--- title: "agent-starter-pack vs llm_agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/googlecloudplatform-agent-starter-pack-vs-mpaepper-llm-agents" tools: ["googlecloudplatform-agent-starter-pack", "mpaepper-llm-agents"] --- # agent-starter-pack vs llm_agents *GraphCanon updated Aug 20, 2026* ## Verdict Pick agent-starter-pack if agent-starter-pack offers built-in CI/CD, evaluation tools, and observability for deploying generative AI agents on Google Cloud with Python; 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. [agent-starter-pack](http://goo.gle/agents-cli) reports 6.5k GitHub stars, 1.5k forks, and 49 open issues, last pushed Jul 21, 2026. [llm_agents](https://www.paepper.com/blog/posts/intelligent-agents-guided-by-llms/) has 1.1k stars, 85 forks, and 3 open issues, last pushed Jun 23, 2025. Figures are from public GitHub metadata via [agent-starter-pack's repository](https://github.com/GoogleCloudPlatform/agent-starter-pack) and [llm_agents's repository](https://github.com/mpaepper/llm_agents). | | [agent-starter-pack](/tools/googlecloudplatform-agent-starter-pack.md) | [llm_agents](/tools/mpaepper-llm-agents.md) | | --- | --- | --- | | Tagline | Ship AI Agents to Google Cloud in minutes with built-in CI/CD, evaluation, and observability | Library to build agents controlled by LLMs | | Stars | 6,537 | 1,053 | | Forks | 1,502 | 85 | | Open issues | 49 | 3 | | Language | Python | Python | | Adopt for | agent-starter-pack offers built-in CI/CD, evaluation tools, and observability for deploying generative AI agents on Google Cloud with Python. | 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. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | AI Agents, Evaluation & Observability | AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agent-starter-pack](/tools/googlecloudplatform-agent-starter-pack.md) | [llm_agents](/tools/mpaepper-llm-agents.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 29d | 418d | | Open issues (now) | 49 | 3 | | Stars delta | +18 (30d) | +3 (30d) | | Open issues delta | +1 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/googlecloudplatform-agent-starter-pack/trust.md) | [trust report](/tools/mpaepper-llm-agents/trust.md) | ## Shared compatibility - **Python**: [agent-starter-pack](/tools/googlecloudplatform-agent-starter-pack.md) - Python runtime; [llm_agents](/tools/mpaepper-llm-agents.md) - Python runtime ## Decision facts: agent-starter-pack - **Requirements:** Depends on Python 3.10+.; Requires the Google Cloud SDK for interacting with GCP services.; Terraform is needed for deployment purposes.; Make utility should be installed for development tasks. - **Adopt for:** agent-starter-pack offers built-in CI/CD, evaluation tools, and observability for deploying generative AI agents on Google Cloud with Python. ## 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. ## Choose when ### Choose agent-starter-pack if… - License: agent-starter-pack is Apache-2.0, llm_agents is MIT. - Requirements: Depends on Python 3.10+.; Requires the Google Cloud SDK for interacting with GCP services.; Terraform is needed for deployment purposes.; Make utility should be installed for development tasks.. - Tags unique to agent-starter-pack: agents, gcp, gemini, genai-agents. - Also covers Evaluation & Observability. - You are working with Google Cloud and wish to deploy generative AI agents quickly using production-ready templates that come with integrated CI/CD pipelines. ### Choose llm_agents if… - License: llm_agents is MIT, agent-starter-pack 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 NOT to use agent-starter-pack - Your project is hosted on a cloud provider other than Google Cloud, as this tool is optimized for GCP services and uses Terraform specifically configured for deployment with it. - You do not need or want built-in CI/CD pipelines and evaluation tools, preferring to manage these components separately through different tools or self-configured setups. ## 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. ## Common questions ### What is the difference between agent-starter-pack and llm_agents? agent-starter-pack: Ship AI Agents to Google Cloud in minutes with built-in CI/CD, evaluation, and observability. llm_agents: Library to build agents controlled by LLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose agent-starter-pack over llm_agents? Choose agent-starter-pack over llm_agents when License: agent-starter-pack is Apache-2.0, llm_agents is MIT; Requirements: Depends on Python 3.10+.; Requires the Google Cloud SDK for interacting with GCP services.; Terraform is needed for deployment purposes.; Make utility should be installed for development tasks.; Tags unique to agent-starter-pack: agents, gcp, gemini, genai-agents; Also covers Evaluation & Observability; You are working with Google Cloud and wish to deploy generative AI agents quickly using production-ready templates that come with integrated CI/CD pipelines. ### When should I choose llm_agents over agent-starter-pack? Choose llm_agents over agent-starter-pack when License: llm_agents is MIT, agent-starter-pack 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 avoid agent-starter-pack? Your project is hosted on a cloud provider other than Google Cloud, as this tool is optimized for GCP services and uses Terraform specifically configured for deployment with it. You do not need or want built-in CI/CD pipelines and evaluation tools, preferring to manage these components separately through different tools or self-configured setups. ### 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. ### Is agent-starter-pack or llm_agents more popular on GitHub? agent-starter-pack has more GitHub stars (6,537 vs 1,053). Stars measure visibility, not whether either tool fits your constraints. ### Are agent-starter-pack and llm_agents open source? Yes - both are open-source projects on GitHub (agent-starter-pack: Apache-2.0, llm_agents: MIT). ### Where can I find alternatives to agent-starter-pack or llm_agents? GraphCanon lists graph-backed alternatives at [agent-starter-pack alternatives](/tools/googlecloudplatform-agent-starter-pack/alternatives) and [llm_agents alternatives](/tools/mpaepper-llm-agents/alternatives) ([agent-starter-pack markdown twin](/tools/googlecloudplatform-agent-starter-pack/alternatives.md), [llm_agents markdown twin](/tools/mpaepper-llm-agents/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/googlecloudplatform-agent-starter-pack-vs-mpaepper-llm-agents.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agent-starter-pack or llm_agents? agent-starter-pack: Active. llm_agents: Dormant. 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 agent-starter-pack and llm_agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agent-starter-pack trust report](/tools/googlecloudplatform-agent-starter-pack/trust); [llm_agents trust report](/tools/mpaepper-llm-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=googlecloudplatform-agent-starter-pack`](/api/graphcanon/graph?tool=googlecloudplatform-agent-starter-pack) - 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/_