--- title: "llm-lobbyist vs autogen" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/johnnay-llm-lobbyist-vs-microsoft-autogen" tools: ["johnnay-llm-lobbyist", "microsoft-autogen"] --- # llm-lobbyist vs autogen *GraphCanon updated Jul 11, 2026* ## Verdict Pick llm-lobbyist when llm-lobbyist is primarily Jupyter Notebook; autogen is Python; pick autogen when autogen is primarily Python; llm-lobbyist is Jupyter Notebook. [llm-lobbyist](https://github.com/JohnNay/llm-lobbyist) reports 174 GitHub stars, 14 forks, and 0 open issues, last pushed Jan 13, 2023. [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 [llm-lobbyist's repository](https://github.com/JohnNay/llm-lobbyist) and [autogen's repository](https://github.com/microsoft/autogen). | | [llm-lobbyist](/tools/johnnay-llm-lobbyist.md) | [autogen](/tools/microsoft-autogen.md) | | --- | --- | --- | | Tagline | Code for the paper: "Large Language Models as Corporate Lobbyists" (2023). | A programming framework for agentic AI | | Stars | 174 | 59,658 | | Forks | 14 | 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 | - | CC-BY-4.0 | | Categories | Evaluation & Observability, LLM Frameworks, Vector Databases | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-lobbyist](/tools/johnnay-llm-lobbyist.md) | [autogen](/tools/microsoft-autogen.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 1275d | 87d | | Open issues (now) | 0 | 945 | | Owner type | User | Organization | | Full report | [trust report](/tools/johnnay-llm-lobbyist/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 llm-lobbyist if… - llm-lobbyist is primarily Jupyter Notebook; autogen is Python. - Tags unique to llm-lobbyist: jupyter notebook. - Also covers Evaluation & Observability, Vector Databases. ### Choose autogen if… - autogen is primarily Python; llm-lobbyist is Jupyter Notebook. - 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 llm-lobbyist - Last GitHub push was 1276 days ago (dormant maintenance, Jan 13, 2023). Validate activity before betting a new project on llm-lobbyist. - 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. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## 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 llm-lobbyist and autogen? llm-lobbyist: Code for the paper: "Large Language Models as Corporate Lobbyists" (2023).. autogen: A programming framework for agentic AI. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-lobbyist over autogen? Choose llm-lobbyist over autogen when llm-lobbyist is primarily Jupyter Notebook; autogen is Python; Tags unique to llm-lobbyist: jupyter notebook; Also covers Evaluation & Observability, Vector Databases. ### When should I choose autogen over llm-lobbyist? Choose autogen over llm-lobbyist when autogen is primarily Python; llm-lobbyist is Jupyter Notebook; 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 llm-lobbyist? Last GitHub push was 1276 days ago (dormant maintenance, Jan 13, 2023). Validate activity before betting a new project on llm-lobbyist. 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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### 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 llm-lobbyist or autogen more popular on GitHub? autogen has more GitHub stars (59,658 vs 174). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-lobbyist and autogen open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to llm-lobbyist or autogen? GraphCanon lists graph-backed alternatives at [llm-lobbyist alternatives](/tools/johnnay-llm-lobbyist/alternatives) and [autogen alternatives](/tools/microsoft-autogen/alternatives) ([llm-lobbyist markdown twin](/tools/johnnay-llm-lobbyist/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/johnnay-llm-lobbyist-vs-microsoft-autogen.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-lobbyist or autogen? llm-lobbyist: 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 llm-lobbyist and autogen? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-lobbyist trust report](/tools/johnnay-llm-lobbyist/trust); [autogen trust report](/tools/microsoft-autogen/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=johnnay-llm-lobbyist`](/api/graphcanon/graph?tool=johnnay-llm-lobbyist) - 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/_