Zendesk-Chat
The Zendesk-Chat agent connector is a Python package that equips AI agents to interact with Zendesk-Chat through strongly typed, well-documented tools. It's ready to use directly in your Python app, in an agent framework, or exposed through an MCP.
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 prompts
The Zendesk-Chat connector is optimized to handle prompts like these.
- List all banned visitors
- List all departments with their settings
- 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
- What triggers are currently active?
- Show agent activity timeline for today
Unsupported prompts
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
Entities and actions
This connector supports the following entities and actions. For more details, see this connector's full reference documentation.
| Entity | Actions |
|---|---|
| Accounts | Get |
| Agents | List, Get, Context Store Search |
| Agent Timeline | List |
| Bans | List, Get |
| Chats | List, Get, Context Store Search |
| Departments | List, Get, Context Store Search |
| Goals | List, Get |
| Roles | List, Get |
| Routing Settings | Get |
| Shortcuts | List, Get, Context Store Search |
| Skills | List, Get |
| Triggers | List, Context Store Search |
Zendesk-Chat API docs
See the official Zendesk-Chat API reference.
SDK installation
uv pip install airbyte-agent-sdk
SDK usage
Connectors can run in hosted or open source mode.
Hosted
In hosted mode, API credentials are stored securely in Airbyte Agents. You provide your Airbyte credentials instead.
If your Airbyte client can access multiple organizations, also set organization_id.
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.
The connect() factory returns a fully typed ZendeskChatConnector and reads AIRBYTE_CLIENT_ID / AIRBYTE_CLIENT_SECRET from the environment:
- Pydantic AI
- LangChain
- OpenAI Agents
- FastMCP
Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
connector = connect("zendesk-chat", workspace_name="<your_workspace_name>")
agent = Agent("openai:gpt-4o")
@agent.tool_plain
@ZendeskChatConnector.tool_utils
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
return await connector.execute(entity, action, params or {})
LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
connector = connect("zendesk-chat", workspace_name="<your_workspace_name>")
@tool
@ZendeskChatConnector.tool_utils
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
"""Execute Zendesk-Chat connector operations."""
result = await connector.execute(entity, action, params or {})
# connector.execute returns a Pydantic envelope for typed actions; fall back to raw data otherwise.
return result.model_dump(mode="json") if hasattr(result, "model_dump") else result
OpenAI Agents
from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
connector = connect("zendesk-chat", workspace_name="<your_workspace_name>")
# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@ZendeskChatConnector.tool_utils(framework="openai_agents")
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
"""Execute Zendesk-Chat connector operations."""
result = await connector.execute(entity, action, params or {})
return result.model_dump(mode="json") if hasattr(result, "model_dump") else result
agent = Agent(name="Zendesk-Chat Assistant", tools=[zendesk_chat_execute])
FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
connector = connect("zendesk-chat", workspace_name="<your_workspace_name>")
mcp = FastMCP("Zendesk-Chat Agent")
@mcp.tool
@ZendeskChatConnector.tool_utils
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
"""Execute Zendesk-Chat connector operations."""
result = await connector.execute(entity, action, params or {})
return result.model_dump(mode="json") if hasattr(result, "model_dump") else result
Or pass credentials explicitly (equivalent, useful when you're not loading them from the environment):
- Pydantic AI
- LangChain
- OpenAI Agents
- FastMCP
Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = ZendeskChatConnector(
auth_config=AirbyteAuthConfig(
workspace_name="<your_workspace_name>",
organization_id="<your_organization_id>", # Optional for multi-org clients
airbyte_client_id="<your-client-id>",
airbyte_client_secret="<your-client-secret>"
)
)
agent = Agent("openai:gpt-4o")
@agent.tool_plain
@ZendeskChatConnector.tool_utils
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
return await connector.execute(entity, action, params or {})
LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = ZendeskChatConnector(
auth_config=AirbyteAuthConfig(
workspace_name="<your_workspace_name>",
organization_id="<your_organization_id>", # Optional for multi-org clients
airbyte_client_id="<your-client-id>",
airbyte_client_secret="<your-client-secret>"
)
)
@tool
@ZendeskChatConnector.tool_utils
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
"""Execute Zendesk-Chat connector operations."""
