Automating Bulk Call Recording Exports using the Recording API
What This Guide Covers
This guide details the construction of a production-grade, asynchronous export pipeline that retrieves, downloads, and archives Genesys Cloud CX call recordings using the Recording API. By the end of this implementation, you will have a deterministic pagination loop, a concurrency-controlled download engine, and a structured metadata manifest that routes audio files directly to external object storage with full audit compliance.
Prerequisites, Roles & Licensing
- Licensing Tier: Genesys Cloud CX 1, CX 2, or CX 3 with the Recording feature enabled. Enterprise Archive or WEM licenses are not required for standard API exports.
- IAM Permissions:
Recording > Read,Recording > Download. Assign these to a dedicated service account user or application role. - OAuth Scopes:
recording:read,recording:download. Required for the Client Credentials grant flow. - External Dependencies: Object storage endpoint (AWS S3, Azure Blob, or GCP Cloud Storage), Python 3.9+ runtime with
aiohttpandasyncio, and a persistent job queue or scheduler (Airflow, Temporal, or cron). - Network Configuration: Outbound HTTPS (443) to
api.mypurecloud.comandmedia.mypurecloud.com. No inbound ports are required.
The Implementation Deep-Dive
1. Service Account Authentication & Scope Provisioning
Automated export pipelines must operate independently of interactive user sessions. The Client Credentials grant flow provides a machine-to-machine token with a fixed lifetime, which is essential for long-running batch operations. Interactive authorization code flows will invalidate tokens after two hours and trigger consent prompts that halt unattended jobs.
Create a dedicated application in the Genesys Cloud Developer Portal. Assign the recording:read and recording:download scopes. Attach the application to a service account user that possesses the Recording > Read and Recording > Download IAM permissions. Never attach these scopes to a shared admin account, as token rotation or user deactivation will immediately break the pipeline.
Request the access token using the following payload. Store the client credentials in a secrets manager, never in environment variables or configuration files.
POST /oauth/token
Host: api.mypurecloud.com
Content-Type: application/x-www-form-urlencoded
grant_type=client_credentials&client_id=<YOUR_CLIENT_ID>&client_secret=<YOUR_CLIENT_SECRET>
The response returns a Bearer token valid for 3600 seconds. Implement a token refresh buffer that requests a new token at 80 percent of the lifetime. If the pipeline attempts to use an expired token during a download stream, the connection terminates with a 401 Unauthorized and the partial file becomes corrupted.
The Trap: Developers frequently assign only recording:read to the OAuth application. This scope grants metadata access but explicitly denies the GET /api/v2/recordings/{recordingId}/download endpoint. The export job will successfully paginate through thousands of recording IDs, only to fail on every download attempt with a 403 Forbidden. Always validate both scopes during the initial handshake phase before spinning up worker threads.
2. Deterministic Pagination & Query Filtering
The Recording API returns metadata through GET /api/v2/recordings. The endpoint supports server-side filtering, which drastically reduces network overhead and memory consumption. You must define strict temporal boundaries using dateRangeStart and dateRangeEnd. The API rejects queries that span more than 90 days in a single request. Partition long exports into monthly or weekly windows.
Set pageSize to the maximum allowed value of 1000. The pageNumber parameter is one-indexed. The API does not return a hasMore flag. You determine exhaustion by comparing the returned array length against the requested pageSize. If the response contains fewer records than pageSize, the catalog is depleted.
GET /api/v2/recordings?dateRangeStart=2024-01-01T00:00:00.000Z&dateRangeEnd=2024-01-31T23:59:59.999Z&mediaTypes=voice&pageSize=1000&pageNumber=1&sortOrder=desc
Authorization: Bearer <ACCESS_TOKEN>
Accept: application/json
Sort by startTime in descending order. This ensures that if the export job pauses and resumes, you can safely continue from the last processed timestamp without missing records that were ingested during the pause. Avoid ascending sorts for bulk exports. New recordings arriving during an ascending sweep will cause infinite pagination loops.
