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, and custom objects for customer relationship management and sales analytics.

ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

Difference between ETL and ELT?

ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.

Agent Connector

Connect your agents to Hubspot

Production-ready access to your Hubspot data with managed auth, parametric control, and the reliability your agents need to actually work.

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Installation & Usage

Get start with the Hubspot connector in minutes.

[01]

Install Package

Using uv or pip

BASH

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uv pip install airbyte-agent-hubspot

[02]

Import

Initialize and use

PYTHON

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from airbyte_agent_hubspot import HubspotConnector
from airbyte_agent_hubspot.models import HubspotPrivateAppAuthConfig

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

[03]

Tool

Add tools to your agent

python

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@agent.tool_plain # assumes you're using Pydantic AI
@HubspotConnector.tool_utils
async def hubspot_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

Supported Entities & Actions

Access all your Hubspot data through a unified API

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| Entity | Actions |\n|--------|---------|\n| Contacts | [List](./REFERENCE.md#contacts-list), [Get](./REFERENCE.md#contacts-get), [API Search](./REFERENCE.md#contacts-api_search), [Search](./REFERENCE.md#contacts-search) |\n| Companies | [List](./REFERENCE.md#companies-list), [Get](./REFERENCE.md#companies-get), [API Search](./REFERENCE.md#companies-api_search), [Search](./REFERENCE.md#companies-search) |\n| Deals | [List](./REFERENCE.md#deals-list), [Get](./REFERENCE.md#deals-get), [API Search](./REFERENCE.md#deals-api_search), [Search](./REFERENCE.md#deals-search) |\n| Tickets | [List](./REFERENCE.md#tickets-list), [Get](./REFERENCE.md#tickets-get), [API Search](./REFERENCE.md#tickets-api_search) |\n| Schemas | [List](./REFERENCE.md#schemas-list), [Get](./REFERENCE.md#schemas-get) |\n| Objects | [List](./REFERENCE.md#objects-list), [Get](./REFERENCE.md#objects-get) |

View full reference documentation

Example Prompts

The Hubspot connector is optimized to handle prompts like these

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List recent deals - List recent tickets - List companies in my CRM - List contacts in my CRM - Show me all deals from \{company\} this quarter - What are the top 5 most valuable deals in my pipeline right now? - Search for contacts in the marketing department at \{company\} - 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

View full reference documentation

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.

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Works with your favorite frameworks

Use the Salesforce connector with any AI agent framework.

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.

How do I authenticate with Hubspot?

The HubSpot connector supports Private App token authentication in open source mode, where you provide your HubSpot Private App access token directly. In hosted mode, credentials are stored securely in Airbyte Cloud using Airbyte client credentials (client ID and client 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?

Currently, the HubSpot connector focuses on read operations only. Write operations such as creating contacts, updating records, deleting tickets, scheduling tasks, or sending emails are not supported at this time but 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.

Ready to connect your AI agents to Hubspot?

Get started in minutes with our open-source connector.

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READ THE DOCS