result = await connector.execute(entity, action, params or {})
# connector.execute returns a Pydantic envelope for typed actions; fall back to raw data otherwise.
return result.model_dump(mode="json") if hasattr(result, "model_dump") else result
OpenAI Agents
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = ZendeskChatConnector(
auth_config=AirbyteAuthConfig(
workspace_name="<your_workspace_name>",
organization_id="<your_organization_id>", # Optional for multi-org clients
airbyte_client_id="<your-client-id>",
airbyte_client_secret="<your-client-secret>"
)
)
# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@ZendeskChatConnector.tool_utils(framework="openai_agents")
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
"""Execute Zendesk-Chat connector operations."""
result = await connector.execute(entity, action, params or {})
return result.model_dump(mode="json") if hasattr(result, "model_dump") else result
agent = Agent(name="Zendesk-Chat Assistant", tools=[zendesk_chat_execute])
FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = ZendeskChatConnector(
auth_config=AirbyteAuthConfig(
workspace_name="<your_workspace_name>",
organization_id="<your_organization_id>", # Optional for multi-org clients
airbyte_client_id="<your-client-id>",
airbyte_client_secret="<your-client-secret>"
)
)
mcp = FastMCP("Zendesk-Chat Agent")
@mcp.tool
@ZendeskChatConnector.tool_utils
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
"""Execute Zendesk-Chat connector operations."""
result = await connector.execute(entity, action, params or {})
return result.model_dump(mode="json") if hasattr(result, "model_dump") else result
Open source
In open source mode, you provide API credentials directly to the connector.
- Pydantic AI
- LangChain
- OpenAI Agents
- FastMCP
Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
from airbyte_agent_sdk.connectors.zendesk_chat.models import ZendeskChatAuthConfig
connector = ZendeskChatConnector(
auth_config=ZendeskChatAuthConfig(
access_token="<Your Zendesk Chat OAuth 2.0 access token>"
)
)
agent = Agent("openai:gpt-4o")
@agent.tool_plain
@ZendeskChatConnector.tool_utils
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
return await connector.execute(entity, action, params or {})
LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
from airbyte_agent_sdk.connectors.zendesk_chat.models import ZendeskChatAuthConfig
connector = ZendeskChatConnector(
auth_config=ZendeskChatAuthConfig(
access_token="<Your Zendesk Chat OAuth 2.0 access token>"
)
)
@tool
@ZendeskChatConnector.tool_utils
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
"""Execute Zendesk-Chat connector operations."""
result = await connector.execute(entity, action, params or {})
# connector.execute returns a Pydantic envelope for typed actions; fall back to raw data otherwise.
return result.model_dump(mode="json") if hasattr(result, "model_dump") else result
OpenAI Agents
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
from airbyte_agent_sdk.connectors.zendesk_chat.models import ZendeskChatAuthConfig
connector = ZendeskChatConnector(
auth_config=ZendeskChatAuthConfig(
access_token="<Your Zendesk Chat OAuth 2.0 access token>"
)
)
# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@ZendeskChatConnector.tool_utils(framework="openai_agents")
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
"""Execute Zendesk-Chat connector operations."""
result = await connector.execute(entity, action, params or {})
return result.model_dump(mode="json") if hasattr(result, "model_dump") else result
agent = Agent(name="Zendesk-Chat Assistant", tools=[zendesk_chat_execute])
FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
from airbyte_agent_sdk.connectors.zendesk_chat.models import ZendeskChatAuthConfig
connector = ZendeskChatConnector(
auth_config=ZendeskChatAuthConfig(
access_token="<Your Zendesk Chat OAuth 2.0 access token>"
)
)
mcp = FastMCP("Zendesk-Chat Agent")
@mcp.tool
@ZendeskChatConnector.tool_utils
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
"""Execute Zendesk-Chat connector operations."""
result = await connector.execute(entity, action, params or {})
return result.model_dump(mode="json") if hasattr(result, "model_dump") else result
Authentication
For all authentication options, see the connector's authentication documentation.
Version information
Connector version: 0.1.10