# Gong connector Source: /docs/connectors/gong.md Use this as an implementation starting point: adapt the worked example to the requested dataset and the source documentation. See [Custom API](/docs/connectors/custom-api) for the shared SDK pattern and document-upload workflow. ## What this connector is for [#what-this-connector-is-for] Revenue intelligence platform for sales conversations, accounts, and deal insights. A connector owns access to the source. Separate syncs shape selected resources into tables for analysis and downstream transformations using the [Python SDK](/docs/reference/python/connectors-and-syncs). ## Data and tables you can pull [#data-and-tables-you-can-pull] These are representative dataset families to consider, not a claim that the worked example implements every resource. Access, fields, and availability depend on the source account, product edition, and permissions. Use the [vendor reference](https://gong.app.gong.io/settings/api/documentation) to select concrete resources. | Suggested table | One row represents | | --- | --- | | `calls` | a call | | `call_transcripts` | a call transcript | | `users` | a user | | `workspaces` | a workspace | | `call_analytics` | a call analytic | ### Relationships and extraction considerations [#relationships-and-extraction-considerations] Calls, transcripts, and CRM-linked context have different permissions and endpoint behavior. Access to call metadata does not establish permission to export every transcript. ## Connecting to Gong [#connecting-to-gong] Uses Gong API access key and secret via HTTP Basic authentication. Configure the regional API base URL for the workspace. Calls/transcripts have separate permissions and endpoints. Additional resources may require additional scopes. Connection settings belong to the connector; dataset filters and resource selection belong to each sync. See the [CLI workflow](/docs/reference/cli/overview) for registration and configuration. ### Connection settings used by the example [#connection-settings-used-by-the-example] | Setting | Requirement | Purpose | | --- | --- | --- | | `access_key` | Required; secret | API Key | | `access_key_secret` | Required; secret | Access Key Secret | | `base_url` | Required | Base Url | ### requirements.in [#requirementsin] Save these dependencies in `requirements.in` alongside `connector.py`; the example includes packages used by its sync. Install the published `vortexify` package for local development. Keep sync-specific dependencies in the sync's own `requirements.in` when splitting the implementation into separate resources. ```text vortexify==0.1.1 httpx>=0.27,<1 pandas>=2,<3 ``` No separately installed database driver is needed by this HTTP example. ## Worked example: sync `calls` [#worked-example-sync-calls] **calls** — Call metadata in a configured time window. Gong controls API page size; output is rebatched locally. The complete files below implement only this example resource. They illustrate the current SDK signatures, a separate preview, bounded DataFrame output, and source-specific extraction. They do not establish complete source coverage. Live credentials and platform ingestion have not been used to validate this guide. With default paths, place the connector files in `connectors/gong/` and sync files in `connectors/gong/syncs/calls/`. Use your project configuration if it specifies different paths. ### Connection schema: `config.json` [#connection-schema-configjson] This file contains a JSON Schema, not actual credential values. Provide those through platform configuration. ```json { "type": "object", "properties": { "access_key": { "type": "string", "title": "API Key", "minLength": 1, "x-vortexify-secret": true, "order": 0 }, "access_key_secret": { "type": "string", "minLength": 1, "x-vortexify-secret": true, "title": "Access Key Secret", "order": 1 }, "base_url": { "type": "string", "minLength": 1, "default": "https://api.gong.io/v2/", "title": "Base Url", "order": 2 } }, "required": [ "access_key", "access_key_secret", "base_url" ] } ``` ### Connector: `connector.py` [#connector-connectorpy] ```python import httpx from vortexify import Column, Connector, DiscoveredTable class GongConnector(Connector): vendor_domain = "gong.io" def client(self): if self._client is None or self._client.is_closed: self._client = httpx.Client( base_url=self.config.get("base_url", "https://api.gong.io/v2/"), headers={"Accept": "application/json"}, auth=(self.config["access_key"], self.config["access_key_secret"]), timeout=60, follow_redirects=False, ) return self._client def check(self) -> bool: response = self.client().get("users") response.raise_for_status() return True def discover(self) -> list[DiscoveredTable]: return [ DiscoveredTable( name="calls", schema=[ Column(name="id", type="string"), Column(name="title", type="string"), Column(name="started", type="timestamp"), Column(name="duration", type="number"), Column(name="document", type="string"), ], ) ] def close(self) -> None: if