--- title: "lingoose vs autogen" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/henomis-lingoose-vs-microsoft-autogen" tools: ["henomis-lingoose", "microsoft-autogen"] --- # lingoose vs autogen *GraphCanon updated Jul 11, 2026* ## Verdict Pick lingoose when lingoose is primarily Go; autogen is Python; pick autogen when autogen is primarily Python; lingoose is Go. [lingoose](https://simonevellei.com/lingoose) reports 834 GitHub stars, 76 forks, and 16 open issues, last pushed Mar 15, 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 [lingoose's repository](https://github.com/henomis/lingoose) and [autogen's repository](https://github.com/microsoft/autogen). | | [lingoose](/tools/henomis-lingoose.md) | [autogen](/tools/microsoft-autogen.md) | | --- | --- | --- | | Tagline | 🪿 LinGoose is a Go framework for building awesome AI/LLM applications. | A programming framework for agentic AI | | Stars | 834 | 59,658 | | Forks | 76 | 8,983 | | Open issues | 16 | 945 | | Language | Go | 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 | MIT | CC-BY-4.0 | | Categories | Data & Retrieval, LLM Frameworks, Vector Databases | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lingoose](/tools/henomis-lingoose.md) | [autogen](/tools/microsoft-autogen.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Steady (60%) | | Days since push | 118d | 87d | | Open issues (now) | 16 | 945 | | Owner type | User | Organization | | Full report | [trust report](/tools/henomis-lingoose/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 lingoose if… - lingoose is primarily Go; autogen is Python. - License: lingoose is MIT, autogen is CC-BY-4.0. - Tags unique to lingoose: embeddings, go, golang, index. - Also covers Data & Retrieval, Vector Databases. ### Choose autogen if… - autogen is primarily Python; lingoose is Go. - License: autogen is CC-BY-4.0, lingoose 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, autogen, autogen-ecosystem. - 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 lingoose - Last GitHub push was 119 days ago (slowing maintenance, Mar 15, 2026). Validate activity before betting a new project on lingoose. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - 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 lingoose and autogen? lingoose: 🪿 LinGoose is a Go framework for building awesome AI/LLM applications.. autogen: A programming framework for agentic AI. See the comparison table for live GitHub stats and shared categories. ### When should I choose lingoose over autogen? Choose lingoose over autogen when lingoose is primarily Go; autogen is Python; License: lingoose is MIT, autogen is CC-BY-4.0; Tags unique to lingoose: embeddings, go, golang, index; Also covers Data & Retrieval, Vector Databases. ### When should I choose autogen over lingoose? Choose autogen over lingoose when autogen is primarily Python; lingoose is Go; License: autogen is CC-BY-4.0, lingoose 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, autogen, autogen-ecosystem; 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 lingoose? Last GitHub push was 119 days ago (slowing maintenance, Mar 15, 2026). Validate activity before betting a new project on lingoose. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. 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 lingoose or autogen more popular on GitHub? autogen has more GitHub stars (59,658 vs 834). Stars measure visibility, not whether either tool fits your constraints. ### Are lingoose and autogen open source? Yes - both are open-source projects on GitHub (lingoose: MIT, autogen: CC-BY-4.0). ### Where can I find alternatives to lingoose or autogen? GraphCanon lists graph-backed alternatives at [lingoose alternatives](/tools/henomis-lingoose/alternatives) and [autogen alternatives](/tools/microsoft-autogen/alternatives) ([lingoose markdown twin](/tools/henomis-lingoose/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/henomis-lingoose-vs-microsoft-autogen.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lingoose or autogen? lingoose: Slowing. 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 lingoose and autogen? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lingoose trust report](/tools/henomis-lingoose/trust); [autogen trust report](/tools/microsoft-autogen/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=henomis-lingoose`](/api/graphcanon/graph?tool=henomis-lingoose) - 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/_