Freshdesk
The Freshdesk agent connector is a Python package that equips AI agents to interact with Freshdesk 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.
Connector for the Freshdesk customer support platform API (v2). Provides read access to helpdesk data including tickets, contacts, agents, groups, companies, roles, satisfaction ratings, surveys, time entries, and ticket fields. Freshdesk is a cloud-based customer support solution that enables companies to manage customer conversations across email, phone, chat, and social media.
Example prompts
The Freshdesk connector is optimized to handle prompts like these.
- List all open tickets in Freshdesk
- Show me all agents in the support team
- List all groups configured in Freshdesk
- Get the details of ticket #26
- Show me all companies in Freshdesk
- List all roles defined in the helpdesk
- Show me the ticket fields and their options
- List time entries for tickets
- What are the high priority tickets from last week?
- Which tickets have breached their SLA due date?
- Show me tickets assigned to agent {agent_name}
- Find all tickets from company {company_name}
- How many tickets were created this month by status?
- What are the satisfaction ratings for resolved tickets?
Unsupported prompts
The Freshdesk connector isn't currently able to handle prompts like these.
- Create a new ticket in Freshdesk
- Update the status of ticket #{ticket_id}
- Delete a contact from Freshdesk
- Assign a ticket to a different agent
Entities and actions
This connector supports the following entities and actions. For more details, see this connector's full reference documentation.
| Entity | Actions |
|---|---|
| Tickets | List, Get, Context Store Search |
| Contacts | List, Get, Context Store Search |
| Agents | List, Get, Context Store Search |
| Groups | List, Get, Context Store Search |
| Companies | List, Get, Context Store Search |
| Roles | List, Get, Context Store Search |
| Satisfaction Ratings | List, Context Store Search |
| Surveys | List, Context Store Search |
| Time Entries | List, Context Store Search |
| Ticket Fields | List, Context Store Search |
Freshdesk API docs
See the official Freshdesk 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 FreshdeskConnector 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.freshdesk import FreshdeskConnector
connector = connect("freshdesk", workspace_name="<your_workspace_name>")
agent = Agent("openai:gpt-4o")
@agent.tool_plain
@FreshdeskConnector.tool_utils
async def freshdesk_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.freshdesk import FreshdeskConnector
connector = connect("freshdesk", workspace_name="<your_workspace_name>")
@tool
@FreshdeskConnector.tool_utils
async def freshdesk_execute(entity: str, action: str, params: dict | None = None):
"""Execute Freshdesk 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.freshdesk import FreshdeskConnector
connector = connect("freshdesk", 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)
@FreshdeskConnector.tool_utils(framework="openai_agents")
async def freshdesk_execute(entity: str, action: str, params: dict | None = None):
"""Execute Freshdesk 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="Freshdesk Assistant", tools=[freshdesk_execute])
FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.freshdesk import FreshdeskConnector
connector = connect("freshdesk", workspace_name="<your_workspace_name>")
mcp = FastMCP("Freshdesk Agent")
@mcp.tool
@FreshdeskConnector.tool_utils
async def freshdesk_execute(entity: str, action: str, params: dict | None = None):
"""Execute Freshdesk 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.freshdesk import FreshdeskConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = FreshdeskConnector(
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
@FreshdeskConnector.tool_utils
async def freshdesk_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.freshdesk import FreshdeskConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = FreshdeskConnector(
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
@FreshdeskConnector.tool_utils
async def freshdesk_execute(entity: str, action: str, params: dict | None = None):
"""Execute Freshdesk 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.freshdesk import FreshdeskConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = FreshdeskConnector(
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)
@FreshdeskConnector.tool_utils(framework="openai_agents")
async def freshdesk_execute(entity: str, action: str, params: dict | None = None):
"""Execute Freshdesk 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="Freshdesk Assistant", tools=[freshdesk_execute])
FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.freshdesk import FreshdeskConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = FreshdeskConnector(
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("Freshdesk Agent")
@mcp.tool
@FreshdeskConnector.tool_utils
async def freshdesk_execute(entity: str, action: str, params: dict | None = None):
"""Execute Freshdesk 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.freshdesk import FreshdeskConnector
from airbyte_agent_sdk.connectors.freshdesk.models import FreshdeskAuthConfig
connector = FreshdeskConnector(
auth_config=FreshdeskAuthConfig(
api_key="<Your Freshdesk API key (found in Profile Settings)>"
)
)
agent = Agent("openai:gpt-4o")
@agent.tool_plain
@FreshdeskConnector.tool_utils
async def freshdesk_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.freshdesk import FreshdeskConnector
from airbyte_agent_sdk.connectors.freshdesk.models import FreshdeskAuthConfig
connector = FreshdeskConnector(
auth_config=FreshdeskAuthConfig(
api_key="<Your Freshdesk API key (found in Profile Settings)>"
)
)
@tool
@FreshdeskConnector.tool_utils
async def freshdesk_execute(entity: str, action: str, params: dict | None = None):
"""Execute Freshdesk 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.freshdesk import FreshdeskConnector
from airbyte_agent_sdk.connectors.freshdesk.models import FreshdeskAuthConfig
connector = FreshdeskConnector(
auth_config=FreshdeskAuthConfig(
api_key="<Your Freshdesk API key (found in Profile Settings)>"
)
)
# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@FreshdeskConnector.tool_utils(framework="openai_agents")
async def freshdesk_execute(entity: str, action: str, params: dict | None = None):
"""Execute Freshdesk 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="Freshdesk Assistant", tools=[freshdesk_execute])
FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.freshdesk import FreshdeskConnector
from airbyte_agent_sdk.connectors.freshdesk.models import FreshdeskAuthConfig
connector = FreshdeskConnector(
auth_config=FreshdeskAuthConfig(
api_key="<Your Freshdesk API key (found in Profile Settings)>"
)
)
mcp = FastMCP("Freshdesk Agent")
@mcp.tool
@FreshdeskConnector.tool_utils
async def freshdesk_execute(entity: str, action: str, params: dict | None = None):
"""Execute Freshdesk 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: 1.0.3