GraphCanon updated Sep 29, 2026 · GitHub synced Sep 29, 2026
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Decision brief
Aqueduct is a deprecated Go-based tool for orchestrating LLM and ML workloads across various cloud infrastructures with Kubernetes support.
Good fit when
- When you need to deploy legacy workflows that depend on Aqueduct's specific implementation of custom ops for resource allocation and training.
- For scenarios where the team already has expertise in Go language and wishes to leverage available codebases integrating Aqueduct.
Avoid when
- Avoid if active project maintenance or community support is required as Aqueduct is no longer maintained.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.
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Dormant (1209d since push)
- As of Sep 29, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 29, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
go get github.com/RunLLM/aqueductSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Aqueduct is a deprecated tool for orchestrating machine learning and large language model workloads across diverse cloud infrastructures with support for Kubernetes orchestration, resource allocation like GPUs, and monitoring.
Capability facts
- Languages
- go
Source: github.language · Sep 29, 2026
Categories
Tags
README
Or write a custom op on your favorite infrastructure! @op( engine='kubernetes', Get a GPU. resources={'gpu resource name': 'nvidia.com/gpu'} ) def train(featurized logs): return model.train(features) Train your model. train(features) ``` Once you publish this workflow to Aqueduct...
For agents
This page has a .md twin and JSON over the API.