# HubSpot-LinkedIn Agent

## Agent Details

Automate customer relationship management, internal company management, etc. with the [HubSpot-LinkedIn agent](https://github.com/Lilypad-Tech/HubSpot-LinkedIn-AgentSync)! Scale inference using the Lilypad network deploying any of the models supported by the [Lilypad Inference API](https://docs.lilypad.tech/lilypad/developer-resources/inference-api).

This workflow serves as a proof of concept (PoC) illustrating the capabilities of agents to interface with platform APIs, specifically those of HubSpot and RapidAPI. 

Github repo [here](https://github.com/Lilypad-Tech/HubSpot-LinkedIn-AgentSync).

## Getting Started

A guide to launch the HubSpot-LinkedIn agent locally and run inference on the Lilypad Network with the [Lilypad Inference API](https://github.com/Lilypad-Tech/HubSpot-LinkedIn-AgentSync).

### Prerequisites

* Python 3.8+
* A HubSpot account with a Private App token
  * [Sign up for HubSpot](https://www.hubspot.com/)
  * [Create a Private App](https://developers.hubspot.com/docs/guides/apps/private-apps/overview)\
    \&#xNAN;*Steps*: Log in > Settings > Integrations > Private Apps > Create private app > Name it > Scopes: `crm.objects.contacts` (read/write contacts) > Create app > Click app name > Auth tab > View access token > Copy token
* A RapidAPI account with a LinkedIn Data API key
  * [Sign up for RapidAPI](https://rapidapi.com/signup)\
    \&#xNAN;*Steps*: Create an account > Subscribe to [LinkedIn Data API](https://rapidapi.com/rockapis-rockapis-default/api/linkedin-data-api) > Go to [Developer Dashboard > Authorization](https://rapidapi.com/developer/authorization) > Select "default-application" > Copy "Application Key"
* An OpenAI-compatible API
  * [Lilypad Inference API](https://example.com/anura-api-docs) (Sign up and generate an API key)'
  * Choose the model you would like to use

### Setup

1. **Clone the Repository**

   ```
   git clone https://github.com/mavericb/HubSpot-LinkedIn-AgentSync.git
   cd HubSpot-LinkedIn-AgentSync
   ```
2. **Install Dependencies**

   ```
   pip install -r requirements.txt
   ```

   *Note*: If `requirements.txt` is missing, install:

   ```
   pip install python-dotenv hubspot-api-client requests schedule phi
   ```
3. **Configure Environment Variables**
   * Create a `.env` file:

     ```
     touch .env
     ```
   * Add your keys:

     ```
     HUBSPOT_ACCESS_TOKEN=your-private-app-token
     RAPIDAPI_KEY=your-default-app-token
     API_KEY=your-openai-compatible-key
     BASE_URL=https://anura-testnet.lilypad.tech/api/v1  # Adjust for your provider
     AGENT_MODEL=qwen2.5:7b  # Adjust for your provider
     ```

### Usage

1. **Start the Daemon**

   ```
   python main.py
   ```

   * Checks HubSpot every minute for contacts modified in the last 7 days (or since `last_processed.json`).
   * Fetches LinkedIn data and updates HubSpot.
   * Runs for 5 minutes (adjustable in `main.py`).
2. **Monitor Logs**
   * Look for "Found X contacts", "HubSpot update: ...", "Attempting to save last\_processed timestamp".
   * If no contacts are found, `last_processed.json` won’t save—edit a HubSpot contact to test.

### File Structure

* `main.py`: Entry point and daemon logic.
* `/tools/hubspot_tools.py`: HubSpot API utilities (fetch/update contacts).
* `/tools/linkedin_tools.py`: LinkedIn API utility (fetch profile data).
* `/agents/hubspot_agent.py`: Agent for updating HubSpot.
* `/agents/linkedin_agent.py`: Agent for fetching LinkedIn data.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.lilypad.tech/lilypad/use-cases-agents-and-projects/agents/hubspot-linkedin-agent.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.