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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.

EntityActions
AccountsGet
AgentsList, Get, Context Store Search, Context Store SQL Query
Agent TimelineList
BansList, Get
ChatsList, Get, Context Store Search, Context Store SQL Query
DepartmentsList, Get, Context Store Search, Context Store SQL Query
GoalsList, Get
RolesList, Get
Routing SettingsGet
ShortcutsList, Get, Context Store Search, Context Store SQL Query
SkillsList, Get
TriggersList, Context Store Search, Context Store SQL Query

Zendesk-Chat API docs​

See the official Zendesk-Chat API reference.

Interfaces​

Use the Zendesk-Chat connector through the Airbyte Agent CLI, the Python SDK, or the API.

CLI​

Install the CLI:

curl -fsSL https://airbyte.ai/install.sh | bash

Authenticate with Airbyte:

airbyte-agent login

Create the connector. The CLI opens the hosted setup flow:

airbyte-agent connectors create --json '{
  "workspace": "<your_workspace_name>",
  "name": "zendesk-chat"
}'

Describe the connector to see its supported entities and actions:

airbyte-agent connectors describe --json '{
  "workspace": "<your_workspace_name>",
  "name": "zendesk-chat"
}'

Execute an action:

airbyte-agent connectors execute --json '{
  "workspace": "<your_workspace_name>",
  "name": "zendesk-chat",
  "entity": "accounts",
  "action": "get"
}'

Python SDK​

Installation​

uv pip install airbyte-agent-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:

The recommended pattern is build_connector_tools, which gives the agent three tools bound to this connector: inspect_connector, read_skill_docs, and execute. The agent can inspect the connector, read only the skill-doc section it needs, and then execute:

inspect_connector() -> read_skill_docs() -> read_skill_docs(section="...") -> execute(entity, action, params)

Pass section IDs verbatim as the outline lists them, prefix included (actions.<entity>.<action>, not <entity>.<action>); anything else returns an error the agent has to recover from.

The builder names its tools inspect_connector, read_skill_docs, and execute, so the tool sets for more than one connector collide when registered on the same agent. Renaming the callables at registration avoids the collision, but the generated execute guidance still names inspect_connector and read_skill_docs, pointing the model at the wrong tools. Use the agent_tool pattern below instead: it weaves your own names into that guidance.

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
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>")

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
Custom tool bodies​

When you need custom tool bodies — or a framework without native support — use ZendeskChatConnector.agent_tool. Register execute, inspect, and docs together so the agent can fetch connector guidance progressively. Pass the framework explicitly when it has a supported failure strategy:

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.agent_tool(
    framework="pydantic_ai",
    inspect_tool="zendesk_chat_inspect",
    docs_tool="zendesk_chat_read_docs",
)
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@agent.tool_plain
@ZendeskChatConnector.agent_tool(framework="pydantic_ai")
async def zendesk_chat_inspect():
    return await connector.inspect_connector()

@agent.tool_plain
@ZendeskChatConnector.agent_tool(framework="pydantic_ai")
async def zendesk_chat_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

Use the same three-function pattern with framework="langchain", "openai_agents", or "mcp" and that framework's registration decorator. Each value translates connector failures into the framework's own signal:

framework=Tool failures surface as
"pydantic_ai"pydantic_ai.ModelRetry
"langchain"langchain_core.tools.ToolException (set handle_tool_error=True to feed it back to the model)
"openai_agents"the failure message returned to the model as the tool result
"mcp"fastmcp.exceptions.ToolError
"none" (default)airbyte_agent_sdk.AirbyteToolError

On a framework the SDK does not support natively — or in a raw LLM dispatch loop — omit framework= and handle AirbyteToolError yourself:

No framework
from airbyte_agent_sdk import AirbyteToolError
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector

connector = connect("zendesk-chat", workspace_name="<your_workspace_name>")

@ZendeskChatConnector.agent_tool(
    inspect_tool="zendesk_chat_inspect",
    docs_tool="zendesk_chat_read_docs",
)
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@ZendeskChatConnector.agent_tool()
async def zendesk_chat_inspect():
    return await connector.inspect_connector()

@ZendeskChatConnector.agent_tool()
async def zendesk_chat_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

# Advertise all three to the model, using each function's docstring as its description.
handlers = {
    fn.__name__: fn
    for fn in (zendesk_chat_inspect, zendesk_chat_read_docs, zendesk_chat_execute)
}

# `tool_name` and `tool_args` come from the model's tool call in your dispatch loop.
try:
    tool_result = await handlers[tool_name](**tool_args)
except AirbyteToolError as err:
    tool_result = str(err)  # hand the message back to the model as an errored tool result

Each function's docstring carries the guidance the model needs, so pass it through as the tool description wherever you register it.

