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Greenhouse

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

Greenhouse is an applicant tracking system (ATS) that helps companies manage their hiring process via the Harvest v3 API with OAuth 2.0 authentication. This connector provides access to candidates, applications, jobs, offers, users, departments, offices, job posts, sources, and interviews for recruiting analytics and talent acquisition insights.

Example prompts​

The Greenhouse connector is optimized to handle prompts like these.

  • List all open jobs
  • Show me recent interviews
  • Show me recent job offers
  • List recent applications
  • Show me candidates from {company} who applied last month
  • What are the top 5 sources for our job applications this quarter?
  • Analyze the interview schedules for our engineering candidates this week
  • Compare the number of applications across different offices
  • Identify candidates who have multiple applications in our system
  • Summarize the candidate pipeline for our latest job posting
  • Find the most active departments in recruiting this month

Unsupported prompts​

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

  • Create a new job posting for the marketing team
  • Schedule an interview for {candidate}
  • Update the status of {candidate}'s application
  • Delete a candidate profile
  • Send an offer letter to {candidate}
  • Edit the details of a job description

Entities and actions​

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

EntityActions
ApplicationsList, Context Store Search, Context Store SQL Query
CandidatesList, Context Store Search, Context Store SQL Query
DepartmentsList, Context Store Search, Context Store SQL Query
InterviewsList
Job PostsList, Context Store Search, Context Store SQL Query, Semantic Search
JobsList, Context Store Search, Context Store SQL Query, Semantic Search
OffersList, Context Store Search, Context Store SQL Query
OfficesList, Context Store Search, Context Store SQL Query
SourcesList, Context Store Search, Context Store SQL Query
UsersList, Context Store Search, Context Store SQL Query
AttachmentsList, Download

Greenhouse API docs​

See the official Greenhouse API reference.

Interfaces​

Use the Greenhouse 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": "greenhouse"
}'

Describe the connector to see its supported entities and actions:

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

Execute an action:

airbyte-agent connectors execute --json '{
  "workspace": "<your_workspace_name>",
  "name": "greenhouse",
  "entity": "applications",
  "action": "list"
}'

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 GreenhouseConnector 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.greenhouse import GreenhouseConnector

connector = connect("greenhouse", 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 GreenhouseConnector.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.greenhouse import GreenhouseConnector

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

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

@agent.tool_plain
@GreenhouseConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="greenhouse_inspect",
    docs_tool="greenhouse_read_docs",
)
async def greenhouse_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

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

@agent.tool_plain
@GreenhouseConnector.agent_tool(framework="pydantic_ai")
async def greenhouse_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.greenhouse import GreenhouseConnector

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

@GreenhouseConnector.agent_tool(
    inspect_tool="greenhouse_inspect",
    docs_tool="greenhouse_read_docs",
)
async def greenhouse_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@GreenhouseConnector.agent_tool()
async def greenhouse_inspect():
    return await connector.inspect_connector()

@GreenhouseConnector.agent_tool()
async def greenhouse_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 (greenhouse_inspect, greenhouse_read_docs, greenhouse_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 GreenhouseConnector.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 GreenhouseConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.greenhouse import GreenhouseConnector

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

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

@agent.tool_plain
@GreenhouseConnector.tool_utils
async def greenhouse_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.greenhouse import GreenhouseConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = GreenhouseConnector(
    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.greenhouse import GreenhouseConnector
from airbyte_agent_sdk.connectors.greenhouse.models import GreenhouseAuthConfig

connector = GreenhouseConnector(
    auth_config=GreenhouseAuthConfig(
        client_id="<Client ID from the Greenhouse OAuth application>",
        client_secret="<Client secret from the Greenhouse OAuth application>",
        refresh_token="<Refresh token generated through the Greenhouse OAuth consent flow>",
        access_token="<Access token generated through the Greenhouse OAuth consent flow (optional if refresh_token is provided)>"
    )
)

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 GreenhouseConnector.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.greenhouse import GreenhouseConnector
from airbyte_agent_sdk.connectors.greenhouse.models import GreenhouseAuthConfig

connector = GreenhouseConnector(
    auth_config=GreenhouseAuthConfig(
        client_id="<Client ID from the Greenhouse OAuth application>",
        client_secret="<Client secret from the Greenhouse OAuth application>",
        refresh_token="<Refresh token generated through the Greenhouse OAuth consent flow>",
        access_token="<Access token generated through the Greenhouse OAuth consent flow (optional if refresh_token is provided)>"
    )
)

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

@agent.tool_plain
@GreenhouseConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="greenhouse_inspect",
    docs_tool="greenhouse_read_docs",
)
async def greenhouse_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

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

@agent.tool_plain
@GreenhouseConnector.agent_tool(framework="pydantic_ai")
async def greenhouse_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.greenhouse import GreenhouseConnector
from airbyte_agent_sdk.connectors.greenhouse.models import GreenhouseAuthConfig

connector = GreenhouseConnector(
    auth_config=GreenhouseAuthConfig(
        client_id="<Client ID from the Greenhouse OAuth application>",
        client_secret="<Client secret from the Greenhouse OAuth application>",
        refresh_token="<Refresh token generated through the Greenhouse OAuth consent flow>",
        access_token="<Access token generated through the Greenhouse OAuth consent flow (optional if refresh_token is provided)>"
    )
)

@GreenhouseConnector.agent_tool(
    inspect_tool="greenhouse_inspect",
    docs_tool="greenhouse_read_docs",
)
async def greenhouse_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@GreenhouseConnector.agent_tool()
async def greenhouse_inspect():
    return await connector.inspect_connector()

@GreenhouseConnector.agent_tool()
async def greenhouse_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 (greenhouse_inspect, greenhouse_read_docs, greenhouse_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 GreenhouseConnector.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 GreenhouseConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.greenhouse import GreenhouseConnector
from airbyte_agent_sdk.connectors.greenhouse.models import GreenhouseAuthConfig

connector = GreenhouseConnector(
    auth_config=GreenhouseAuthConfig(
        client_id="<Client ID from the Greenhouse OAuth application>",
        client_secret="<Client secret from the Greenhouse OAuth application>",
        refresh_token="<Refresh token generated through the Greenhouse OAuth consent flow>",
        access_token="<Access token generated through the Greenhouse OAuth consent flow (optional if refresh_token is provided)>"
    )
)

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

@agent.tool_plain
@GreenhouseConnector.tool_utils
async def greenhouse_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: 0.2.0

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