Give agents tools for secure, real-time access to fetch, search, write, and sync across every system, with replication, entity mapping, and auth built-in.
About this 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.
CRM
Sales Analytics
Customer Data
Version Information
Package version
0.1.25
Connector version
0.1.6
SDK commit
5b20f488dec0e8f29410823753106603c23a4b65
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Installation & Usage
1
Install Package
Using uv or pip
bash
uv pip install airbyte-agent-zendesk-chat
2
Import
Initialize and use
python
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>"
)
)3
Tool
Add tools to your agent
python
@agent.tool_plain # assumes you're using Pydantic AI
@ZendeskChatConnector.tool_utils
async def zendesk-chat_execute(entity: str, action: str, params: dict | None = None):
return await connector.execute(entity, action, params or {})Supported Entities & Actions
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| Entity | Actions |\n|--------|---------|\n| Accounts | [Get](./REFERENCE.md#accounts-get) |\n| Agents | [List](./REFERENCE.md#agents-list), [Get](./REFERENCE.md#agents-get) |\n| Agent Timeline | [List](./REFERENCE.md#agent-timeline-list) |\n| Bans | [List](./REFERENCE.md#bans-list), [Get](./REFERENCE.md#bans-get) |\n| Chats | [List](./REFERENCE.md#chats-list), [Get](./REFERENCE.md#chats-get) |\n| Departments | [List](./REFERENCE.md#departments-list), [Get](./REFERENCE.md#departments-get) |\n| Goals | [List](./REFERENCE.md#goals-list), [Get](./REFERENCE.md#goals-get) |\n| Roles | [List](./REFERENCE.md#roles-list), [Get](./REFERENCE.md#roles-get) |\n| Routing Settings | [Get](./REFERENCE.md#routing-settings-get) |\n| Shortcuts | [List](./REFERENCE.md#shortcuts-list), [Get](./REFERENCE.md#shortcuts-get) |\n| Skills | [List](./REFERENCE.md#skills-list), [Get](./REFERENCE.md#skills-get) |\n| Triggers | [List](./REFERENCE.md#triggers-list) |
Example Prompts
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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
Why Airbyte for AI Agents?
Built for production AI workloads with enterprise-grade reliability
Agent-Native Design
Structured, LLM-friendly schemas optimized for AI agent consumption with natural language query support.
Secure Authentication
Built-in OAuth 2.0 handling with automatic token refresh. No hard-coded credentials.
Production Ready
Battle-tested connectors with comprehensive error handling, logging, and retry logic.
Open Source
Fully open source under the MIT license. Contribute, customize, and extend freely.
Works with your favorite frameworks
🦜
LangChain
🦙
LlamaIndex
🤖
CrewAI
⚡
AutoGen
🧠
OpenAI Agents SDK
🔮
Claude Agents SDK
Frequently Asked Questions
Didn't find your answer? Please don't hesitate to reach out.
The zendesk-chat connector supports OAuth 2.0 authentication via access token. You provide your Zendesk Chat OAuth 2.0 access token in the auth_config when initializing the connector.
Can I use this connector with any AI agent framework?
The connector is compatible with any Python-based AI agent framework including LangChain, LlamaIndex, CrewAI, Pydantic AI, and custom implementations.
Does this connector support write operations?
No, the zendesk-chat connector currently focuses on read operations only. It cannot start chat sessions, send messages, create agents, update department settings, or delete shortcuts. Write support may be added in future versions.
How is this different from the Airbyte data connector?
Agent connectors are specifically designed for AI agents and LLM applications. They provide natural language interfaces, optimized response formats, and seamless integration with agent frameworks, unlike traditional ETL-focused connectors.
Will there be a platform for agent connectors?
The hosted version with secure credential storage through Airbyte Cloud is already available. See the hosted usage section in the documentation for setup instructions.
