---
title: "llm-workflow-engine vs aqueduct"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/llm-workflow-engine-llm-workflow-engine-vs-runllm-aqueduct"
tools: ["llm-workflow-engine-llm-workflow-engine", "runllm-aqueduct"]
---

# llm-workflow-engine vs aqueduct

*GraphCanon updated Sep 29, 2026*

## Verdict

Pick llm-workflow-engine if critical Decision Factors for 'llm-workflow-engine'; pick aqueduct if aqueduct is a deprecated Go-based tool for orchestrating LLM and ML workloads across various cloud infrastructures with Kubernetes support.

[llm-workflow-engine](https://github.com/llm-workflow-engine/llm-workflow-engine) reports 3.7k GitHub stars, 467 forks, and 3 open issues, last pushed Sep 5, 2026. [aqueduct](https://aqueducthq.com) has 518 stars, 20 forks, and 11 open issues, last pushed Jun 7, 2023. Figures are from public GitHub metadata via [llm-workflow-engine's repository](https://github.com/llm-workflow-engine/llm-workflow-engine) and [aqueduct's repository](https://github.com/RunLLM/aqueduct).

| | [llm-workflow-engine](/tools/llm-workflow-engine-llm-workflow-engine.md) | [aqueduct](/tools/runllm-aqueduct.md) |
| --- | --- | --- |
| Tagline | Power CLI and Workflow manager for LLMs (core package) | Orchestrate LLM and ML workloads on any cloud infrastructure using Go. |
| Stars | 3,715 | 518 |
| Forks | 467 | 20 |
| Open issues | 3 | 11 |
| Language | Python | Go |
| Adopt for | Critical Decision Factors for 'llm-workflow-engine' | Aqueduct is a deprecated Go-based tool for orchestrating LLM and ML workloads across various cloud infrastructures with Kubernetes support. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT-licensed, offering flexibility under non-restrictive open-source terms. | Apache-2.0 |
| Categories | Developer Tools, LLM Frameworks | Inference & Serving, LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [llm-workflow-engine](/tools/llm-workflow-engine-llm-workflow-engine.md) | [aqueduct](/tools/runllm-aqueduct.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Dormant (18%) |
| Days since push | 1d | 1209d |
| Open issues (now) | 3 | 11 |
| Stars delta | -5 (30d) | +1 (30d) |
| Full report | [trust report](/tools/llm-workflow-engine-llm-workflow-engine/trust.md) | [trust report](/tools/runllm-aqueduct/trust.md) |

## Decision facts: llm-workflow-engine

- **Requirements:** Built for a Python environment which may not fully cater to workflows outside of this language.
- **Adopt for:** Critical Decision Factors for 'llm-workflow-engine'
- **License detail:** MIT-licensed, offering flexibility under non-restrictive open-source terms.

## 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.

## Choose when

### Choose llm-workflow-engine if…

- llm-workflow-engine is primarily Python; aqueduct is Go.
- License: llm-workflow-engine is MIT, aqueduct is Apache-2.0.
- Requirements: Built for a Python environment which may not fully cater to workflows outside of this language..
- Tags unique to llm-workflow-engine: chatbot, chatgpt, gpt-3, gpt3.
- Also covers Developer Tools.
- llm-workflow-engine ships Docker support for self-hosted deployment.
- When developing workflows around Large Language Models (LLMs), particularly if your projects are Python-based, to streamline model integration and management via CLI.

### Choose aqueduct if…

- aqueduct is primarily Go; llm-workflow-engine is Python.
- License: aqueduct is Apache-2.0, llm-workflow-engine is MIT.
- Tags unique to aqueduct: ai, data, data-science, kubernetes.
- Also covers Inference & Serving, 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 NOT to use llm-workflow-engine

- Avoid using llm-workflow-engine if you need deep integrations with proprietary systems that are incompatible with MIT licensing terms and conditions.
- Do not use this tool if your primary development environment is not Python-based, as the effectiveness of the CLI and workflow manager might be limited without Python support.

## 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.

## Common questions

### What is the difference between llm-workflow-engine and aqueduct?

llm-workflow-engine: Power CLI and Workflow manager for LLMs (core package). aqueduct: Orchestrate LLM and ML workloads on any cloud infrastructure using Go.. See the comparison table for live GitHub stats and shared categories.

### When should I choose llm-workflow-engine over aqueduct?

Choose llm-workflow-engine over aqueduct when llm-workflow-engine is primarily Python; aqueduct is Go; License: llm-workflow-engine is MIT, aqueduct is Apache-2.0; Requirements: Built for a Python environment which may not fully cater to workflows outside of this language.; Tags unique to llm-workflow-engine: chatbot, chatgpt, gpt-3, gpt3; Also covers Developer Tools; llm-workflow-engine ships Docker support for self-hosted deployment; When developing workflows around Large Language Models (LLMs), particularly if your projects are Python-based, to streamline model integration and management via CLI.

### When should I choose aqueduct over llm-workflow-engine?

Choose aqueduct over llm-workflow-engine when aqueduct is primarily Go; llm-workflow-engine is Python; License: aqueduct is Apache-2.0, llm-workflow-engine is MIT; Tags unique to aqueduct: ai, data, data-science, kubernetes; Also covers Inference & Serving, 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 avoid llm-workflow-engine?

Avoid using llm-workflow-engine if you need deep integrations with proprietary systems that are incompatible with MIT licensing terms and conditions. Do not use this tool if your primary development environment is not Python-based, as the effectiveness of the CLI and workflow manager might be limited without Python support.

### 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.

### Is llm-workflow-engine or aqueduct more popular on GitHub?

llm-workflow-engine has more GitHub stars (3,715 vs 518). Stars measure visibility, not whether either tool fits your constraints.

### Are llm-workflow-engine and aqueduct open source?

Yes - both are open-source projects on GitHub (llm-workflow-engine: MIT, aqueduct: Apache-2.0).

### Where can I find alternatives to llm-workflow-engine or aqueduct?

GraphCanon lists graph-backed alternatives at [llm-workflow-engine alternatives](/tools/llm-workflow-engine-llm-workflow-engine/alternatives) and [aqueduct alternatives](/tools/runllm-aqueduct/alternatives) ([llm-workflow-engine markdown twin](/tools/llm-workflow-engine-llm-workflow-engine/alternatives.md), [aqueduct markdown twin](/tools/runllm-aqueduct/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/llm-workflow-engine-llm-workflow-engine-vs-runllm-aqueduct.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, llm-workflow-engine or aqueduct?

llm-workflow-engine: Very active. aqueduct: 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 llm-workflow-engine and aqueduct?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-workflow-engine trust report](/tools/llm-workflow-engine-llm-workflow-engine/trust); [aqueduct trust report](/tools/runllm-aqueduct/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=llm-workflow-engine-llm-workflow-engine`](/api/graphcanon/graph?tool=llm-workflow-engine-llm-workflow-engine)
- 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/_