Writing a Python Script to Bulk Export All Architect Flows as YAML Using the Flows API

How-To-Articles
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Writing a Python Script to Bulk Export All Architect Flows as YAML Using the Flows API

What This Guide Covers

This guide provides a production-ready Python implementation to retrieve every Architect flow from a Genesys Cloud organization and serialize them into structured YAML files. The end result is a version-controlled directory of flows that preserves node topology, routing logic, and integration bindings for safe migration, infrastructure-as-code tracking, or disaster recovery.

Prerequisites, Roles & Licensing

  • Licensing Tier: Genesys Cloud CX 2 or CX 3. The Flows API is restricted to organizations with at least a CX 2 license. CX 1 does not include Architect flows.
  • Role Permissions: admin or a custom role with explicit flow:read and flow:view permissions. The role must also have integration:read if you plan to resolve dependency bindings.
  • OAuth Scopes: flow:read for the Flows API, oauth:client:credentials for service account token generation.
  • External Dependencies: Python 3.9+, requests (HTTP client), pyyaml (serialization), tenacity (retry/backoff logic), os and json (standard library).
  • API Base URL: https://{organization_subdomain}.mygen.com/api/v2/

The Implementation Deep-Dive

1. Service Account Authentication and Token Lifecycle Management

Bulk export operations require non-interactive authentication. Interactive OAuth flows will timeout during pagination, and personal access tokens lack the granular scope isolation required for infrastructure scripts. You must use the Client Credentials Grant with a dedicated service account.

Create a service account in the Genesys Cloud Admin portal under Admin > Users > Service Accounts. Generate a client ID and secret. The script must request an access token before initiating any flow retrieval.

HTTP Request for Token Generation:

POST /api/v2/oauth/token
Host: {organization_subdomain}.mygen.com
Content-Type: application/x-www-form-urlencoded

{
  "grant_type": "client_credentials",
  "client_id": "YOUR_CLIENT_ID",
  "client_secret": "YOUR_CLIENT_SECRET",
  "scope": "flow:read"
}

Genesys Cloud issues tokens with a strict 1-hour expiration window. A full organization export with thousands of flows will exceed this window. You cannot rely on a single token fetch at script initialization. Implement a token validation wrapper that checks the expires_in claim and refreshes automatically when the remaining lifespan drops below 5 minutes.

The Trap: Hardcoding the initial token or ignoring the expiration claim. The downstream effect is a cascading 401 Unauthorized failure mid-pagination. The script will have partially exported flows, leaving your directory in an inconsistent state. Subsequent imports will fail because the manifest will reference flows that were never fully written to disk.

Architectural Reasoning: We wrap the requests session with a token interceptor. Instead of passing the token manually on every call, we attach it to the session headers and implement a pre-request hook that validates the cached token. This centralizes authentication logic and prevents scope drift across different API endpoints.

import requests
import time
from datetime import datetime, timezone

class GenesysAuthSession(requests.Session):
    def __init__(self, org_subdomain, client_id, client_secret, scopes):
        super().__init__()
        self.org_subdomain = org_subdomain
        self.client_id = client_id
        self.client_secret = client_secret
        self.scopes = scopes
        self.token_expiry = 0
        self.access_token = None
        
    def _refresh_token(self):
        url = f"https://{self.org_subdomain}.mygen.com/api/v2/oauth/token"
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": self.scopes
        }
        response = requests.post(url, data=payload)
        response.raise_for_status()
        token_data = response.json()
        self.access_token = token_data["access_token"]
        self.token_expiry = time.time() + token_data["expires_in"] - 300  # 5-minute buffer
        self.headers.update({"Authorization": f"Bearer {self.access_token}"})
        
    def request(self, method, url, **kwargs):
        if not self.access_token or time.time() >= self.token_expiry:
            self._refresh_token()
        return super().request(method, url, **kwargs)

2. Pagination Strategy and Rate Limit Adherence

The Flows API list endpoint (GET /api/v2/flows) returns metadata only. It does not contain the full flow graph, node configurations, or routing logic. You must iterate through the paginated list to collect flow IDs, then fetch each flow individually.