Legacy alternatives​

These examples are kept for existing integrations. The deprecated ZendeskChatConnector.tool_utils pattern loads the connector's full generated catalog into one broad execute tool description instead of letting the agent read skill docs on demand. For new code, use build_connector_tools or ZendeskChatConnector.agent_tool above.

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 {})

Or pass credentials explicitly (equivalent, useful when you're not loading them from the environment):

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
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>"
    )
)

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
Open source​

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

The recommended pattern is build_connector_tools, which gives the agent three tools bound to this connector: inspect_connector, read_skill_docs, and execute. The agent can inspect the connector, read only the skill-doc section it needs, and then execute:

inspect_connector() -> read_skill_docs() -> read_skill_docs(section="...") -> execute(entity, action, params)

Pass section IDs verbatim as the outline lists them, prefix included (actions.<entity>.<action>, not <entity>.<action>); anything else returns an error the agent has to recover from.

The builder names its tools inspect_connector, read_skill_docs, and execute, so the tool sets for more than one connector collide when registered on the same agent. Renaming the callables at registration avoids the collision, but the generated execute guidance still names inspect_connector and read_skill_docs, pointing the model at the wrong tools. Use the agent_tool pattern below instead: it weaves your own names into that guidance.

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
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>"
    ),
    subdomain="<Your Zendesk subdomain (the part before .zendesk.com in your Zendesk URL)>"
)

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
Custom tool bodies​

When you need custom tool bodies — or a framework without native support — use ZendeskChatConnector.agent_tool. Register execute, inspect, and docs together so the agent can fetch connector guidance progressively. Pass the framework explicitly when it has a supported failure strategy:

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>"
    ),
    subdomain="<Your Zendesk subdomain (the part before .zendesk.com in your Zendesk URL)>"
)

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@ZendeskChatConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="zendesk_chat_inspect",
    docs_tool="zendesk_chat_read_docs",
)
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@agent.tool_plain
@ZendeskChatConnector.agent_tool(framework="pydantic_ai")
async def zendesk_chat_inspect():
    return await connector.inspect_connector()

@agent.tool_plain
@ZendeskChatConnector.agent_tool(framework="pydantic_ai")
async def zendesk_chat_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

Use the same three-function pattern with framework="langchain", "openai_agents", or "mcp" and that framework's registration decorator. Each value translates connector failures into the framework's own signal:

framework=Tool failures surface as
"pydantic_ai"pydantic_ai.ModelRetry
"langchain"langchain_core.tools.ToolException (set handle_tool_error=True to feed it back to the model)
"openai_agents"the failure message returned to the model as the tool result
"mcp"fastmcp.exceptions.ToolError
"none" (default)airbyte_agent_sdk.AirbyteToolError

On a framework the SDK does not support natively — or in a raw LLM dispatch loop — omit framework= and handle AirbyteToolError yourself:

No framework
from airbyte_agent_sdk import AirbyteToolError
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>"
    ),
    subdomain="<Your Zendesk subdomain (the part before .zendesk.com in your Zendesk URL)>"
)

@ZendeskChatConnector.agent_tool(
    inspect_tool="zendesk_chat_inspect",
    docs_tool="zendesk_chat_read_docs",
)
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@ZendeskChatConnector.agent_tool()
async def zendesk_chat_inspect():
    return await connector.inspect_connector()

@ZendeskChatConnector.agent_tool()
async def zendesk_chat_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

# Advertise all three to the model, using each function's docstring as its description.
handlers = {
    fn.__name__: fn
    for fn in (zendesk_chat_inspect, zendesk_chat_read_docs, zendesk_chat_execute)
}

# `tool_name` and `tool_args` come from the model's tool call in your dispatch loop.
try:
    tool_result = await handlers[tool_name](**tool_args)
except AirbyteToolError as err:
    tool_result = str(err)  # hand the message back to the model as an errored tool result

Each function's docstring carries the guidance the model needs, so pass it through as the tool description wherever you register it.

Legacy alternatives​

These examples are kept for existing integrations. The deprecated ZendeskChatConnector.tool_utils pattern loads the connector's full generated catalog into one broad execute tool description instead of letting the agent read skill docs on demand. For new code, use build_connector_tools or ZendeskChatConnector.agent_tool above.

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>"
    ),
    subdomain="<Your Zendesk subdomain (the part before .zendesk.com in your Zendesk URL)>"
)

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 {})

Authentication​

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

IP allow list​

If your organization restricts access to specific IPs, add the Airbyte Agents IP addresses to your allow list.

Version information​

Connector version: 0.1.10

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