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By Alexey Volkov, Bo Li, Ben Chen, Maksym Yezhov, Pete Luferenko, and Volv Grebennikov – Shopify
Machine learning work is full of loops: form a hypothesis, build a pipeline, run it, read the metrics, adjust, repeat. Tangle is already where Shopify’s ML experiments run, giving engineers a shared platform to build and execute pipelines.
Tangent is an autonomous agent that orchestrates ML experimentation workflows on top of Tangle, deciding what to run, running it, and reporting back. Around Tangent we built a Linux-based platform for hosting these agents securely so they can persist, work remotely, and reach the services they need without ever holding a credential. This is done using containerized isolation, a certificate-issuing proxy, and per-instance persistent storage (details below).
Tangle is an open source, platform-agnostic ML experimentation platform with a powerful drag-and-drop visual editor. Users drag components onto a canvas, wire outputs to inputs to form a pipeline graph, and submit it for execution locally or in the cloud. A caching layer skips or reuses previously executed steps, including steps still in flight, so iteration is fast and cheap. All pipeline runs are stored forever (including graphs, components, and logs), making pipelines reproducible even after years have passed.
Because runs and caches are shared, teammates can inspect, copy, and modify each other’s pipelines in seconds, with no cloning a private notebook and hoping the environment matches. Tangle has similarities with other OSS tools like Airflow and Kubeflow Pipelines; its caching model and visual editor are what make fast experimentation loops possible. Any containerized CLI program written in any language can be used as a Tangle component, and those components exchange data as files in any format (CSV, Parquet, JSON, etc.).
Tangle composes components the way shell scripts, makefiles, and *nix pipes do. More information about Tangle is available at tangleml.com and you can immediately try it out.
Tangle’s pipeline canvas and architecture.
Tangent is an autonomous ML engineering agent designed to accelerate your Tangle experimentation workflows. It follows the pattern Andrej Karpathy recently popularized as autoresearch. Tangent takes that idea from a single training script on one GPU to full experiment pipelines running on Tangle, with a fleet of specialized subagents, gated checkpoints, and persistent memory. You point it at a scenario, a model, a metric to improve, a dataset, and it iterates: it analyzes results, forms hypotheses, modifies pipelines, submits runs, monitors them, and synthesizes what it learned. How much you delegate is up to you. Tangent works interactively, so you can hand it a single step (build this component, debug this failed run, analyze these metrics), or you can hand off the entire loop with one command and let it run:
Screenshot of the eight-step loop progressing.
Under the hood is an eight-step loop with gated checkpoints. The agent won’t advance until every item on a step’s checklist passes, and it reloads its instructions and context at each gate so it doesn’t drift over a long run. Memory is persistent and plain-text: a MEMORY.md holds the best-known configuration and hard-won lessons; daily session logs capture the play-by-play, and per-run learnings are archived to object storage.
Tangent’s brain is a skill, written in Markdown. The entry point is a single SKILL.md, backed by a fleet of subagent skills: a researcher, a builder, a debugger, a reviewer, and more. Because skills are just files, they’re portable, reviewable in a pull request, and harness-agnostic, the same skill drives whichever coding agent you prefer.
By Alexey Volkov, Bo Li, Ben Chen, Maksym Yezhov, Pete Luferenko, and Volv Grebennikov – Shopify
Machine learning work is full of loops: form a hypothesis, build a pipeline, run it, read the metrics, adjust, repeat. Tangle is already where Shopify’s ML experiments run, giving engineers a shared platform to build and execute pipelines.
Tangent is an autonomous agent that orchestrates ML experimentation workflows on top of Tangle, deciding what to run, running it, and reporting back. Around Tangent we built a Linux-based platform for hosting these agents securely so they can persist, work remotely, and reach the services they need without ever holding a credential. This is done using containerized isolation, a certificate-issuing proxy, and per-instance persistent storage (details below).
Tangle is an open source, platform-agnostic ML experimentation platform with a powerful drag-and-drop visual editor. Users drag components onto a canvas, wire outputs to inputs to form a pipeline graph, and submit it for execution locally or in the cloud. A caching layer skips or reuses previously executed steps, including steps still in flight, so iteration is fast and cheap. All pipeline runs are stored forever (including graphs, components, and logs), making pipelines reproducible even after years have passed.
Because runs and caches are shared, teammates can inspect, copy, and modify each other’s pipelines in seconds, with no cloning a private notebook and hoping the environment matches. Tangle has similarities with other OSS tools like Airflow and Kubeflow Pipelines; its caching model and visual editor are what make fast experimentation loops possible. Any containerized CLI program written in any language can be used as a Tangle component, and those components exchange data as files in any format (CSV, Parquet, JSON, etc.).
Tangle composes components the way shell scripts, makefiles, and *nix pipes do. More information about Tangle is available at tangleml.com and you can immediately try it out.
Tangle’s pipeline canvas and architecture.
Tangent is an autonomous ML engineering agent designed to accelerate your Tangle experimentation workflows. It follows the pattern Andrej Karpathy recently popularized as autoresearch. Tangent takes that idea from a single training script on one GPU to full experiment pipelines running on Tangle, with a fleet of specialized subagents, gated checkpoints, and persistent memory. You point it at a scenario, a model, a metric to improve, a dataset, and it iterates: it analyzes results, forms hypotheses, modifies pipelines, submits runs, monitors them, and synthesizes what it learned. How much you delegate is up to you. Tangent works interactively, so you can hand it a single step (build this component, debug this failed run, analyze these metrics), or you can hand off the entire loop with one command and let it run:
Screenshot of the eight-step loop progressing.
Under the hood is an eight-step loop with gated checkpoints. The agent won’t advance until every item on a step’s checklist passes, and it reloads its instructions and context at each gate so it doesn’t drift over a long run. Memory is persistent and plain-text: a MEMORY.md holds the best-known configuration and hard-won lessons; daily session logs capture the play-by-play, and per-run learnings are archived to object storage.
Tangent’s brain is a skill, written in Markdown. The entry point is a single SKILL.md, backed by a fleet of subagent skills: a researcher, a builder, a debugger, a reviewer, and more. Because skills are just files, they’re portable, reviewable in a pull request, and harness-agnostic, the same skill drives whichever coding agent you prefer.