--- title: "autogen vs EnterpriseRAG-Bench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/microsoft-autogen-vs-onyx-dot-app-enterpriserag-bench" tools: ["microsoft-autogen", "onyx-dot-app-enterpriserag-bench"] --- # autogen vs EnterpriseRAG-Bench *GraphCanon updated Jul 11, 2026* ## Verdict Pick autogen when license: autogen is CC-BY-4.0, EnterpriseRAG-Bench is MIT; pick EnterpriseRAG-Bench when license: EnterpriseRAG-Bench is MIT, 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. [EnterpriseRAG-Bench](https://www.onyx.app/) has 454 stars, 46 forks, and 9 open issues, last pushed May 8, 2026. Figures are from public GitHub metadata via [autogen's repository](https://github.com/microsoft/autogen) and [EnterpriseRAG-Bench's repository](https://github.com/onyx-dot-app/EnterpriseRAG-Bench). | | [autogen](/tools/microsoft-autogen.md) | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | | --- | --- | --- | | Tagline | A programming framework for agentic AI | Dataset and benchmark for RAG on company internal documents. | | Stars | 59,658 | 454 | | Forks | 8,983 | 46 | | Open issues | 945 | 9 | | 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 | CC-BY-4.0 | MIT | | Categories | AI Agents, LLM Frameworks | Data & Retrieval, Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autogen](/tools/microsoft-autogen.md) | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | | --- | --- | --- | | Days since push | 87d | 64d | | Open issues (now) | 945 | 9 | | Full report | [trust report](/tools/microsoft-autogen/trust.md) | [trust report](/tools/onyx-dot-app-enterpriserag-bench/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 autogen if… - License: autogen is CC-BY-4.0, EnterpriseRAG-Bench is MIT. - 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. ### Choose EnterpriseRAG-Bench if… - License: EnterpriseRAG-Bench is MIT, autogen is CC-BY-4.0. - Tags unique to EnterpriseRAG-Bench: benchmark, dataset, enterprise, enterprise-search. - Also covers Data & Retrieval, 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 EnterpriseRAG-Bench - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - 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. ## Common questions ### What is the difference between autogen and EnterpriseRAG-Bench? autogen: A programming framework for agentic AI. EnterpriseRAG-Bench: Dataset and benchmark for RAG on company internal documents.. See the comparison table for live GitHub stats and shared categories. ### When should I choose autogen over EnterpriseRAG-Bench? Choose autogen over EnterpriseRAG-Bench when License: autogen is CC-BY-4.0, EnterpriseRAG-Bench is MIT; 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 choose EnterpriseRAG-Bench over autogen? Choose EnterpriseRAG-Bench over autogen when License: EnterpriseRAG-Bench is MIT, autogen is CC-BY-4.0; Tags unique to EnterpriseRAG-Bench: benchmark, dataset, enterprise, enterprise-search; Also covers Data & Retrieval, 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 EnterpriseRAG-Bench? Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. 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. ### Is autogen or EnterpriseRAG-Bench more popular on GitHub? autogen has more GitHub stars (59,658 vs 454). Stars measure visibility, not whether either tool fits your constraints. ### Are autogen and EnterpriseRAG-Bench open source? Yes - both are open-source projects on GitHub (autogen: CC-BY-4.0, EnterpriseRAG-Bench: MIT). ### Where can I find alternatives to autogen or EnterpriseRAG-Bench? GraphCanon lists graph-backed alternatives at [autogen alternatives](/tools/microsoft-autogen/alternatives) and [EnterpriseRAG-Bench alternatives](/tools/onyx-dot-app-enterpriserag-bench/alternatives) ([autogen markdown twin](/tools/microsoft-autogen/alternatives.md), [EnterpriseRAG-Bench markdown twin](/tools/onyx-dot-app-enterpriserag-bench/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-onyx-dot-app-enterpriserag-bench.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, autogen or EnterpriseRAG-Bench? autogen: Steady. EnterpriseRAG-Bench: 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 autogen and EnterpriseRAG-Bench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autogen trust report](/tools/microsoft-autogen/trust); [EnterpriseRAG-Bench trust report](/tools/onyx-dot-app-enterpriserag-bench/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/_