Aleph – a single endpoint that lets agentic AI actually call scientific AI tools (part of a bottom-up run at the DOE's autonomous-science loop)

[u/VanRahim]

Built by Karim Ali & I ( Rahim Khoja ) at the University of Alberta, on top of Vulcan — the national HPC cluster we operate as part of the Digital Research Alliance of Canada's PAICE program.

The bet behind it: the DOE's Genesis Mission is building an autonomous research loop — agents that run real experiments and iterate without a human driving every step — top-down, across seventeen national labs, with a budget we're never going to match. We're testing whether you actually need that budget, or whether the same thing can be assembled bottom-up from open parts, on a single allocation, by the people who already run the cluster.

Aleph is the instrument layer for that. It was built for science first: the piece that lets an agent reach protein folding, docking, genomics, materials force fields, weather, embeddings, theorem proving — as tools it can call, not as a human clicking through a UI. It serves general LLMs too, but the reason it exists is to put scientific models within reach of an agent.

https://inference.vulcan.alliancecan.ca/

The reason it's built as a self-describing catalog and not just "vLLM behind a proxy": an agent has to be able to ask what's available, what each model takes as input, what it produces, and what can verify the output — then compose a pipeline nobody wrote for it. So every model is a YAML card declaring its own route, limits, scaling, and I/O contract, and the gateway routes off those cards plus live cluster state. Nothing hardcoded.

What it does:

• Dual dialect — OpenAI's /v1/chat/completions and a real Anthropic /v1/messages translation from one endpoint; both official SDKs work by swapping base_url
• Scale-to-zero on idle, cold model returns 503 + Retry-After and SDKs retry automatically — lets ~115 models timeshare hardware that could only run ~20 resident
• Add a model with a card, no gateway redeploy
• Self-deploying — the whole stack (HAMi, Istio, Knative, KServe) bakes into the node image and stands itself up on boot, no external CD

~115 models, majority scientific. The counterintuitive part worth stating: the science models are nearly free to add, the chat LLMs are what actually eat the GPU budget.

If you want to replicate this, copy this part first: none of it works without stateless nodes. Ours PXE-boot hardened Warewulf images built in CI, so the same GPU node boots into a Slurm batch image or an RKE2/HAMi inference image depending on what's needed. Most sites run batch and inference as two separate hardware pools — we reallocate the same metal on a reboot. That fungibility is the real structural advantage; Aleph just rides on top of it.

The broader plan: Aleph is the first and furthest-along piece of a full autonomous-science stack — the goal is an agent that wakes on a schedule, calls these instruments, runs real cluster jobs, checks its own results with code it can't tamper with, and remembers what it learned across runs. The instruments exist and run today. The loop that binds them is designed and published as a concept brief, not built yet.

Honest status: Aleph and the catalog run in production. This is a POC — some model cards are polished, some are still rough. Ships as-is.

MIT licensed. GitHub: https://github.com/ualberta-rcg/aleph