Genesys Cloud uses page-based pagination for this endpoint. The maximum pageSize is 500. Requesting fewer records per page increases HTTP round trips and exhausts the tenant rate limit faster. Requesting more than 500 returns a 400 Bad Request.

HTTP Request for Flow Metadata:

GET /api/v2/flows?pageSize=500&page=1
Host: {organization_subdomain}.mygen.com
Authorization: Bearer {access_token}

The response includes a page, pageSize, and an array of flow objects. You loop until the returned array length is less than pageSize.

The Trap: Implementing parallel concurrent requests for the individual flow details without respecting tenant-level throttling. Genesys Cloud enforces a hard limit on concurrent GET requests per organization, typically around 15 to 20 requests per second for metadata-heavy endpoints. Aggressive threading triggers 429 Too Many Requests responses. The downstream effect is temporary IP throttling, which blocks other administrative APIs and disrupts active WFM integrations.

Architectural Reasoning: We use sequential pagination for the list endpoint, followed by controlled sequential retrieval for individual flows. Network latency for the Flows API is low compared to the serialization overhead of large flow graphs. A single-threaded approach with adaptive retry logic outperforms unbounded concurrency because it avoids queue buildup and memory fragmentation. We implement tenacity with exponential backoff to handle transient rate limits gracefully.

from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type

@retry(stop=stop_after_attempt(5), wait=wait_exponential(multiplier=1, min=2, max=30),
       retry=retry_if_exception_type(requests.exceptions.HTTPError))
def fetch_flows_list(session, page=1, page_size=500):
    url = f"https://{session.org_subdomain}.mygen.com/api/v2/flows"
    params = {"pageSize": page_size, "page": page}
    response = session.get(url, params=params)
    response.raise_for_status()
    return response.json()

3. Individual Flow Retrieval and YAML Serialization

Once you collect all flow IDs, you must retrieve the full definition for each flow. The individual flow endpoint returns a complex JSON object containing the flowType, nodes, integrations, and routingRules.

HTTP Request for Single Flow:

GET /api/v2/flows/{flowId}
Host: {organization_subdomain}.mygen.com
Authorization: Bearer {access_token}
Accept: application/json

Serialization to YAML requires strict configuration. Default Python YAML dumpers sort dictionary keys alphabetically. Genesys Cloud Architect relies on insertion order for certain routing blocks, decision trees, and script sequences. Alphabetical sorting will reorder node execution paths, breaking the flow logic when imported into a target environment.

The Trap: Using yaml.dump(data) without disabling key sorting. The downstream effect is a complete topology inversion for complex flows. Agents and supervisors will experience misrouted calls, broken IVR menus, and failed message workflows immediately after deployment. Debugging requires manual reconstruction of the original node order.

Architectural Reasoning: We configure the YAML dumper with sort_keys=False and default_flow_style=False. This preserves the exact JSON key order returned by the API and formats the output for human readability. We also strip transient metadata like lastUpdatedTimestamp and version before serialization to ensure the export remains stable across repeated runs.

import yaml
import os

def serialize_flow_to_yaml(flow_data, output_path):
    # Remove volatile metadata that changes on every run
    flow_data.pop("lastUpdatedTimestamp", None)
    flow_data.pop("version", None)
    flow_data.pop("createdTimestamp", None)
    
    with open(output_path, "w", encoding="utf-8") as f:
        yaml.dump(flow_data, f, default_flow_style=False, sort_keys=False, allow_unicode=True)

4. Environment Agnostic Export and Manifest Generation

Exporting flows with hardcoded tenant-specific IDs creates a fragile backup. Flow IDs, queue IDs, and integration IDs are unique per organization. When you import these flows into a development or staging environment, the references will point to non-existent resources.

You must generate a parallel manifest file that maps original IDs to environment-agnostic identifiers. The manifest allows you to run a separate ID-replacement script during the import phase. This aligns with the infrastructure-as-code patterns used in our WFM schedule export workflows.

