Managing Agents Using the Agents API - Archetype AI Documentation

Creating an Agent Using an Agent Blueprint

Use the Create Bundle endpoint to create an agent bundle using an agent blueprint.

curl -X POST -H "Authorization: Bearer $ATAI_API_KEY" -H "Content-Type: application/json" \
  "$ATAI_API_ENDPOINT/agents/bundle" -d '{
    "blueprint": "osm",
    "name": "My agent name",
    "values": {"window_size": 16, "step_size": 1,
               "sample_rate_interval_tolerance": 10.0, "max_temporal_gap": 10.0},
    "artifacts": {"fit-classifier": "s3://my-aws-folder/fit-classifier-name.safetensors"}
  }'

import os

import requests

api_key = os.environ["ATAI_API_KEY"]
endpoint = os.environ["ATAI_API_ENDPOINT"]

payload = {
  "blueprint": "osm",
  "name": "My agent name",
  "values": {
    "window_size": 16,
    "step_size": 1,
    "sample_rate_interval_tolerance": 10.0,
    "max_temporal_gap": 10.0,
  },
  "artifacts": {
    "fit-classifier": "s3://my-aws-folder/fit-classifier-name.safetensors"
  },
}

response = requests.post(
  f"{endpoint}/agents/bundle",
  headers={
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json",
  },
  json=payload,
  timeout=60
)

const apiKey = process.env.ATAI_API_KEY;
const endpoint = process.env.ATAI_API_ENDPOINT;

const payload = {
  blueprint: "osm",
  name: "My agent name",
  values: {
    window_size: 16,
    step_size: 1,
    sample_rate_interval_tolerance: 10.0,
    max_temporal_gap: 10.0,
  },
  artifacts: {
    "fit-classifier": "s3://my-aws-folder/fit-classifier-name.safetensors",
  },
};

const response = await fetch(`${endpoint}/agents/bundle`, {
  method: "POST",
  headers: {
    Authorization: `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify(payload)
});

This example creates an agent based on the Operational State Monitoring (OSM) agent blueprint, giving the new agent the name “My agent name”, providing a Safetensors file as a fit classifier artifact, and providing several custom configuration options (window_size, step_size, sample_rate_interval_tolerance, and max_temporal_gap).

Running an Agent

Once an agent has been created, you can run it using the Run Bundle endpoint.

curl -X POST -H "Authorization: Bearer $ATAI_API_KEY" -H "Content-Type: application/json" \
  "$ATAI_API_ENDPOINT/agents/bundle/$BUNDLE_ID/run" -d '{
    "connectors": {"source": [{"type": "file", "id": "sample-data.csv"}]}
  }'

import os

import requests

api_key = os.environ["ATAI_API_KEY"]
endpoint = os.environ["ATAI_API_ENDPOINT"]
bundle_id = os.environ["BUNDLE_ID"]

payload = {
  "connectors": {
    "source": [{"type": "file", "id": "sample-data.csv"}]
  }
}

response = requests.post(
  f"{endpoint}/agents/bundle/{bundle_id}/run",
  headers={
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json",
  },
  json=payload,
  timeout=60
)

const apiKey = process.env.ATAI_API_KEY;
const endpoint = process.env.ATAI_API_ENDPOINT;
const bundleId = process.env.BUNDLE_ID;

const payload = {
  connectors: {
    source: [{ type: "file", id: "sample-data.csv" }],
  },
};

const response = await fetch(`${endpoint}/agents/bundle/${bundleId}/run`, {
  method: "POST",
  headers: {
    Authorization: `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify(payload)
});

This code uses the agent to process the data in a CSV file named sample-data.csv which has already been uploaded to the Archetype Platform using the Files API.

Managing Agents Using the Developer Console

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