--- title: "autogen vs llama-hub" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/microsoft-autogen-vs-run-llama-llama-hub" tools: ["microsoft-autogen", "run-llama-llama-hub"] --- # autogen vs llama-hub *GraphCanon updated Jul 12, 2026* ## Verdict Pick autogen when autogen is primarily Python; llama-hub is Jupyter Notebook; pick llama-hub when llama-hub is primarily Jupyter Notebook; autogen is Python. [autogen](https://microsoft.github.io/autogen/) reports 60k GitHub stars, 9.0k forks, and 945 open issues, last pushed Apr 15, 2026. [llama-hub](https://llamahub.ai/) has 3.5k stars, 719 forks, and 96 open issues, last pushed Mar 1, 2024. Figures are from public GitHub metadata via [autogen's repository](https://github.com/microsoft/autogen) and [llama-hub's repository](https://github.com/run-llama/llama-hub). | | [autogen](/tools/microsoft-autogen.md) | [llama-hub](/tools/run-llama-llama-hub.md) | | --- | --- | --- | | Tagline | A programming framework for agentic AI | A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain | | Stars | 59,658 | 3,473 | | Forks | 8,983 | 719 | | Open issues | 945 | 96 | | Language | Python | Jupyter Notebook | | 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) | [llama-hub](/tools/run-llama-llama-hub.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Archived (8%) | | Days since push | 87d | 861d | | Archived on GitHub | No | Yes | | Open issues (now) | 945 | 96 | | Security scan | No lockfile | 121 low (121 low) | | Full report | [trust report](/tools/microsoft-autogen/trust.md) | [trust report](/tools/run-llama-llama-hub/trust.md) | ## Shared compatibility - **Python**: [autogen](/tools/microsoft-autogen.md) - Python runtime; [llama-hub](/tools/run-llama-llama-hub.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… - autogen is primarily Python; llama-hub is Jupyter Notebook. - License: autogen is CC-BY-4.0, llama-hub 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 llama-hub if… - llama-hub is primarily Jupyter Notebook; autogen is Python. - License: llama-hub is MIT, autogen is CC-BY-4.0. - Tags unique to llama-hub: jupyter notebook. - 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 llama-hub - llama-hub is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. - 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 llama-hub? autogen: A programming framework for agentic AI. llama-hub: A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain. See the comparison table for live GitHub stats and shared categories. ### When should I choose autogen over llama-hub? Choose autogen over llama-hub when autogen is primarily Python; llama-hub is Jupyter Notebook; License: autogen is CC-BY-4.0, llama-hub 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 llama-hub over autogen? Choose llama-hub over autogen when llama-hub is primarily Jupyter Notebook; autogen is Python; License: llama-hub is MIT, autogen is CC-BY-4.0; Tags unique to llama-hub: jupyter notebook; 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 llama-hub? llama-hub is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. 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 llama-hub more popular on GitHub? autogen has more GitHub stars (59,658 vs 3,473). Stars measure visibility, not whether either tool fits your constraints. ### Are autogen and llama-hub open source? Yes - both are open-source projects on GitHub (autogen: CC-BY-4.0, llama-hub: MIT). ### Where can I find alternatives to autogen or llama-hub? GraphCanon lists graph-backed alternatives at [autogen alternatives](/tools/microsoft-autogen/alternatives) and [llama-hub alternatives](/tools/run-llama-llama-hub/alternatives) ([autogen markdown twin](/tools/microsoft-autogen/alternatives.md), [llama-hub markdown twin](/tools/run-llama-llama-hub/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-run-llama-llama-hub.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, autogen or llama-hub? autogen: Steady. llama-hub: Archived. 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 llama-hub? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autogen trust report](/tools/microsoft-autogen/trust); [llama-hub trust report](/tools/run-llama-llama-hub/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/_