The Trap: Exporting raw flows without a manifest and attempting manual ID replacement. The downstream effect is broken dependency chains. A flow referencing queueId: "12345678-90ab-cdef-1234-567890abcdef" will fail validation in the target environment because that queue does not exist. The import API returns a 400 Bad Request with a validation error, but it does not specify which node contains the invalid reference.

Architectural Reasoning: We parse the flow JSON to extract all referenced resource IDs (queueId, integrationId, userId, groupId). We write these to a manifest.json that tracks the source environment mapping. During the import phase, you run a secondary script that queries the target environment for equivalent resources by name and updates the YAML files accordingly. This decouples the export process from environment-specific constraints.

import json

def generate_manifest(flow_id, flow_name, flow_type, references, manifest_path):
    entry = {
        "flowId": flow_id,
        "flowName": flow_name,
        "flowType": flow_type,
        "references": references
    }
    if os.path.exists(manifest_path):
        with open(manifest_path, "r") as f:
            manifest = json.load(f)
    else:
        manifest = {"flows": []}
        
    manifest["flows"].append(entry)
    with open(manifest_path, "w") as f:
        json.dump(manifest, f, indent=2)

Consolidated Execution Script:
The following script integrates all components. It authenticates, paginates through all flows, retrieves each flow definition, serializes it to YAML, and generates a dependency manifest.

import requests
import time
import yaml
import json
import os
from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type

class GenesysAuthSession(requests.Session):
    def __init__(self, org_subdomain, client_id, client_secret):
        super().__init__()
        self.org_subdomain = org_subdomain
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_expiry = 0
        self.access_token = None
        
    def _refresh_token(self):
        url = f"https://{self.org_subdomain}.mygen.com/api/v2/oauth/token"
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "flow:read"
        }
        response = requests.post(url, data=payload)
        response.raise_for_status()
        token_data = response.json()
        self.access_token = token_data["access_token"]
        self.token_expiry = time.time() + token_data["expires_in"] - 300
        self.headers.update({"Authorization": f"Bearer {self.access_token}"})
        
    def request(self, method, url, **kwargs):
        if not self.access_token or time.time() >= self.token_expiry:
            self._refresh_token()
        return super().request(method, url, **kwargs)

@retry(stop=stop_after_attempt(5), wait=wait_exponential(multiplier=1, min=2, max=30),
       retry=retry_if_exception_type(requests.exceptions.HTTPError))
def fetch_flows_list(session, page=1, page_size=500):
    url = f"https://{session.org_subdomain}.mygen.com/api/v2/flows"
    params = {"pageSize": page_size, "page": page}
    response = session.get(url, params=params)
    response.raise_for_status()
    return response.json()

@retry(stop=stop_after_attempt(5), wait=wait_exponential(multiplier=1, min=2, max=30),
       retry=retry_if_exception_type(requests.exceptions.HTTPError))
def fetch_flow_details(session, flow_id):
    url = f"https://{session.org_subdomain}.mygen.com/api/v2/flows/{flow_id}"
    response = session.get(url)
    response.raise_for_status()
    return response.json()

def extract_references(flow_data):
    refs = {"queues": [], "integrations": [], "users": [], "groups": []}
    if "nodes" in flow_data:
        for node in flow_data["nodes"]:
            node_refs = node.get("properties", {}).get("references", [])
            for ref in node_refs:
                if "queueId" in ref: refs["queues"].append(ref["queueId"])
                if "integrationId" in ref: refs["integrations"].append(ref["integrationId"])
                if "userId" in ref: refs["users"].append(ref["userId"])
                if "groupId" in ref: refs["groups"].append(ref["groupId"])
    return refs

def export_all_flows(org_subdomain, client_id, client_secret, output_dir):
    session = GenesysAuthSession(org_subdomain, client_id, client_secret)
    os.makedirs(output_dir, exist_ok=True)
    
    page = 1
    manifest = {"flows": []}
    
    while True:
        print(f"Fetching flow metadata page {page}...")
        list_resp = fetch_flows_list(session, page=page)
        flows = list_resp.get("entities", [])
        
        if not flows:
            break
            
        for flow_meta in flows:
            flow_id = flow_meta["id"]
            flow_name = flow_meta["name"]
            flow_type = flow_meta["flowType"]
            
            print(f"Retrieving full definition for: {flow_name} ({flow_id})")
            flow_data = fetch_flow_details(session, flow_id)
            