The Trap: Relying solely on pageNumber without tracking the sortOrder and dateRange boundaries causes pagination drift. If a recording is modified or reprocessed by Genesys internal jobs during your export, the page contents shift. Subsequent pages return duplicates or skip records entirely. Always implement a watermark strategy. Store the startTime of the last successfully processed recording. On restart, append &dateRangeEnd=<LAST_PROCESSED_TIMESTAMP> to the query. This guarantees exactly-once processing semantics regardless of backend indexing changes.
3. Concurrency-Controlled Download Pipeline
Metadata retrieval is computationally cheap. Audio download is I/O intensive and subject to strict platform throttling. Genesys Cloud enforces a per-organization download concurrency limit. Exceeding this limit triggers 429 Too Many Requests responses and temporarily locks the download endpoint for your organization ID. This impacts live agent playback and compliance audits.
Implement a semaphore-based concurrency controller. Limit simultaneous download streams to a conservative baseline, typically between 10 and 20 concurrent connections per worker process. Scale horizontally across multiple processes rather than vertically within a single process. Use asynchronous I/O to handle network latency without blocking the event loop.
import asyncio
import aiohttp
import os
from datetime import datetime
DOWNLOAD_CONCURRENCY = 15
RETRY_MAX_ATTEMPTS = 3
BACKOFF_BASE = 2.0
async def download_recording(session: aiohttp.ClientSession, recording_id: str, token: str, output_path: str, semaphore: asyncio.Semaphore):
url = f"https://api.mypurecloud.com/api/v2/recordings/{recording_id}/download"
headers = {
"Authorization": f"Bearer {token}",
"Accept": "application/octet-stream"
}
async with semaphore:
for attempt in range(1, RETRY_MAX_ATTEMPTS + 1):
try:
async with session.get(url, headers=headers, timeout=aiohttp.ClientTimeout(total=120)) as response:
if response.status == 200:
file_size = int(response.headers.get('Content-Length', 0))
with open(output_path, 'wb') as f:
async for chunk, _ in response.content.iter_chunks():
f.write(chunk)
print(f"Successfully downloaded {recording_id} ({file_size} bytes)")
return True
elif response.status == 429:
retry_after = float(response.headers.get('Retry-After', BACKOFF_BASE ** attempt))
print(f"Rate limited on {recording_id}. Retrying in {retry_after}s")
await asyncio.sleep(retry_after)
continue
else:
print(f"Failed to download {recording_id}: {response.status} {response.reason}")
return False
except (aiohttp.ClientError, asyncio.TimeoutError) as e:
print(f"Network error on {recording_id}: {e}. Attempt {attempt}")
await asyncio.sleep(BACKOFF_BASE ** attempt)
return False
Stream the response directly to disk. Never buffer the entire audio file in memory. Voice recordings average 15 to 45 kilobytes per second. A 10-minute call consumes approximately 15 MB. Buffering thousands of records will trigger MemoryError exceptions in the worker process. Use iter_chunks() to write incrementally.
Implement exponential backoff with jitter for 429 responses. The Retry-After header dictates the mandatory wait time. Always honor it. Ignoring the header and retrying immediately causes the platform to escalate the throttle duration, potentially locking your organization out for minutes.
The Trap: Developers frequently reuse the same HTTP session across all workers without configuring connection pooling limits. The default aiohttp pool allows unlimited connections, which rapidly exhausts the OS file descriptor limit and triggers TCP TIME_WAIT accumulation. Configure aiohttp.TCPConnector(limit=50, limit_per_host=20) to enforce connection recycling. Additionally, never parallelize downloads for the same recordingId. Duplicate requests for identical media waste bandwidth and increase the probability of hitting the concurrency ceiling.
4. Manifest Generation & External Storage Routing
Storing audio files without contextual metadata destroys compliance utility. You must generate a parallel manifest that maps the recording UUID to agent identifiers, queue routing information, duration, and storage location. Object storage systems do not index file contents. The manifest becomes your queryable index.
