Comparison
aqueduct vs llmflows
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.
Markdown twin · aqueduct alternatives · llmflows alternatives
GraphCanon updated Sep 29, 2026
vs
Trust & integrity
| Signal | aqueduct | llmflows |
|---|---|---|
| Maintenance | Dormant (1209d since push) As of Sep 29, 2026 · github_public_v1 | Dormant (574d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 29, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings As of Sep 18, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- aqueduct
- Orchestrate LLM and ML workloads on any cloud infrastructure using Go.
- llmflows
- Simple, Explicit and Transparent LLM Apps
Stars
- aqueduct
- 518
- llmflows
- 708
Forks
- aqueduct
- 20
- llmflows
- 35
Open issues
- aqueduct
- 11
- llmflows
- 19
Language
- aqueduct
- Go
- llmflows
- Python
Adopt for
- aqueduct
- Aqueduct is a deprecated Go-based tool for orchestrating LLM and ML workloads across various cloud infrastructures with Kubernetes support.
- llmflows
- LLMFlows is a Python library designed for creating and managing LLM applications with a focus on simplicity and transparency.
Persona
- aqueduct
- -
- llmflows
- -
Runtime
- aqueduct
- -
- llmflows
- -
License
- aqueduct
- Apache-2.0
- llmflows
- LLMFlows is distributed under the MIT license, allowing for free use, modification, and distribution.
Last pushed
- aqueduct
- Jun 7, 2023
- llmflows
- Feb 20, 2025
Categories
- aqueduct
- Inference & Serving, LLM Frameworks, Model Training
- llmflows
- Inference & Serving, LLM Frameworks
Trust and health
Days since push
- aqueduct
- 1209d
- llmflows
- 574d
Open issues (now)
- aqueduct
- 11
- llmflows
- 19
Owner type
- aqueduct
- Organization
- llmflows
- User
OSV dependency advisories
- aqueduct
- No lockfile (source not queried)
- llmflows
- Published findings
Full report
- aqueduct
- Trust report
- llmflows
- Trust report
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.
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.
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 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (RunLLM/aqueduct) · observed Sep 29, 2026
- GitHub forks (RunLLM/aqueduct) · observed Sep 29, 2026
- Last push (RunLLM/aqueduct) · observed Jun 7, 2023
- License file (Apache-2.0) · observed Sep 29, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (stoyan-stoyanov/llmflows) · observed Sep 20, 2026
- GitHub forks (stoyan-stoyanov/llmflows) · observed Sep 20, 2026
- Last push (stoyan-stoyanov/llmflows) · observed Feb 20, 2025
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: aqueduct 518 · llmflows 708 (synced Sep 29, 2026).
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 and llmflows alternatives (aqueduct markdown twin, llmflows markdown twin), 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 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; llmflows trust report.