AgentStack

AgentStack is a CLI-first framework for rapidly scaffolding AI agent workflows. It helps developers generate agents, tasks, and tool configurations with minimal setup using simple commands. AgentStack supports multi-agent execution engines such as CrewAI and LangGraph and is designed to integrate seamlessly with real-time data tools and observability platforms.

Dappier is a platform that connects LLMs and Agentic AI agents to real-time, rights-cleared data from trusted sources, including web search, finance, and news. By providing enriched, prompt-ready data, Dappier empowers AI with verified and up-to-date information for a wide range of applications.

Real-Time Use Cases

The following real-time cookbooks demonstrate how to use Dappier tools inside AgentStack to build fully local, multi-agent workflows. Each example includes project initialization, agent and task setup, tool configuration, and runtime execution with OpenAI and AgentOps integration.

• Dynamic Travel Planner
  Generate a travel itinerary for a selected city and date, using Dappier’s real-time web search tool. The system retrieves current weather, live events, and hotel deals, then compiles a structured markdown itinerary with daily activity suggestions and booking links.

• Stock Market Researcher
  Build an end-to-end research agent that analyzes public companies using real-time web and financial data. This workflow combines company overviews, financial metrics, peer benchmarking, live stock performance, and categorized financial news into a single markdown investment report.

Overview

The AgentStack integration with Dappier allows developers to enhance their agent workflows with real-time search and AI-powered content recommendation tools. By connecting to Dappier’s pre-trained, rights-cleared APIs, agents built with AgentStack can retrieve up-to-date information across domains such as news, weather, finance, sports, pet care, sustainable living, and local events.

This integration includes access to the following tools:

• real_time_web_search: Perform real-time web queries across general domains including news, weather, events, and more.
• stock_market_data_search: Retrieve live financial news and stock-specific insights.
• get_sports_news: Access real-time sports news and analysis from top media sources.
• get_lifestyle_news: Retrieve lifestyle content recommendations from trusted lifestyle publications.
• get_iheartdogs_content: Surface dog care, behavior, and health tips from iHeartDogs.
• get_iheartcats_content: Fetch cat care guidance and lifestyle content from iHeartCats.
• get_greenmonster_guides: Recommend sustainable living content and eco-conscious guides.
• get_wishtv_news: Access local and national news, sports, entertainment, and multicultural headlines from WISH-TV.

These tools can be selectively added to agents and invoked through natural language tasks. By integrating Dappier with AgentStack, developers can create agents that reason with live, trusted data to deliver context-aware, up-to-date outputs across a variety of use cases.

Usage with AgentStack

To use Dappier tools inside your AgentStack project, you’ll first need to initialize a new project and then add the Dappier toolset using the CLI.

Step 1: Initialize an AgentStack Project

Run the following command to scaffold a new project:

agentstack init my_project_name

This will generate a full project structure using crewai

Step 2: Add the Dappier Tool

To add Dappier tools to your project, run:

agentstack tools add dappier

This will:

• Automatically install the dappier Python package (if not already installed)
• Add Dappier’s toolset to your project configuration
• Make the tools available for use inside your agents via the agentstack.tools["dappier"] registry

You can then selectively assign tools like real_time_web_search or get_sports_news to individual agents using helper functions inside crew.py.

Available Tools

After running agentstack tools add dappier, the following tools become available in your project. These tools can be selectively assigned to agents and used to perform real-time searches or content recommendations across various domains.

Real-Time Search Tools

These tools allow agents to access live data from the public web and financial markets using natural language queries. They return formatted summaries of current results pulled from trusted sources.

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real_time_web_search

Perform a real-time web search across a variety of domains such as news, weather, events, travel, and general updates.
Use case: Dynamic content retrieval when no stock symbol is required.

Parameters

• query (str)

  A natural language query describing the information to retrieve.
  Required
  Example: "What are the top tourist attractions in Rome today?"

Output

Returns a formatted string containing real-time search results from the public web, including summaries, source references, and context.

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stock_market_data_search

Retrieve real-time financial news, stock prices, earnings data, and sentiment-tagged insights. This tool is optimized for use with stock ticker symbols.
Use case: Live stock analysis and financial market updates.

Parameters

• query (str)

  A natural language query including a stock ticker or financial topic.
  Required
  Example: "Show me the latest earnings and performance of AAPL"

Output

Returns a formatted string summarizing stock-specific financial data, including price movements, valuation metrics, and financial news headlines.

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These tools are available after running agentstack tools add dappier and can be imported from agentstack.tools["dappier"] for use inside your agents via crew.py.

AI-Powered Recommendation Tools

These tools return high-quality, curated article recommendations using AI-powered retrieval across vertical-specific sources. Each tool accepts a natural language query and supports additional options for tuning the result relevance, domain focus, and retrieval strategy.

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get_sports_news

Get sports news from trusted sources like Sportsnaut, Ringside Intel, LAFB Network, and others.
Use case: Live sports updates, analysis, and highlights.

Parameters

• query (str)

  A natural language query for sports-related content.
  Required

• similarity_top_k (int)

  Number of top articles to return.
  Default: 9

• ref (str)

  Optional domain to prioritize.
  Example: "sportsnaut.com"

• num_articles_ref (int)

  Minimum number of articles from the reference domain.

• search_algorithm (str)

  Retrieval method: "most_recent", "semantic", "most_recent_semantic", "trending"
  Default: "most_recent"

Output

A formatted string of sports article headlines, summaries, and links.

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get_lifestyle_news

Access lifestyle stories and trends from The Mix, Nerdable, Snipdaily, Familyproof, and more.
Use case: Style, wellness, culture, and lifestyle journalism.

Parameters

Same as get_sports_news.

Output

A formatted list of lifestyle article titles, summaries, and domains.

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get_iheartdogs_content

Fetch pet care content from iHeartDogs.com covering health, training, grooming, and behavior.
Use case: Dog ownership support and informational content.

Parameters

Same as get_sports_news.

Output

A formatted string with dog-related article recommendations and links.

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get_iheartcats_content

Access curated cat care resources from iHeartCats.com.
Use case: Feline health, enrichment, and daily care articles.

Parameters

Same as get_sports_news.

Output

A formatted string of recommended articles tailored to cat care topics.

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get_greenmonster_guides

Get sustainable living tips and eco-conscious articles from GreenMonster.
Use case: Conscious consumerism, environmental awareness, and lifestyle.

Parameters

Same as get_sports_news.

Output

A list of articles related to green living, with descriptions and source references.

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get_wishtv_news

Fetch localized and national headlines from WISH-TV including breaking news, sports, politics, and multicultural stories.
Use case: Hyperlocal news delivery and trending updates.

Parameters

Same as get_sports_news.

Output

A formatted feed of news article summaries and sources from the WISH-TV network.

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These tools are available after running agentstack tools add dappier and can be imported from agentstack.tools["dappier"] for use inside your agents via crew.py.

Conclusion

Integrating Dappier with AgentStack enables AI agents to access real-time, trusted information across a wide range of domains. Whether you’re building a travel planner, stock research assistant, content summarizer, or local news agent, Dappier’s tools provide factual, prompt-ready data that enhances reasoning and output quality.

With just a few commands, you can scaffold your agent workflow using AgentStack and power it with live insights using Dappier — all while maintaining flexibility, observability, and modularity in your AI system design.