Installation & Usage
[01]
Install Package
Using uv or pip
BASH
uv pip install airbyte-agent-zendesk-talk
[02]
Import
Initialize and use
PYTHON
from airbyte_agent_zendesk_talk import ZendeskTalkConnector
from airbyte_agent_zendesk_talk.models import ZendeskTalkApiTokenAuthConfig
connector = ZendeskTalkConnector(
auth_config=ZendeskTalkApiTokenAuthConfig(
email="<Your Zendesk account email address>",
api_token="<Your Zendesk API token from Admin Center>"
)
)[03]
Tool
Add tools to your agent
python
@agent.tool_plain # assumes you're using Pydantic AI
@ZendeskTalkConnector.tool_utils
async def zendesk_talk_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| Phone Numbers | [List](./REFERENCE.md#phone-numbers-list), [Get](./REFERENCE.md#phone-numbers-get), [Search](./REFERENCE.md#phone-numbers-search) |\n| Addresses | [List](./REFERENCE.md#addresses-list), [Get](./REFERENCE.md#addresses-get), [Search](./REFERENCE.md#addresses-search) |\n| Greetings | [List](./REFERENCE.md#greetings-list), [Get](./REFERENCE.md#greetings-get), [Search](./REFERENCE.md#greetings-search) |\n| Greeting Categories | [List](./REFERENCE.md#greeting-categories-list), [Get](./REFERENCE.md#greeting-categories-get), [Search](./REFERENCE.md#greeting-categories-search) |\n| Ivrs | [List](./REFERENCE.md#ivrs-list), [Get](./REFERENCE.md#ivrs-get), [Search](./REFERENCE.md#ivrs-search) |\n| Agents Activity | [List](./REFERENCE.md#agents-activity-list), [Search](./REFERENCE.md#agents-activity-search) |\n| Agents Overview | [List](./REFERENCE.md#agents-overview-list), [Search](./REFERENCE.md#agents-overview-search) |\n| Account Overview | [List](./REFERENCE.md#account-overview-list), [Search](./REFERENCE.md#account-overview-search) |\n| Current Queue Activity | [List](./REFERENCE.md#current-queue-activity-list), [Search](./REFERENCE.md#current-queue-activity-search) |\n| Calls | [List](./REFERENCE.md#calls-list), [Search](./REFERENCE.md#calls-search) |\n| Call Legs | [List](./REFERENCE.md#call-legs-list), [Search](./REFERENCE.md#call-legs-search) |
Example Prompts
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List all phone numbers in our Zendesk Talk account - Show all addresses on file - List all IVR configurations - Show all greetings - List greeting categories - Show agent activity statistics - Show the account overview stats - Show current queue activity - Which phone numbers have SMS enabled? - Find agents who have missed the most calls today - What is the average call duration across all calls? - Which phone numbers are toll-free?
Why Airbyte for AI Agents?
Built for production AI workloads with enterprise-grade reliability
Secure Authentication
Built-in OAuth 2.0 handling with automatic token refresh. No hard-coded credentials.
Agent-Native Design
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Structured, LLM-friendly schemas optimized for AI agent consumption with natural language query support.
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
CrewAI
LlamaIndex
AutoGen
OpenAI Agents SDK
Claude Agents SDK
Frequently Asked Questions
Didn't find your answer? Please don't hesitate to reach out.
The zendesk-talk connector supports API Token authentication. You provide your Zendesk account email address and an API token generated from the Zendesk Admin Center via the ZendeskTalkApiTokenAuthConfig. Alternatively, in hosted mode, credentials are stored securely in Airbyte Cloud using AirbyteAuthConfig with your Airbyte client ID and secret.
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?
The zendesk-talk connector currently supports read operations only. Write operations such as creating phone numbers, deleting IVR configurations, updating greetings, or making outbound calls are not supported. 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.
Get started in minutes with our open-source connector.