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aqueduct

RunLLM/aqueduct

Orchestrate LLM and ML workloads on any cloud infrastructure using Go.

GraphCanon updated 3w · GitHub synced 3w

517 stars20 forksLast push 3y Go Apache-2.0

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  • Avoid if active project maintenance or community support is required as Aqueduct is no longer maintained.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 (1152d since push)
As of 3w
Provenance
Not a fork · Organization account
As of 3w
Security (OSV)
No lockfile
As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

go get github.com/RunLLM/aqueduct
pkg.go.dev

Similar 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 · Aug 3, 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, you can see it on the UI: 



To see how to build your first workflow, check out our **[quickstart guide! →](https://docs.aqueducthq.com/quickstart-guide)**

For agents

This page has a .md twin and JSON over the API.

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