airbyte-agent-zendesk-chat 0.1.5

Airbyte Zendesk-Chat Connector for AI platforms

Zendesk-Chat agent connector

Zendesk Chat enables real-time customer support through live chat. This connector provides access to chat transcripts, agents, departments, shortcuts, triggers, and other chat configuration data for analytics and support insights.

Supported Entities

• accounts: Account information and billing details
• agents: Chat agents with roles and department assignments
• agent_timeline: Agent activity timeline (incremental export)
• bans: Banned visitors (IP and visitor-based)
• chats: Chat transcripts with full conversation history (incremental export)
• departments: Chat departments for routing
• goals: Conversion goals for tracking
• roles: Agent role definitions
• routing_settings: Account-level routing configuration
• shortcuts: Canned responses for agents
• skills: Agent skills for skill-based routing
• triggers: Automated chat triggers

Rate Limits

Zendesk Chat API uses the Retry-After header for rate limit backoff. The connector handles this automatically.

Example questions

The Zendesk-Chat connector is optimized to handle prompts like these.

• Show me all chats from last week
• List all agents in the support department
• What are the most used chat shortcuts?
• Show chat volume by department
• List all banned visitors
• What triggers are currently active?
• Show agent activity timeline for today
• List all departments with their settings

Unsupported questions

The Zendesk-Chat connector isn't currently able to handle prompts like these.

• Start a new chat session
• Send a message to a visitor
• Create a new agent
• Update department settings
• Delete a shortcut

Installation

uv pip install airbyte-agent-zendesk-chat

Usage

Connectors can run in open source or hosted mode.

Open source

In open source mode, you provide API credentials directly to the connector.

from airbyte_agent_zendesk-chat import ZendeskChatConnector
from airbyte_agent_zendesk_chat.models import ZendeskChatAuthConfig

connector = ZendeskChatConnector(
    auth_config=ZendeskChatAuthConfig(
        access_token="<Your Zendesk Chat OAuth 2.0 access token>"
    )
)

@agent.tool_plain # assumes you're using Pydantic AI
@ZendeskChatConnector.describe
async def zendesk-chat_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

Hosted

In hosted mode, API credentials are stored securely in Airbyte Cloud. You provide your Airbyte credentials instead.

This example assumes you've already authenticated your connector with Airbyte. See Authentication to learn more about authenticating. If you need a step-by-step guide, see the hosted execution tutorial.

from airbyte_agent_zendesk-chat import ZendeskChatConnector

connector = ZendeskChatConnector(
    external_user_id="<your-scoped-token>",
    airbyte_client_id="<your-client-id>",
    airbyte_client_secret="<your-client-secret>"
)

@agent.tool_plain # assumes you're using Pydantic AI
@ZendeskChatConnector.describe
async def zendesk-chat_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

Full documentation

This connector supports the following entities and actions.

Entity	Actions
Accounts	Get
Agents	List, Get
Agent Timeline	List
Bans	List, Get
Chats	List, Get
Departments	List, Get
Goals	List, Get
Roles	List, Get
Routing Settings	Get
Shortcuts	List, Get
Skills	List, Get
Triggers	List

For all authentication options, see the connector's authentication documentation.

For detailed documentation on available actions and parameters, see this connector's full reference documentation.

For the service's official API docs, see the Zendesk-Chat API reference.

Version information

• Package version: 0.1.5
• Connector version: 0.1.3
• Generated with Connector SDK commit SHA: 1da193dd2b3d4b2e2147ba74bd9a4062a62c4186