement of your agent's performance are critical. ### When should I choose autogen over judgeval? Choose autogen over judgeval when License: autogen is CC-BY-4.0, judgeval 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, ai, autogen, autogen-ecosystem; Also covers LLM Frameworks; You need a framework that supports integration with multiple AI models via OpenAI's chat completion client. ### When should I avoid judgeval? If you are looking for a tool focused solely on the theoretical aspects of AI development without practical, continuous improvement methodologies. You require a solution that only supports evaluation metrics and does not offer integrated environment data support, diverging from Judgeval’s comprehensive approach. ### 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 judgeval or autogen more popular on GitHub? autogen has more GitHub stars (59,658 vs 1,040). Stars measure visibility, not whether either tool fits your constraints. ### Are judgeval and autogen open source? Yes - both are open-source projects on GitHub (judgeval: Apache-2.0, autogen: CC-BY-4.0). ### Where can I find alternatives to judgeval or autogen? GraphCanon lists graph-backed alternatives at [judgeval alternatives](/tools/judgmentlabs-judgeval/alternatives) and [autogen alternatives](/tools/microsoft-autogen/alternatives) ([judgeval markdown twin](/tools/judgmentlabs-judgeval/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/judgmentlabs-judgeval-vs-microsoft-autogen.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, judgeval or autogen? judgeval: Very active. 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 judgeval and autogen? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [judgeval trust report](/tools/judgmentlabs-judgeval/trust); [autogen trust report](/tools/microsoft-autogen/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=judgmentlabs-judgeval`](/api/graphcanon/graph?tool=judgmentlabs-judgeval) - 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/_