agentapps 0.1.0

AgenticAI - A flexible multi-agent orchestration framework with

AgentApps

A flexible multi-agent orchestration framework for building intelligent agent applications.

Features

• 🤖 Simple Agent Creation - Clean, intuitive Phi-style API
• 👥 Team Collaboration - Multiple agents working together
• 🔄 Sequential Workflows - Automatic multi-step execution
• 🛠️ Built-in Tools - Web search, scraping, calculations
• 🎯 Custom Tools - Easy tool creation
• 📊 Streaming Support - Real-time responses
• 🔍 Web Search - DuckDuckGo integration
• 🌐 Web Scraping - Extract content from any URL

Installation

pip install agentapps

This installs all dependencies:

• openai - OpenAI API client
• ddgs - DuckDuckGo search
• beautifulsoup4 - HTML parsing
• requests - HTTP requests

Quick Start

from agentapps import Agent
from agentapps.model import OpenAIChat
from agentapps.tools import SearchSummaryTool

# Create an agent
agent = Agent(
    name="Research Assistant",
    role="Search and analyze information",
    model=OpenAIChat(id="gpt-4", api_key="your-openai-key"),
    tools=[SearchSummaryTool()],
    instructions=["Always include sources"],
    show_tool_calls=True
)

# Use it!
agent.print_response("What is the latest news about AI?")

Available Tools

SearchSummaryTool

Search the web and get detailed snippets:

from agentapps.tools import SearchSummaryTool

agent = Agent(
    name="Searcher",
    model=OpenAIChat(id="gpt-4", api_key="key"),
    tools=[SearchSummaryTool()]
)

WebScraperTool

Scrape content from URLs:

from agentapps.tools import WebScraperTool

agent = Agent(
    name="Scraper",
    model=OpenAIChat(id="gpt-4", api_key="key"),
    tools=[WebScraperTool()]
)

CalculatorTool

Perform calculations:

from agentapps.tools import CalculatorTool

agent = Agent(
    name="Calculator",
    model=OpenAIChat(id="gpt-4", api_key="key"),
    tools=[CalculatorTool()]
)

Team Agents

Create teams that work together sequentially:

from agentapps import Agent
from agentapps.model import OpenAIChat
from agentapps.tools import SearchSummaryTool, WebScraperTool

# Create specialist agents
search_agent = Agent(
    name="Search Agent",
    role="Search the web",
    model=OpenAIChat(id="gpt-4", api_key="your-key"),
    tools=[SearchSummaryTool()]
)

scraper_agent = Agent(
    name="Scraper Agent",
    role="Read web pages",
    model=OpenAIChat(id="gpt-4", api_key="your-key"),
    tools=[WebScraperTool()]
)

# Create team with sequential workflow
team = Agent(
    team=[search_agent, scraper_agent],
    instructions=[
        "First, search for relevant URLs",
        "Then, scrape content from those URLs",
        "Finally, provide comprehensive answer"
    ],
    show_tool_calls=True
)

# Team automatically: searches → scrapes → answers
team.print_response("Research NVIDIA's latest AI developments")

Custom Tools

Create your own tools easily:

from agentapps import Tool

class WeatherTool(Tool):
    def __init__(self):
        super().__init__(
            name="get_weather",
            description="Get weather for a city"
        )
    
    def execute(self, city: str) -> str:
        # Your implementation
        return f"Weather in {city}: Sunny, 72°F"
    
    def get_parameters(self):
        return {
            "type": "object",
            "properties": {
                "city": {"type": "string", "description": "City name"}
            },
            "required": ["city"]
        }

# Use it
agent = Agent(
    name="Weather Agent",
    model=OpenAIChat(id="gpt-4", api_key="key"),
    tools=[WeatherTool()]
)

Examples

Stock Analysis

agent = Agent(
    name="Stock Analyst",
    role="Analyze stocks",
    model=OpenAIChat(id="gpt-4", api_key="key"),
    tools=[SearchSummaryTool()],
    instructions=["Include price targets and analyst ratings"]
)

agent.print_response("Analyze NVDA stock with latest news and recommendations")

Research Assistant

research_team = Agent(
    team=[search_agent, scraper_agent],
    instructions=[
        "Search for academic sources",
        "Read full articles",
        "Provide comprehensive summary with citations"
    ]
)

research_team.print_response("What are the latest breakthroughs in quantum computing?")

API Reference

Agent

Agent(
    name: str = "Agent",
    role: str = "General Assistant",
    model: Model = None,
    tools: List[Tool] = None,
    instructions: List[str] = None,
    team: List[Agent] = None,
    show_tool_calls: bool = False,
    markdown: bool = False,
    temperature: float = None
)

Methods

• run(message: str, stream: bool = False) - Execute agent
• print_response(message: str, stream: bool = False) - Print response
• clear_history() - Clear conversation history
• add_tool(tool: Tool) - Add a tool
• get_info() - Get agent information

Requirements

• Python >= 3.8
• OpenAI API key

License

MIT License

Contributing

Contributions welcome! Please feel free to submit a Pull Request.

Links

• GitHub: https://github.com/91Abdul/agentapps
• PyPI: https://pypi.org/project/agentapps
• Issues: https://github.com/91Abdul/agentapps/issues