Ashby

This connector is optimized for AI agents. For the data replication connector, see Ashby.

The Ashby agent connector is a Python package that equips AI agents to interact with Ashby 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.

Ashby is a modern applicant tracking system (ATS) and recruiting platform that helps companies manage their hiring process. This connector provides access to candidates, applications, jobs, departments, locations, users, job postings, sources, archive reasons, candidate tags, custom fields, and feedback form definitions for talent acquisition analytics and hiring insights.

Example prompts

The Ashby connector is optimized to handle prompts like these.

List all open jobs
Show me all candidates
List recent applications
List all departments
Show me all job postings
List all users in the organization
Show me candidates who applied last month
What are the top sources for job applications?
Compare the number of applications across different departments
Find candidates with multiple applications
Summarize the candidate pipeline for our latest job posting
Find the most active departments in recruiting this month

Unsupported prompts

The Ashby connector isn't currently able to handle prompts like these.

Create a new job posting
Schedule an interview for a candidate
Update a candidates application status
Delete a candidate profile
Send an offer letter to a candidate

Entities and actions

This connector supports the following entities and actions. For more details, see this connector's full reference documentation.

Entity	Actions
Candidates	List, Get, Context Store Search
Applications	List, Get, Context Store Search
Jobs	List, Get, Context Store Search
Departments	List, Get
Locations	List, Get
Users	List, Get, Context Store Search
Job Postings	List, Get, Context Store Search
Sources	List
Archive Reasons	List
Candidate Tags	List
Custom Fields	List
Feedback Form Definitions	List

Ashby API docs

See the official Ashby 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 AshbyConnector 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.ashby import AshbyConnector

connector = connect("ashby", workspace_name="<your_workspace_name>")

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

@agent.tool_plain
@AshbyConnector.tool_utils
async def ashby_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.ashby import AshbyConnector

connector = connect("ashby", workspace_name="<your_workspace_name>")

@tool
@AshbyConnector.tool_utils
async def ashby_execute(entity: str, action: str, params: dict | None = None):
    """Execute Ashby 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.ashby import AshbyConnector

connector = connect("ashby", 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)
@AshbyConnector.tool_utils(framework="openai_agents")
async def ashby_execute(entity: str, action: str, params: dict | None = None):
    """Execute Ashby 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="Ashby Assistant", tools=[ashby_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.ashby import AshbyConnector

connector = connect("ashby", workspace_name="<your_workspace_name>")

mcp = FastMCP("Ashby Agent")

@mcp.tool
@AshbyConnector.tool_utils
async def ashby_execute(entity: str, action: str, params: dict | None = None):
    """Execute Ashby 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.ashby import AshbyConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = AshbyConnector(
    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
@AshbyConnector.tool_utils
async def ashby_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.ashby import AshbyConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = AshbyConnector(
    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
@AshbyConnector.tool_utils
async def ashby_execute(entity: str, action: str, params: dict | None = None):
    """Execute Ashby 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.ashby import AshbyConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = AshbyConnector(
    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)
@AshbyConnector.tool_utils(framework="openai_agents")
async def ashby_execute(entity: str, action: str, params: dict | None = None):
    """Execute Ashby 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="Ashby Assistant", tools=[ashby_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.ashby import AshbyConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = AshbyConnector(
    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("Ashby Agent")

@mcp.tool
@AshbyConnector.tool_utils
async def ashby_execute(entity: str, action: str, params: dict | None = None):
    """Execute Ashby 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.ashby import AshbyConnector
from airbyte_agent_sdk.connectors.ashby.models import AshbyAuthConfig

connector = AshbyConnector(
    auth_config=AshbyAuthConfig(
        api_key="<Your Ashby API key>"
    )
)

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

@agent.tool_plain
@AshbyConnector.tool_utils
async def ashby_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.ashby import AshbyConnector
from airbyte_agent_sdk.connectors.ashby.models import AshbyAuthConfig

connector = AshbyConnector(
    auth_config=AshbyAuthConfig(
        api_key="<Your Ashby API key>"
    )
)

@tool
@AshbyConnector.tool_utils
async def ashby_execute(entity: str, action: str, params: dict | None = None):
    """Execute Ashby 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.ashby import AshbyConnector
from airbyte_agent_sdk.connectors.ashby.models import AshbyAuthConfig

connector = AshbyConnector(
    auth_config=AshbyAuthConfig(
        api_key="<Your Ashby API key>"
    )
)

# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@AshbyConnector.tool_utils(framework="openai_agents")
async def ashby_execute(entity: str, action: str, params: dict | None = None):
    """Execute Ashby 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="Ashby Assistant", tools=[ashby_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.ashby import AshbyConnector
from airbyte_agent_sdk.connectors.ashby.models import AshbyAuthConfig

connector = AshbyConnector(
    auth_config=AshbyAuthConfig(
        api_key="<Your Ashby API key>"
    )
)

mcp = FastMCP("Ashby Agent")

@mcp.tool
@AshbyConnector.tool_utils
async def ashby_execute(entity: str, action: str, params: dict | None = None):
    """Execute Ashby 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: 0.1.4

Example prompts​

Unsupported prompts​

Entities and actions​

Ashby API docs​

SDK installation​

SDK usage​

Hosted​

Open source​

Authentication​

Version information​