The serverless runtime for intelligence.

// Install: ai@7 disk @archildata/ai-sdk
// Set ARCHIL_API_KEY, ARCHIL_REGION, and AI_GATEWAY_API_KEY.
import { generateText, isStepCount } from "ai";
import * as archil from "disk";
import { createDiskTools } from "@archildata/ai-sdk";
 
const { disk: agentDisk } = await archil.createDisk({ name: "agent-workspace" });
 
// Give the agent your whole filesystem as tools
await generateText({
  model: "anthropic/claude-sonnet-4.6",
  prompt: "Write a short story, format it nicely and save it as story.pdf",
  tools: createDiskTools(agentDisk),
  stopWhen: isStepCount(30),
});
 
// Read the saved file after the agent finishes
const story = await agentDisk.getObject("story.pdf");

import * as archil from "disk";
 
const { disk } = await archil.createDisk({ name: "analytics" });
 
// Call 1: write a small CSV to the disk
await disk.exec("printf 'region,revenue\\nwest,120\\neast,95\\nwest,80\\n' > /mnt/archil/sales.csv");
 
// Call 2: a fresh container summarizes it with stdlib Python
const { stdout } = await disk.exec(`python3 - <<'EOF'
import csv
from collections import Counter
totals = Counter()
for row in csv.DictReader(open("/mnt/archil/sales.csv")):
    totals[row["region"]] += int(row["revenue"])
print(dict(totals))
EOF`);
 
// Example stdout: {'west': 200, 'east': 95}

import * as archil from "disk";
 
// Prerequisites: ARCHIL_API_KEY in your CI secrets, an existing disk,
// and a repository the exec container can clone. Steps run for up to 5 min.
const disk = await archil.getDisk("dsk-abc123");
const checkout = `/mnt/archil/build-${crypto.randomUUID()}`;
 
// Step 1: clone and build in a fresh container
const build = await disk.exec(
  `git clone https://github.com/your-org/app.git ${checkout} && cd ${checkout} && npm ci && npm run build`
);
if (build.exitCode !== 0) throw new Error(build.stderr);
 
// Step 2: a later step tests the same checkout, already on the disk
const test = await disk.exec(`cd ${checkout} && npm test`);
if (test.exitCode !== 0) throw new Error(test.stderr);

# Prerequisite: an existing disk, "training-data", that already holds
# your dataset, and a mount token for that disk.
 
# On your own GPU host: install the client
curl -s https://archil.com/install | sh
 
# Mount the dataset read-only
sudo mkdir -p /mnt/training-data
export ARCHIL_MOUNT_TOKEN="<disk-token>"
sudo --preserve-env=ARCHIL_MOUNT_TOKEN archil mount training-data /mnt/training-data \
  --region aws-us-east-1 --read-only
 
# Your training job reads it like any local directory
ls /mnt/training-data