--- title: "aqueduct vs llmflows" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/runllm-aqueduct-vs-stoyan-stoyanov-llmflows" tools: ["runllm-aqueduct", "stoyan-stoyanov-llmflows"] --- # aqueduct vs llmflows *GraphCanon updated Sep 29, 2026* ## Verdict Pick aqueduct if aqueduct is a deprecated Go-based tool for orchestrating LLM and ML workloads across various cloud infrastructures with Kubernetes support; pick llmflows if lLMFlows is a Python library designed for creating and managing LLM applications with a focus on simplicity and transparency. [aqueduct](https://aqueducthq.com) reports 518 GitHub stars, 20 forks, and 11 open issues, last pushed Jun 7, 2023. [llmflows](https://llmflows.readthedocs.io) has 708 stars, 35 forks, and 19 open issues, last pushed Feb 20, 2025. Figures are from public GitHub metadata via [aqueduct's repository](https://github.com/RunLLM/aqueduct) and [llmflows's repository](https://github.com/stoyan-stoyanov/llmflows). | | [aqueduct](/tools/runllm-aqueduct.md) | [llmflows](/tools/stoyan-stoyanov-llmflows.md) | | --- | --- | --- | | Tagline | Orchestrate LLM and ML workloads on any cloud infrastructure using Go. | Simple, Explicit and Transparent LLM Apps | | Stars | 518 | 708 | | Forks | 20 | 35 | | Open issues | 11 | 19 | | Language | Go | Python | | Adopt for | Aqueduct is a deprecated Go-based tool for orchestrating LLM and ML workloads across various cloud infrastructures with Kubernetes support. | LLMFlows is a Python library designed for creating and managing LLM applications with a focus on simplicity and transparency. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | LLMFlows is distributed under the MIT license, allowing for free use, modification, and distribution. | | Categories | Inference & Serving, LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aqueduct](/tools/runllm-aqueduct.md) | [llmflows](/tools/stoyan-stoyanov-llmflows.md) | | --- | --- | --- | | Days since push | 1209d | 574d | | Open issues (now) | 11 | 19 | | Owner type | Organization | User | | Full report | [trust report](/tools/runllm-aqueduct/trust.md) | [trust report](/tools/stoyan-stoyanov-llmflows/trust.md) | ## Decision facts: aqueduct - **Adopt for:** Aqueduct is a deprecated Go-based tool for orchestrating LLM and ML workloads across various cloud infrastructures with Kubernetes support. ## Decision facts: llmflows - **Pricing:** freemium - The library is free to use, but additional services or support might incur costs. - **Requirements:** Min 2 GB RAM; Python environment is required. - **Adopt for:** LLMFlows is a Python library designed for creating and managing LLM applications with a focus on simplicity and transparency. - **License detail:** LLMFlows is distributed under the MIT license, allowing for free use, modification, and distribution. ## Choose when ### Choose aqueduct if… - aqueduct is primarily Go; llmflows is Python. - License: aqueduct is Apache-2.0, llmflows is MIT. - Tags unique to aqueduct: data, data-science, kubernetes, ml. - Also covers Model Training. - When you need to deploy legacy workflows that depend on Aqueduct's specific implementation of custom ops for resource allocation and training. ### Choose llmflows if… - llmflows is primarily Python; aqueduct is Go. - License: llmflows is MIT, aqueduct is Apache-2.0. - Pricing: The library is free to use, but additional services or support might incur costs.. - Requirements: Min 2 GB RAM; Python environment is required.. - Tags unique to llmflows: chatgpt, gpt-4, llm-inference, llmops. - When you need a straightforward and transparent approach to building LLM applications, LLMFlows offers a simple framework that prioritizes clarity in the development process. ## When NOT to use aqueduct - Avoid if active project maintenance or community support is required as Aqueduct is no longer maintained. - Skip this tool for new projects focusing on state-of-the-art ML orchestration, opting instead for actively supported alternatives. ## When NOT to use llmflows - Avoid LLMFlows if you require advanced customization options that go beyond its simple and explicit design philosophy. - Do not use LLMFlows if your project demands a more complex framework that integrates a wide range of third-party services, as LLMFlows focuses on simplicity and may lack extensive integration features ## Common questions ### What is the difference between aqueduct and llmflows? aqueduct: Orchestrate LLM and ML workloads on any cloud infrastructure using Go.. llmflows: Simple, Explicit and Transparent LLM Apps. See the comparison table for live GitHub stats and shared categories. ### When should I choose aqueduct over llmflows? Choose aqueduct over llmflows when aqueduct is primarily Go; llmflows is Python; License: aqueduct is Apache-2.0, llmflows is MIT; Tags unique to aqueduct: data, data-science, kubernetes, ml; Also covers Model Training; When you need to deploy legacy workflows that depend on Aqueduct's specific implementation of custom ops for resource allocation and training. ### When should I choose llmflows over aqueduct? Choose llmflows over aqueduct when llmflows is primarily Python; aqueduct is Go; License: llmflows is MIT, aqueduct is Apache-2.0; Pricing: The library is free to use, but additional services or support might incur costs.; Requirements: Min 2 GB RAM; Python environment is required.; Tags unique to llmflows: chatgpt, gpt-4, llm-inference, llmops; When you need a straightforward and transparent approach to building LLM applications, LLMFlows offers a simple framework that prioritizes clarity in the development process. ### When should I avoid aqueduct? Avoid if active project maintenance or community support is required as Aqueduct is no longer maintained. Skip this tool for new projects focusing on state-of-the-art ML orchestration, opting instead for actively supported alternatives. ### When should I avoid llmflows? Avoid LLMFlows if you require advanced customization options that go beyond its simple and explicit design philosophy. Do not use LLMFlows if your project demands a more complex framework that integrates a wide range of third-party services, as LLMFlows focuses on simplicity and may lack extensive integration features ### Is aqueduct or llmflows more popular on GitHub? llmflows has more GitHub stars (708 vs 518). Stars measure visibility, not whether either tool fits your constraints. ### Are aqueduct and llmflows open source? Yes - both are open-source projects on GitHub (aqueduct: Apache-2.0, llmflows: MIT). ### Where can I find alternatives to aqueduct or llmflows? GraphCanon lists graph-backed alternatives at [aqueduct alternatives](/tools/runllm-aqueduct/alternatives) and [llmflows alternatives](/tools/stoyan-stoyanov-llmflows/alternatives) ([aqueduct markdown twin](/tools/runllm-aqueduct/alternatives.md), [llmflows markdown twin](/tools/stoyan-stoyanov-llmflows/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/runllm-aqueduct-vs-stoyan-stoyanov-llmflows.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aqueduct or llmflows? aqueduct: Dormant. llmflows: 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 aqueduct and llmflows? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aqueduct trust report](/tools/runllm-aqueduct/trust); [llmflows trust report](/tools/stoyan-stoyanov-llmflows/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=runllm-aqueduct`](/api/graphcanon/graph?tool=runllm-aqueduct) - 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/_