--- title: "agent-opt vs llm_agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-agent-opt-vs-mpaepper-llm-agents" tools: ["future-agi-agent-opt", "mpaepper-llm-agents"] --- # agent-opt vs llm_agents *GraphCanon updated Aug 15, 2026* ## Verdict Pick agent-opt if agent-opt is tailored for teams that require automated optimization of AI workflows and support for continuous integration/continuous delivery (CI/CD), relying on Python and specific library dependencies; 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-opt](https://app.futureagi.com) reports 71 GitHub stars, 7 forks, and 0 open issues, last pushed Jun 30, 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-opt's repository](https://github.com/future-agi/agent-opt) and [llm_agents's repository](https://github.com/mpaepper/llm_agents). | | [agent-opt](/tools/future-agi-agent-opt.md) | [llm_agents](/tools/mpaepper-llm-agents.md) | | --- | --- | --- | | Tagline | Open Source Library for Automated Optimization of AI Agent Workflows | Library to build agents controlled by LLMs | | Stars | 71 | 1,053 | | Forks | 7 | 85 | | Open issues | 0 | 3 | | Language | Python | Python | | Adopt for | Agent-opt is tailored for teams that require automated optimization of AI workflows and support for continuous integration/continuous delivery (CI/CD), relying on Python and specific library dependencies. | 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-opt](/tools/future-agi-agent-opt.md) | [llm_agents](/tools/mpaepper-llm-agents.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 35d | 418d | | Open issues (now) | 0 | 3 | | Stars delta | Unknown | +3 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/future-agi-agent-opt/trust.md) | [trust report](/tools/mpaepper-llm-agents/trust.md) | ## Shared compatibility - **Python**: [agent-opt](/tools/future-agi-agent-opt.md) - Python runtime; [llm_agents](/tools/mpaepper-llm-agents.md) - Python runtime ## Decision facts: agent-opt - **Adopt for:** Agent-opt is tailored for teams that require automated optimization of AI workflows and support for continuous integration/continuous delivery (CI/CD), relying on Python and specific library dependencies. ## 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-opt if… - License: agent-opt is Apache-2.0, llm_agents is MIT. - Tags unique to agent-opt: agent, ai-agents, aioptimization, automation. - Also covers Evaluation & Observability. - - When your project needs seamless CI/CD integration alongside automated optimization ### Choose llm_agents if… - License: llm_agents is MIT, agent-opt 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-opt - - If your project does not require Python or if it cannot meet the specific requirement of having Python ≥ 3.10 - - In scenarios where CI/CD integration is not a priority for your AI workflow optimization ## 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-opt and llm_agents? agent-opt: Open Source Library for Automated Optimization of AI Agent Workflows. 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-opt over llm_agents? Choose agent-opt over llm_agents when License: agent-opt is Apache-2.0, llm_agents is MIT; Tags unique to agent-opt: agent, ai-agents, aioptimization, automation; Also covers Evaluation & Observability; - When your project needs seamless CI/CD integration alongside automated optimization. ### When should I choose llm_agents over agent-opt? Choose llm_agents over agent-opt when License: llm_agents is MIT, agent-opt 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-opt? - If your project does not require Python or if it cannot meet the specific requirement of having Python ≥ 3.10 - In scenarios where CI/CD integration is not a priority for your AI workflow optimization ### 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-opt or llm_agents more popular on GitHub? llm_agents has more GitHub stars (1,053 vs 71). Stars measure visibility, not whether either tool fits your constraints. ### Are agent-opt and llm_agents open source? Yes - both are open-source projects on GitHub (agent-opt: Apache-2.0, llm_agents: MIT). ### Where can I find alternatives to agent-opt or llm_agents? GraphCanon lists graph-backed alternatives at [agent-opt alternatives](/tools/future-agi-agent-opt/alternatives) and [llm_agents alternatives](/tools/mpaepper-llm-agents/alternatives) ([agent-opt markdown twin](/tools/future-agi-agent-opt/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/future-agi-agent-opt-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-opt or llm_agents? agent-opt: Steady. 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-opt and llm_agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agent-opt trust report](/tools/future-agi-agent-opt/trust); [llm_agents trust report](/tools/mpaepper-llm-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=future-agi-agent-opt`](/api/graphcanon/graph?tool=future-agi-agent-opt) - 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/_