            # Clean volatile metadata
            for key in ["lastUpdatedTimestamp", "version", "createdTimestamp", "updatedTimestamp"]:
                flow_data.pop(key, None)
                
            # Serialize to YAML
            safe_name = "".join(c for c in flow_name if c.isalnum() or c in (" ", "-", "_")).rstrip()
            yaml_filename = f"{safe_name}_{flow_id}.yaml"
            yaml_path = os.path.join(output_dir, yaml_filename)
            
            with open(yaml_path, "w", encoding="utf-8") as f:
                yaml.dump(flow_data, f, default_flow_style=False, sort_keys=False, allow_unicode=True)
                
            # Track dependencies
            refs = extract_references(flow_data)
            manifest["flows"].append({
                "flowId": flow_id,
                "flowName": flow_name,
                "flowType": flow_type,
                "filePath": yaml_filename,
                "references": refs
            })
            
        page += 1
        
    manifest_path = os.path.join(output_dir, "flow_manifest.json")
    with open(manifest_path, "w") as f:
        json.dump(manifest, f, indent=2)
        
    print(f"Export complete. {len(manifest['flows'])} flows saved to {output_dir}")

if __name__ == "__main__":
    EXPORT_DIR = "./genesys_flows_export"
    ORG_SUBDOMAIN = "your-org"
    CLIENT_ID = "your_client_id"
    CLIENT_SECRET = "your_client_secret"
    
    export_all_flows(ORG_SUBDOMAIN, CLIENT_ID, CLIENT_SECRET, EXPORT_DIR)

Validation, Edge Cases & Troubleshooting

Edge Case 1: Payload Size Throttling on Complex Decision Trees

  • The failure condition: The script receives a 503 Service Unavailable or 413 Payload Too Large response when fetching a specific flow ID.
  • The root cause: Architect flows with deeply nested decision trees, large script blocks, or extensive routing rules can exceed the default response size limits enforced by the API gateway. Genesys Cloud compresses large responses, but malformed payloads or extreme node counts can trigger gateway protection.
  • The solution: Implement a chunked download fallback or request the flow with the Accept-Encoding: gzip header explicitly. If the flow consistently fails, break the export into smaller batches by flow type. Use the flowType filter in the list endpoint (?flowType=call) to isolate problematic flows. Increase the retry backoff multiplier to 3 and add a 10-second delay between individual flow requests.

Edge Case 2: Orphaned Resource References in Exported Flows

  • The failure condition: The manifest shows valid flow exports, but the target environment import fails with Resource not found errors during validation.
  • The root cause: The source flow references a queue, integration, or user that was deleted in the source organization but remains in the flow definition. The Flows API does not purge orphaned references automatically to preserve audit trails.
  • The solution: Run a validation pass against the manifest before export. Query the target environment for each referenced ID. If an ID does not exist, flag the flow in the manifest and generate a broken_references.log. You must either recreate the missing resources in the target environment before import or strip the orphaned nodes using a pre-import sanitization script. Reference the Speech Analytics integration guide for resource mapping patterns.

Edge Case 3: Pagination Boundary Miscalculation

  • The failure condition: The script terminates early, leaving 10 to 50 flows unexported.
  • The root cause: The pagination loop checks for an empty entities array to break. If the total flow count is an exact multiple of pageSize (e.g., 500 flows with pageSize=500), the final page returns 500 records. The loop increments page and fetches the next page, which returns an empty array. This is correct. However, if the API returns a truncated page due to a transient error, the script may misinterpret it as the end of the dataset.
  • The solution: Implement a checksum validation. Count the total flows returned across all pages. Compare it against the pageCount metadata if available, or run a second pass with pageSize=1 and page={last_page+1} to verify exhaustion. Add a retry mechanism specifically for pagination endpoints that validates response length consistency before proceeding to detail retrieval.

Official References