Pre-built Templates

Your first AI agent in ByteChef. The agent uses an OpenAI model and one tool, the HTTP Client, to fetch a random quote from a public API, then explains the quote in one sentence and logs the answer. It runs on a manual trigger, so after importing it you connect your OpenAI API key, click Run and watch the tool call in the execution log.

This workflow fetches an inspirational quote on weekdays, and a joke on the weekends and then sends it to the email address you specify.

New to ByteChef? This simple workflow is the perfect way to get started. In just a few minutes, you'll build your first automation: it fetches a random quote from the internet and logs it. It uses a manual trigger, so after importing it you click Run and see the result immediately. Swap in a Schedule trigger when you want it to run on its own.

Polls Gmail for new emails and uses OpenAI to classify each one into a single department label (Sales, Support, Finance, Operations, HR, or URGENT). The matching Gmail label is applied, the department's recipient address is stored, and the original email is forwarded to the correct team.

When a Google Forms response is submitted, downloads the attached CV PDF from Google Drive, extracts its text, and summarizes it with AI. OpenAI scores the candidate from 0 to 10 against a Senior Java Developer job description. The name, email, summary, and score are recorded in a Google Sheet, and the CV file is filed into a Rejected or Potential candidates Google Drive folder based on the score.

Lists all files in a local documentation directory recursively, then loops over each file, reads its contents, and loads it into a Pinecone vector database as a Markdown document, building a searchable knowledge base for retrieval-augmented generation.

This workflow prompts OpenAI for an inspirational quote on weekdays and a joke on the weekends. The quote is enriched with some context. Afterwards, the joke or the quote is sent via email.

When a new HubSpot ticket is created, fetches the ticket and uses OpenAI to rewrite its content into a clear, structured format. OpenAI then categorizes the ticket as tech or customer support, and a condition routes a Slack notification with the rewritten ticket to either the tech-team or sales-team channel.

On every new Gmail message, checks whether it arrived within business hours (Mon-Fri, 09:00-17:00 UTC). For emails received outside working hours, OpenAI classifies the urgency, the email is logged to a Google Sheet, and a condition branches: urgent emails get an AI-summarized internal Slack alert plus an urgent auto-reply, while non-urgent emails get an AI-generated helpful auto-reply.

Polls Gmail for new emails. For each email, OpenAI extracts the patient name, doctor type, and requested date-time period. Free time slots are fetched from Google Calendar for that period, the workflow loops over them keeping only slots at least 45 minutes long, and OpenAI proposes a list of concrete 45-minute appointment options. The workflow replies to the original email with the suggestions.

When currency rate between EUR and USD changes, action is invoked. Python Script is used to process data and apply arithmetic operations.

Retrieves an offices.csv object from AWS S3, reads it into rows, and loops over each row keeping only four selected columns. The reduced rows are collected into a list, written to a new CSV file, and uploaded back to S3 as offices_reduced.csv. The workflow runs on demand.

Deletes a specific sheet from a Google Sheets spreadsheet and logs the result. A minimal, on-demand example workflow.

Lists all sheets in a Google Sheets spreadsheet and logs them. A minimal, on-demand example workflow.

On the first day of every month at 09:00, computes the previous month, creates a new Google Sheet tab for it, and fetches the tracked-time report from Nifty. It loops over each tracked task to retrieve details and compute its duration in hours, writes a row per task, adds a total-hours row, and emails a notification with a link to the spreadsheet.

Reads a local cities.csv file, parses it, and loops over each row. Rows with both a City and a State value are logged and inserted into the PostgreSQL CITY table; rows missing either value are logged as warnings. After the loop, it queries the row count of the CITY table and logs the total.