Skip to main content
Activepieces is an open-source, no-code automation platform that enables users to connect apps, automate workflows, and integrate AI-powered actions through a visual interface. It supports scheduled triggers, conditional logic, and integrations with tools like Gmail, OpenAI, Slack, and Twitter—allowing technical and non-technical users to build scalable automations with ease. Dappier is a platform that connects LLMs and automation frameworks to real-time, rights-cleared data from trusted sources, including web search, finance, and news. By providing enriched, prompt-ready data, Dappier empowers automations with verified and up-to-date information for a wide range of applications.

​
Real-Time Use Cases

The following real-time workflows demonstrate how to use Dappier actions inside Activepieces to build dynamic, real-world automations. Each example highlights scheduled triggers, real-time data retrieval, AI-powered text generation, and multi-app integrations such as Twitter and Gmail.
  • Stock Market Analyst Bot Automatically analyzes any stock or public company mentioned in your inbox. This workflow identifies the stock ticker, pulls live financial data using Dappier, generates a full investment report using OpenAI, and sends it back via Gmail.
  • Auto-Tweet Real-Time News Tweets trending AI-related content every few minutes. It uses Dappier to pull live headlines, OpenAI to write engaging tweets under 280 characters, and Twitter to publish them—completely hands-free.

​
Overview

The Activepieces integration with Dappier enables users to build powerful, real-time automations by connecting their flows to rights-cleared data from the public web, financial markets, and media publishers. By combining Dappier’s live data tools with OpenAI and other pieces in Activepieces, users can build end-to-end workflows that automatically respond to the latest trends, financial insights, or curated content—without writing any backend logic. This integration includes access to the following Dappier-powered actions:
  • Real Time Data – Perform live Google-like web searches including the latest news, weather, deals, events, and general updates.
  • Stock Market Data – Retrieve real-time financial data, earnings updates, stock prices, and market sentiment.
  • Sports News – Access sports headlines and breaking stories from publishers like Sportsnaut, LAFB Network, Ringside Intel, and more.
  • Lifestyle News – Get curated stories from lifestyle sites like The Mix, Nerdable, Snipdaily, and Familyproof.
Each action accepts natural language queries and can be added to your to create responsive, AI-driven automations.

​
Usage with Activepieces

To use Dappier actions inside your Activepieces workflow, you’ll start by setting up a new flow and then adding the Dappier piece using the visual editor. Dappier actions can be placed after any trigger (like a schedule or email) and paired with OpenAI or other integrations to act on live data.

​
Step 1: Create a New Flow

Go to cloud.activepieces.com and click “New Flow”. Give your flow a name and click “Start building”.

​
Step 2: Add a Trigger

Choose a trigger based on your use case, such as:
  • Schedule – Run the flow every X minutes
  • Gmail – Trigger on new incoming emails
  • Slack – Trigger on a command or message
  • Webhook – Trigger the flow from an external system

​
Step 3: Add the Dappier Action

Click the ”+” button to add a new step, then search for and select the Dappier piece. Choose one of the following supported actions:
  • Real Time Data
  • Stock Market Data
  • Sports News
  • Lifestyle News
You’ll be prompted to authenticate with your Dappier API key, and then enter a natural language query to retrieve content dynamically.

​
Step 4: Chain Additional Steps (Optional)

Add downstream actions like:
  • OpenAI – Summarize or rephrase the results into human-friendly content
  • Gmail – Send the result as an email
  • Twitter – Post content as an auto-generated tweet
  • Slack – Send alerts or summaries to a channel
Once done, click “Publish” to activate the flow.

​
Available Actions

After adding the Dappier piece to your flow, you can select from four real-time data actions. Each action supports natural language queries and returns structured results that can be used directly or passed to other pieces like OpenAI, Gmail, or Twitter.

​
Real Time Data

Perform live web searches to access the latest information from trusted sources including Google-indexed news, weather updates, travel alerts, shopping deals, and more. Use case: General-purpose content automation triggered by live web search results.

​
Parameters

  • ​
    query (str)

    A natural language description of what you’re searching for. Required Example: 
    Latest news on artificial intelligence and machine learning this week

​
Output

Returns a real-time, formatted response with summaries of recent search results relevant to the query.

​
Stock Market Data

Access real-time stock data and financial news powered by Polygon.io. Use it to fetch company earnings, valuation metrics, price changes, and live market performance. Use case: Investment report generation, stock alert bots, and financial monitoring.

​
Parameters

  • ​
    query (str)

    A stock ticker or financial question written in natural language. Required Example: 
    Show stock performance and financial updates for AAPL

​
Output

Returns up-to-the-minute stock information including price movements, earnings highlights, and sentiment-tagged headlines.

​
Sports News

Retrieve real-time sports stories from top-tier media publishers like Sportsnaut, Ringside Intel, LAFB Network, Minnesota Sports Fan, and Bounding Into Sports. Use case: Sports digest generators, highlight summaries, and AI-driven fan updates.

​
Parameters

  • query (str)
    A natural language request for sports-related content. Required Example: 
    Latest sports news
  • number_of_results (int)
    Maximum number of articles to retrieve.
  • preferred_domain (str)
    Domain to prioritize in the results (e.g., sportsnaut.com).
  • num_articles_from_domain (int)
    Minimum number of articles required from the preferred domain.
  • search_algorithm (str)
    Strategy for result retrieval: "most_recent", "semantic", "most_recent_semantic", or "trending".

​
Output

Returns a formatted list of sports headlines with summaries and source links.

​
Lifestyle News

Get curated lifestyle updates from sources like The Mix, Nerdable, Snipdaily, and Familyproof. Covers entertainment, wellness, pop culture, and human-interest stories. Use case: Lifestyle newsletters, trending content generators, or social media content pipelines.

​
Parameters

  • query (str)
    A natural language request for lifestyle-related content. Required Example: 
    Latest lifestyle news
  • number_of_results (int)
    Maximum number of articles to retrieve.
  • preferred_domain (str)
    Domain to prioritize in the results (e.g., reuters.com).
  • num_articles_from_domain (int)
    Minimum number of articles required from the preferred domain.
  • search_algorithm (str)
    Strategy for result retrieval: "most_recent", "semantic", "most_recent_semantic", or "trending".

​
Output

Returns a list of lifestyle-focused articles with context-rich summaries and links.

​
Conclusion

Integrating Dappier with Activepieces unlocks powerful, real-time automation capabilities that require no code. Whether you’re building a financial analyst bot, an AI news tweet generator, or a personalized content delivery system, Dappier provides high-quality, rights-cleared data from trusted sources—ready for use inside any Activepieces flow. With just a few steps, you can:
  • Trigger automations on a schedule or based on external events
  • Retrieve live web or financial data using Dappier
  • Use OpenAI to convert raw data into human-friendly summaries
  • Deliver content through Gmail, Twitter, Slack, or custom channels
By combining Dappier’s live data tools with Activepieces’ visual automation builder, you can create responsive, intelligent workflows that stay up-to-date with the world—no backend or infrastructure required. For real-world recipes, see the full cookbooks at: