Automated Prompt Replication

Bulk-copy Portkey prompts and point them at a new model using the Admin API—no manual duplication in the UI.

Use this workflow when many prompts target one model (for example Claude 3.7) and you want replicas that keep the same template and settings but run on another model (for example Claude 3.5 Sonnet on Bedrock). The List prompts, Retrieve prompt, and Create prompt endpoints drive the migration.

Auth: Use an Admin API key or a Workspace API key with prompt permissions. Send x-portkey-api-key on every request.

When to use this

• Migrate dozens of prompts to a new default model after a provider or catalog change
• Keep originals untouched by creating named replicas (for example append -replica)
• Automate what would otherwise be repeated copy-paste in Prompt Studio

How it works

1. List all prompts and collect their IDs.
2. Retrieve each prompt’s full definition (template string, parameters, virtual_key, metadata, etc.).
3. Create a new prompt per ID with the same body fields and a new model value.

Replace the example model string (anthropic.claude-3-5-sonnet) with the exact model identifier your workspace uses.

---

Step 1: List prompts and collect IDs

Python

import requests

BASE = "https://api.portkey.ai/v1"
headers = {"x-portkey-api-key": "YOUR_API_KEY"}

r = requests.get(f"{BASE}/prompts", headers=headers)
r.raise_for_status()

prompt_ids = [item["id"] for item in r.json()["data"]]
print(prompt_ids)

Node.js

async function main() {
  const BASE = 'https://api.portkey.ai/v1';
  const headers = { 'x-portkey-api-key': process.env.PORTKEY_API_KEY ?? 'YOUR_API_KEY' };

  const res = await fetch(`${BASE}/prompts`, { headers });
  if (!res.ok) throw new Error(`List prompts failed: ${res.status}`);
  const { data } = await res.json();
  const promptIds = data.map((row) => row.id);
  console.log(promptIds);
}

main().catch(console.error);

Python (steps 2–3): Run the snippets in order in the same session so BASE, headers, and prompt_ids / prompt_data stay in scope. Node.js: Examples use native fetch (Node.js 18+).

---

Step 2: Fetch one prompt’s full configuration

Use this shape to see which fields the API returns before building the create payload (names vary slightly by version; always log once and adjust keys if needed).

Python

prompt_id = prompt_ids[0]
url = f"{BASE}/prompts/{prompt_id}"

prompt_data = requests.get(url, headers=headers).json()
print(prompt_data)

Node.js

async function main() {
  const BASE = 'https://api.portkey.ai/v1';
  const headers = { 'x-portkey-api-key': process.env.PORTKEY_API_KEY ?? 'YOUR_API_KEY' };

  const listRes = await fetch(`${BASE}/prompts`, { headers });
  const { data } = await listRes.json();
  const promptId = data[0].id;

  const promptData = await fetch(`${BASE}/prompts/${promptId}`, { headers }).then((r) =>
    r.json()
  );
  console.log(promptData);
}

main().catch(console.error);

---

Step 3: Create a single replicated prompt

The replica reuses template content and metadata, overrides model, and uses a distinct name so it does not collide with the original.

Python

TARGET_MODEL = "anthropic.claude-3-5-sonnet"

payload = {
    "name": prompt_data["name"] + "-replica",
    "collection_id": prompt_data["collection_id"],
    "string": prompt_data["string"],
    "parameters": prompt_data["parameters"],
    "virtual_key": prompt_data["virtual_key"],
    "model": TARGET_MODEL,
    "version_description": prompt_data.get(
        "prompt_version_description", "Replicated prompt"
    ),
    "template_metadata": prompt_data["template_metadata"],
}

r = requests.post(f"{BASE}/prompts", json=payload, headers=headers)
r.raise_for_status()
print(r.json())

Node.js

async function main() {
  const BASE = 'https://api.portkey.ai/v1';
  const headers = { 'x-portkey-api-key': process.env.PORTKEY_API_KEY ?? 'YOUR_API_KEY' };
  const TARGET_MODEL = 'anthropic.claude-3-5-sonnet';

  const listRes = await fetch(`${BASE}/prompts`, { headers });
  const { data } = await listRes.json();
  const promptData = await fetch(`${BASE}/prompts/${data[0].id}`, { headers }).then((r) =>
    r.json()
  );

  const payload = {
    name: `${promptData.name}-replica`,
    collection_id: promptData.collection_id,
    string: promptData.string,
    parameters: promptData.parameters,
    virtual_key: promptData.virtual_key,
    model: TARGET_MODEL,
    version_description:
      promptData.prompt_version_description ?? 'Replicated prompt',
    template_metadata: promptData.template_metadata,
  };

  const created = await fetch(`${BASE}/prompts`, {
    method: 'POST',
    headers: { ...headers, 'Content-Type': 'application/json' },
    body: JSON.stringify(payload),
  }).then((r) => r.json());
  console.log(created);
}

main().catch(console.error);

---

Full loop: replicate every prompt

Python

import requests

BASE = "https://api.portkey.ai/v1"
TARGET_MODEL = "anthropic.claude-3-5-sonnet"

headers = {"x-portkey-api-key": "YOUR_API_KEY"}

list_res = requests.get(f"{BASE}/prompts", headers=headers)
list_res.raise_for_status()
prompt_ids = [row["id"] for row in list_res.json()["data"]]

for prompt_id in prompt_ids:
    data = requests.get(f"{BASE}/prompts/{prompt_id}", headers=headers).json()

    payload = {
        "name": data["name"] + "-replica",
        "collection_id": data["collection_id"],
        "string": data["string"],
        "parameters": data["parameters"],
        "virtual_key": data["virtual_key"],
        "model": TARGET_MODEL,
        "version_description": data.get(
            "prompt_version_description", "Replicated"
        ),
        "template_metadata": data["template_metadata"],
    }

    r = requests.post(f"{BASE}/prompts", json=payload, headers=headers)
    r.raise_for_status()
    print(r.json())

Node.js

async function main() {
  const BASE = 'https://api.portkey.ai/v1';
  const TARGET_MODEL = 'anthropic.claude-3-5-sonnet';
  const headers = { 'x-portkey-api-key': process.env.PORTKEY_API_KEY ?? 'YOUR_API_KEY' };

  const listRes = await fetch(`${BASE}/prompts`, { headers });
  if (!listRes.ok) throw new Error(`List failed: ${listRes.status}`);
  const { data: rows } = await listRes.json();
  const promptIds = rows.map((r) => r.id);

  for (const promptId of promptIds) {
    const data = await fetch(`${BASE}/prompts/${promptId}`, { headers }).then((r) =>
      r.json()
    );

    const payload = {
      name: `${data.name}-replica`,
      collection_id: data.collection_id,
      string: data.string,
      parameters: data.parameters,
      virtual_key: data.virtual_key,
      model: TARGET_MODEL,
      version_description: data.prompt_version_description ?? 'Replicated',
      template_metadata: data.template_metadata,
    };

    const r = await fetch(`${BASE}/prompts`, {
      method: 'POST',
      headers: { ...headers, 'Content-Type': 'application/json' },
      body: JSON.stringify(payload),
    });
    if (!r.ok) throw new Error(`Create failed for ${promptId}: ${r.status}`);
    console.log(await r.json());
  }
}

main().catch(console.error);

Field names: If retrieve responses use different keys (for example nested version objects), log one response and map fields explicitly. Null collection_id: Omit or pass null only if the create API accepts it for your workspace. Rate limits: Add backoff or batching for very large prompt libraries.

After replication

• Point applications at the new prompt IDs or keep names predictable (for example *-replica) and resolve by name if your tooling supports it.
• For runtime calls, use the Prompt API (/v1/prompts/{promptId}/completions) with the replica’s ID.

Summary

Step	Action
1	GET /v1/prompts → collect IDs
2	GET /v1/prompts/{id} → read full config
3	POST /v1/prompts → same body + new model + new name

Bulk replication avoids manual duplication, keeps templates aligned, and makes model upgrades repeatable across the workspace.