--- title: "llm_agents vs agent-kernel" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mpaepper-llm-agents-vs-yaalalabs-agent-kernel" tools: ["mpaepper-llm-agents", "yaalalabs-agent-kernel"] --- # llm_agents vs agent-kernel *GraphCanon updated Aug 15, 2026* ## Verdict Pick llm_agents if llm_agents is a Python library enabling users to build simple agents directed by large language models, featuring functions like executing Python code and using Google search; pick agent-kernel if agent-kernel provides an operating system for scalable enterprise AI agents, supporting deployment and orchestration at scale with native integration support for MCP and A2A. [llm_agents](https://www.paepper.com/blog/posts/intelligent-agents-guided-by-llms/) reports 1.1k GitHub stars, 85 forks, and 3 open issues, last pushed Jun 23, 2025. [agent-kernel](https://kernel.yaala.ai/) has 113 stars, 60 forks, and 128 open issues, last pushed Aug 7, 2026. Figures are from public GitHub metadata via [llm_agents's repository](https://github.com/mpaepper/llm_agents) and [agent-kernel's repository](https://github.com/yaalalabs/agent-kernel). | | [llm_agents](/tools/mpaepper-llm-agents.md) | [agent-kernel](/tools/yaalalabs-agent-kernel.md) | | --- | --- | --- | | Tagline | Library to build agents controlled by LLMs | The Operating System for Scalable Enterprise AI Agents | | Stars | 1,053 | 113 | | Forks | 85 | 60 | | Open issues | 3 | 128 | | Language | Python | Python | | Adopt for | llm_agents is a Python library enabling users to build simple agents directed by large language models, featuring functions like executing Python code and using Google search. | Agent-kernel provides an operating system for scalable enterprise AI agents, supporting deployment and orchestration at scale with native integration support for MCP and A2A. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents | AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm_agents](/tools/mpaepper-llm-agents.md) | [agent-kernel](/tools/yaalalabs-agent-kernel.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 418d | 2d | | Open issues (now) | 3 | 128 | | Stars delta | +3 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/mpaepper-llm-agents/trust.md) | [trust report](/tools/yaalalabs-agent-kernel/trust.md) | ## Shared compatibility - **Python**: [llm_agents](/tools/mpaepper-llm-agents.md) - Python runtime; [agent-kernel](/tools/yaalalabs-agent-kernel.md) - Python runtime ## Decision facts: llm_agents - **Requirements:** Min 1 GB RAM; Requires the installation of requirements specified by running `pip install -r requirements.txt` followed by `pip install -e .`.; Dependencies include setting up environment variables for `OPENAI_API_KEY` to use OpenAI API and optionally `SERPAPI_API_KEY` if Google search tool is utilized. - **Adopt for:** llm_agents is a Python library enabling users to build simple agents directed by large language models, featuring functions like executing Python code and using Google search. ## Decision facts: agent-kernel - **Requirements:** It requires Python versions between 3.12 and 3.13.x.; Supports deployment to various environments such as AWS Lambda, ECS, Azure Functions, or Container Apps via one Terraform module. - **Adopt for:** Agent-kernel provides an operating system for scalable enterprise AI agents, supporting deployment and orchestration at scale with native integration support for MCP and A2A. ## Choose when ### Choose llm_agents if… - License: llm_agents is MIT, agent-kernel is Apache-2.0. - Requirements: Min 1 GB RAM; Requires the installation of requirements specified by running `pip install -r requirements.txt` followed by `pip install -e .`.; Dependencies include setting up environment variables for `OPENAI_API_KEY` to use OpenAI API and optionally `SERPAPI_API_KEY` if Google search tool is utilized.. - Tags unique to llm_agents: deep-learning, langchain, llms, machine-learning. - Use llm_agents when you require a lightweight solution for building agents controlled by LLMs with specific tools such as Python REPL execution or Hacker News search. ### Choose agent-kernel if… - License: agent-kernel is Apache-2.0, llm_agents is MIT. - Requirements: It requires Python versions between 3.12 and 3.13.x.; Supports deployment to various environments such as AWS Lambda, ECS, Azure Functions, or