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Hubspot

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

HubSpot is a CRM platform that provides tools for marketing, sales, customer service, and content management. This connector provides access to contacts, companies, deals, tickets, notes, calls, emails, meetings, tasks, and custom objects for customer relationship management and sales analytics.

Example prompts​

The Hubspot connector is optimized to handle prompts like these.

  • List recent deals
  • List recent tickets
  • List companies in my CRM
  • List contacts in my CRM
  • Create a new contact with email john@example.com and name John Smith
  • Create a new deal called 'Enterprise License' with amount 50000
  • Update the deal stage to 'closedwon' for a specific deal
  • Create a new company called 'Acme Corp' with domain acme.com
  • Create a support ticket with subject 'Login issue' and priority HIGH
  • Update the contact email for a specific contact
  • Associate contact 123 with deal 456
  • Link a contact to a company in HubSpot
  • Set contact 123 as the Primary contact for company 456
  • List all associations for contact 123 to companies
  • Remove an association between a contact and a deal
  • Add a note to contact 12345 saying 'Discussed pricing options'
  • List recent notes in my CRM
  • Get the details of a specific note
  • Delete a note from HubSpot
  • Log a call with contact 12345 about pricing discussion
  • List recent calls in my CRM
  • Create an email record for outreach to a contact
  • List recent emails in my CRM
  • Schedule a meeting with a contact for next Tuesday
  • List recent meetings in my CRM
  • Create a follow-up task for a deal
  • List tasks in my CRM
  • Show me all deals from Acme Corp this quarter
  • What are the top 5 most valuable deals in my pipeline right now?
  • Search for contacts in the marketing department at HubSpot
  • Give me an overview of my sales team's deals in the last 30 days
  • Identify the most active companies in our CRM this month
  • Compare the number of deals closed by different sales representatives
  • Find all tickets related to a specific product issue and summarize their status

Unsupported prompts​

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

  • Delete a contact from HubSpot
  • Delete a deal record

Entities and actions​

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

EntityActions
ContactsList, Create, Get, Update, Search, Context Store Search, Context Store SQL Query
CompaniesList, Create, Get, Update, Search, Context Store Search, Context Store SQL Query
DealsList, Create, Get, Update, Search, Context Store Search, Context Store SQL Query
TicketsList, Create, Get, Update, Search, Context Store Search, Context Store SQL Query, Semantic Search
NotesList, Create, Get, Update, Delete, Context Store Search, Context Store SQL Query, Semantic Search
CallsList, Create, Get, Update, Delete, Context Store Search, Context Store SQL Query, Semantic Search
EmailsList, Create, Get, Update, Delete, Context Store Search, Context Store SQL Query, Semantic Search
MeetingsList, Create, Get, Update, Delete, Context Store Search, Context Store SQL Query, Semantic Search
TasksList, Create, Get, Update, Delete, Context Store Search, Context Store SQL Query, Semantic Search
SchemasList, Get
ObjectsList, Get
AssociationsList, Create, Delete

Hubspot API docs​

See the official Hubspot API reference.

Interfaces​

Use the Hubspot 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": "hubspot"
}'

Describe the connector to see its supported entities and actions:

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

Execute an action:

airbyte-agent connectors execute --json '{
  "workspace": "<your_workspace_name>",
  "name": "hubspot",
  "entity": "contacts",
  "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 HubspotConnector 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.hubspot import HubspotConnector

connector = connect("hubspot", 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 HubspotConnector.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.hubspot import HubspotConnector

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

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

@agent.tool_plain
@HubspotConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="hubspot_inspect",
    docs_tool="hubspot_read_docs",
)
async def hubspot_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

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

@agent.tool_plain
@HubspotConnector.agent_tool(framework="pydantic_ai")
async def hubspot_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.hubspot import HubspotConnector

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

@HubspotConnector.agent_tool(
    inspect_tool="hubspot_inspect",
    docs_tool="hubspot_read_docs",
)
async def hubspot_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@HubspotConnector.agent_tool()
async def hubspot_inspect():
    return await connector.inspect_connector()

@HubspotConnector.agent_tool()
async def hubspot_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 (hubspot_inspect, hubspot_read_docs, hubspot_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 HubspotConnector.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 HubspotConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.hubspot import HubspotConnector

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

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

@agent.tool_plain
@HubspotConnector.tool_utils
async def hubspot_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.hubspot import HubspotConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = HubspotConnector(
    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.hubspot import HubspotConnector
from airbyte_agent_sdk.connectors.hubspot.models import HubspotPrivateAppAuthConfig

connector = HubspotConnector(
    auth_config=HubspotPrivateAppAuthConfig(
        private_app_token="<Access token from a HubSpot Private App>"
    )
)

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 HubspotConnector.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.hubspot import HubspotConnector
from airbyte_agent_sdk.connectors.hubspot.models import HubspotPrivateAppAuthConfig

connector = HubspotConnector(
    auth_config=HubspotPrivateAppAuthConfig(
        private_app_token="<Access token from a HubSpot Private App>"
    )
)

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

@agent.tool_plain
@HubspotConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="hubspot_inspect",
    docs_tool="hubspot_read_docs",
)
async def hubspot_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

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

@agent.tool_plain
@HubspotConnector.agent_tool(framework="pydantic_ai")
async def hubspot_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.hubspot import HubspotConnector
from airbyte_agent_sdk.connectors.hubspot.models import HubspotPrivateAppAuthConfig

connector = HubspotConnector(
    auth_config=HubspotPrivateAppAuthConfig(
        private_app_token="<Access token from a HubSpot Private App>"
    )
)

@HubspotConnector.agent_tool(
    inspect_tool="hubspot_inspect",
    docs_tool="hubspot_read_docs",
)
async def hubspot_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@HubspotConnector.agent_tool()
async def hubspot_inspect():
    return await connector.inspect_connector()

@HubspotConnector.agent_tool()
async def hubspot_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 (hubspot_inspect, hubspot_read_docs, hubspot_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 HubspotConnector.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 HubspotConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.hubspot import HubspotConnector
from airbyte_agent_sdk.connectors.hubspot.models import HubspotPrivateAppAuthConfig

connector = HubspotConnector(
    auth_config=HubspotPrivateAppAuthConfig(
        private_app_token="<Access token from a HubSpot Private App>"
    )
)

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

@agent.tool_plain
@HubspotConnector.tool_utils
async def hubspot_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: 0.1.20

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