self._client is not None: try: self._client.close() finally: self._client = None ``` ### Sync schema: `syncs/calls/config.json` [#sync-schema-syncscallsconfigjson] ```json { "type": "object", "properties": { "start_time": { "type": "string", "title": "Start Time", "description": "ISO 8601 timestamp.", "minLength": 1, "order": 0 }, "end_time": { "type": "string", "title": "End Time", "description": "ISO 8601 timestamp.", "minLength": 1, "order": 1 } }, "required": [ "start_time", "end_time" ] } ``` ### Sync: `syncs/calls/sync.py` [#sync-syncscallssyncpy] ```python from collections.abc import Iterator from math import isfinite import json import pandas as pd from vortexify import Sync class GongCallsSync(Sync): def preview(self) -> pd.DataFrame: cfg = self.connector.config page_size = 100 path = "calls" params = { "fromDateTime": self.config["start_time"], "toDateTime": self.config["end_time"], } response = self.client.get(path, params=params) response.raise_for_status() payload = response.json() records = payload["calls"] if not isinstance(records, list) or any( (not isinstance(record, dict) for record in records) ): raise ValueError( "Unexpected API record shape; refusing a partial successful sync" ) records = records[:100] rows = [] for record in records: row = { "id": record.get("id"), "title": record.get("title"), "started": record.get("started"), "duration": record.get("duration"), "document": json.dumps(record, ensure_ascii=False, default=str), } rows.append( { key: ( json.dumps(value, ensure_ascii=False, default=str) if isinstance(value, (dict, list)) else value ) for key, value in row.items() } ) return pd.DataFrame( rows, columns=["id", "title", "started", "duration", "document"], dtype=object, ) def extract( self, *, avg_row_size_bytes: float | None = None ) -> Iterator[pd.DataFrame]: memory_mib = self.compute.memory if self.compute else None memory_mib = memory_mib if memory_mib is not None and memory_mib > 0 else 1024 target_bytes = min(16 * 1024 * 1024, max(1, memory_mib * 1024 * 1024 // 20)) batch_size = 100 if avg_row_size_bytes is not None: if not isfinite(avg_row_size_bytes) or avg_row_size_bytes <= 0: raise ValueError("avg_row_size_bytes must be finite and positive") batch_size = max(1, int(target_bytes / max(1.0, avg_row_size_bytes) / 4)) batch_size = min(batch_size, 100) cfg = self.connector.config page_size = batch_size path = "calls" params = { "fromDateTime": self.config["start_time"], "toDateTime": self.config["end_time"], } seen_pages = set() while True: response = self.client.get(path, params=params) response.raise_for_status() payload = response.json() records = payload["calls"] if not isinstance(records, list) or any( (not isinstance(record, dict) for record in records) ): raise ValueError( "Unexpected API record shape; refusing a partial successful sync" ) rows = [] for record in records: row = { "id": record.get("id"), "title": record.get("title"), "started": record.get("started"), "duration": record.get("duration"), "document": json.dumps(record, ensure_ascii=False, default=str), } rows.append( { key: ( json.dumps(value, ensure_ascii=False, default=str) if isinstance(value, (dict, list)) else value ) for key, value in row.items() } ) for start in range(0, len(rows), batch_size): yield pd.DataFrame( rows[start : start + batch_size], columns=["id", "title", "started", "duration", "document"], dtype=object, ) next_token = payload.get("records", {}).get("cursor") if not next_token: break marker = json.dumps(next_token, sort_keys=True) if marker in seen_pages: raise ValueError("API repeated a pagination token") seen_pages.add(marker) params["cursor"] = next_token ``` ### Running and extending the example [#running-and-extending-the-example] Register the connector before the sync and commit each resource separately: ```sh vortexify create connector gong vortexify create sync gong/calls ``` Edit the files, inspect `vortexify status`, and use the [CLI guide](/docs/reference/cli/overview) to commit and configure them. Registration alone does not run extraction or deploy the connector. Preview must run with a fresh connector and disposable state. The extraction implementation determines full/incremental behavior; merely setting a mode does not implement change capture. API/driver failures propagate unless explicitly handled in the code. Check source limits and single-record/response sizes as well as output batch size. ## Sources and further reading [#sources-and-further-reading] - [Vendor API and data reference](https://gong.app.gong.io/settings/api/documentation) — Resource definitions, authentication, fields, and source-specific limits. Check the selected resource's reference when extending the example. - [Vortexify connector and sync contracts](/docs/reference/python/connectors-and-syncs) — Client ownership, discovery, preview isolation, and DataFrame extraction. - [Vortexify CLI workflow](/docs/reference/cli/overview) — Project paths, resource registration, source commits, and credential configuration.