Structure the storage path to align with common compliance retention policies. Use a hierarchical layout that separates data by year, month, and queue. This enables lifecycle policies to archive or delete entire directories without scanning individual objects.
import json
import hashlib
def generate_manifest_entry(recording_metadata: dict, storage_path: str) -> dict:
return {
"recordingId": recording_metadata["id"],
"startTime": recording_metadata["startTime"],
"endTime": recording_metadata["endTime"],
"duration": recording_metadata["duration"],
"mediaType": recording_metadata["mediaType"],
"agentId": recording_metadata.get("participants", [{}])[0].get("id"),
"queueId": recording_metadata.get("queueId"),
"wrapupCode": recording_metadata.get("wrapupCode"),
"storagePath": storage_path,
"checksum": hashlib.md5(open(storage_path, 'rb').read()).hexdigest(),
"exportTimestamp": datetime.utcnow().isoformat() + "Z"
}
Write the manifest as a newline-delimited JSON (NDJSON) file. This format allows incremental appends without rewriting the entire index. Partition manifests by export run ID. Downstream analytics pipelines can ingest NDJSON directly into data lakes or document databases.
Route files to object storage using server-side encryption. Generate pre-signed URLs only when external systems require temporary access. Never embed pre-signed URLs in the manifest if the manifest is publicly accessible. Store the manifest in a separate bucket with strict IAM policies.
The Trap: Naming files using only the recordingId UUID without verifying the file extension causes playback failures. Genesys Cloud exports voice recordings as webm containers with opus audio codec by default. Some legacy compliance systems expect mp3 or wav. If your downstream system lacks opus decoder support, the export pipeline silently succeeds while playback fails. Always validate the mediaType and container fields in the metadata. If transcoding is required, implement a post-download processing step using ffmpeg before writing to the final storage tier. Do not transcode in the download thread, as CPU-bound operations will block the I/O event loop and degrade download throughput.
Validation, Edge Cases & Troubleshooting
Edge Case 1: Pagination Drift During Long-Running Exports
The failure condition: The export job completes with duplicate recording IDs in the manifest, or skips entire pages of metadata.
The root cause: Genesys Cloud reindexes recordings during quality management reviews, transcription jobs, or compliance audits. When a recording status changes, it may shift position in the sorted result set. Ascending pagination cursors lose their position relative to the moving index.
The solution: Implement a watermark-based resume strategy. Store the startTime of the last successfully downloaded recording in a durable state store. On job restart, query with dateRangeEnd=<WATERMARK>. Deduplicate results by maintaining a bloom filter or Redis set of processed recordingId values. This guarantees exactly-once semantics regardless of backend reindexing events.
Edge Case 2: Download Concurrency Throttling & 429 Storms
The failure condition: The pipeline receives continuous 429 Too Many Requests responses, causing export throughput to drop to zero for extended periods.
The root cause: Multiple export workers, live agent playback sessions, and WEM transcription services share the same organizational download quota. A spike in concurrent requests exceeds the token bucket refill rate.
The solution: Implement adaptive rate limiting. Monitor the 429 response rate. If the error rate exceeds 5 percent of total requests, dynamically reduce the semaphore limit by half. Introduce a global jitter of 100 to 300 milliseconds between download initiations. Coordinate with platform administrators to schedule bulk exports during off-peak hours when live playback demand is minimal. Never retry 429 responses faster than the Retry-After header specifies.
Edge Case 3: Media Container Incompatibility & Codec Mismatches
The failure condition: Downloads complete successfully, but downstream compliance systems report corrupted files or unsupported formats.
The root cause: Genesys Cloud standardizes on webm with opus for voice recordings. Chat transcripts export as json. Video recordings use mp4. Mixing media types in a single export query without filtering causes the download handler to attempt binary streaming on JSON payloads, resulting in malformed files.
The solution: Segment export jobs by mediaTypes parameter. Run separate pipelines for voice, chat, and video. Validate the Content-Type header on every download response. If the header does not match the expected media type, quarantine the file and log a schema violation. Implement a validation step that checks the first 12 bytes of the downloaded file against known magic numbers (webm, mp4, json) before committing to the storage tier.