Container Apps via one Terraform module.. - Tags unique to agent-kernel: a2a, adk, aws, azure. - If you require seamless scalability across different cloud providers like AWS and Azure without lock-in or rewrites. ## When NOT to use llm_agents - Avoid llm_agents if you need a more robust and feature-rich product for complex tasks that require extensive integration capabilities beyond Python REPL, Google search, and Hacker News. - Do not choose it when you are looking for advanced abstraction layers like those found in LangChain, as llm_agents aims to remain simple with fewer files and a straightforward core. ## When NOT to use agent-kernel - If your project is confined to a single, specific AI framework which doesn't require the flexibility Agent-kernel offers. - When you do not have Python version 3.12 - 3.13.x, as it's the required runtime environment. ## Common questions ### What is the difference between llm_agents and agent-kernel? llm_agents: Library to build agents controlled by LLMs. agent-kernel: The Operating System for Scalable Enterprise AI Agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm_agents over agent-kernel? Choose llm_agents over agent-kernel when License: llm_agents is MIT, agent-kernel is Apache-2.0; Requirements: Min 1 GB RAM; Requires the installation of requirements specified by running `pip install -r requirements.txt` followed by `pip install -e .`.; Dependencies include setting up environment variables for `OPENAI_API_KEY` to use OpenAI API and optionally `SERPAPI_API_KEY` if Google search tool is utilized.; Tags unique to llm_agents: deep-learning, langchain, llms, machine-learning; Use llm_agents when you require a lightweight solution for building agents controlled by LLMs with specific tools such as Python REPL execution or Hacker News search. ### When should I choose agent-kernel over llm_agents? Choose agent-kernel over llm_agents when License: agent-kernel is Apache-2.0, llm_agents is MIT; Requirements: It requires Python versions between 3.12 and 3.13.x.; Supports deployment to various environments such as AWS Lambda, ECS, Azure Functions, or Container Apps via one Terraform module.; Tags unique to agent-kernel: a2a, adk, aws, azure; If you require seamless scalability across different cloud providers like AWS and Azure without lock-in or rewrites. ### When should I avoid llm_agents? Avoid llm_agents if you need a more robust and feature-rich product for complex tasks that require extensive integration capabilities beyond Python REPL, Google search, and Hacker News. Do not choose it when you are looking for advanced abstraction layers like those found in LangChain, as llm_agents aims to remain simple with fewer files and a straightforward core. ### When should I avoid agent-kernel? If your project is confined to a single, specific AI framework which doesn't require the flexibility Agent-kernel offers. When you do not have Python version 3.12 - 3.13.x, as it's the required runtime environment. ### Is llm_agents or agent-kernel more popular on GitHub? llm_agents has more GitHub stars (1,053 vs 113). Stars measure visibility, not whether either tool fits your constraints. ### Are llm_agents and agent-kernel open source? Yes - both are open-source projects on GitHub (llm_agents: MIT, agent-kernel: Apache-2.0). ### Where can I find alternatives to llm_agents or agent-kernel? GraphCanon lists graph-backed alternatives at [llm_agents alternatives](/tools/mpaepper-llm-agents/alternatives) and [agent-kernel alternatives](/tools/yaalalabs-agent-kernel/alternatives) ([llm_agents markdown twin](/tools/mpaepper-llm-agents/alternatives.md), [agent-kernel markdown twin](/tools/yaalalabs-agent-kernel/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/mpaepper-llm-agents-vs-yaalalabs-agent-kernel.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm_agents or agent-kernel? llm_agents: Dormant. agent-kernel: Very active. 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_agents and agent-kernel? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm_agents trust report](/tools/mpaepper-llm-agents/trust); [agent-kernel trust report](/tools/yaalalabs-agent-kernel/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mpaepper-llm-agents`](/api/graphcanon/graph?tool=mpaepper-llm-agents) - 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/_