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Task Automation with AI

Learn how to automate complex tasks using QuantaLogic's intelligent agent system, combining multiple tools and reasoning capabilities.

Overview

Task automation enables your agent to: - Process web content - Analyze and summarize information - Integrate multiple tools - Perform multi-step reasoning tasks

Prerequisites

  • Python 3.12 or later
  • QuantaLogic library installed
  • OpenAI API key (or alternative LLM provider)

Setting Up Task Automation Tools

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import os
from quantalogic import Agent
from quantalogic.console_print_events import console_print_events
from quantalogic.console_print_token import console_print_token
from quantalogic.tools import (
    LLMTool, 
    MarkitdownTool
)

# Set API key
os.environ["OPENAI_API_KEY"] = "your-openai-key"

# Define model and tools
MODEL_NAME = "gpt-4o-mini"
agent = Agent(
    model_name=MODEL_NAME,
    tools=[
        MarkitdownTool(),
        LLMTool(
            model_name=MODEL_NAME, 
            on_token=console_print_token
        ),
    ],
)

Available Automation Tools

Tool Description
MarkitdownTool Read and process web content
LLMTool Perform advanced reasoning tasks

Example Tasks

1. Web Content Analysis

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# Analyze and summarize latest AI research
result = agent.solve_task(
    """
    1. Read the latest news about AI from arxiv.org
    2. Select the top 5 articles based on impact
    3. Summarize key points of each article
    """,
    streaming=True
)
print(result)

2. Multi-Step Research Task

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# Comprehensive research task
result = agent.solve_task(
    """
    1. Research emerging AI technologies
    2. Compare different machine learning approaches
    3. Create a summary report with pros and cons
    """,
    streaming=True
)
print(result)

3. Content Summarization

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# Summarize complex documents
result = agent.solve_task(
    """
    1. Read a long research paper
    2. Extract key findings
    3. Write an executive summary
    """,
    streaming=True
)
print(result)

Event Monitoring

Track task execution and debug complex workflows:

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# Configure event listeners
agent.event_emitter.on(
    event=[
        "task_complete",
        "task_think_start",
        "task_think_end",
        "tool_execution_start",
        "tool_execution_end",
    ],
    listener=console_print_events
)

# Optional token streaming
agent.event_emitter.on(
    event=["stream_chunk"],
    listener=console_print_token
)

Best Practices

  • Break complex tasks into clear steps
  • Use streaming for long-running tasks
  • Leverage multiple tools
  • Monitor task execution
  • Validate results

Intelligent Automation

Combine tools creatively to solve complex, multi-step tasks.

Complete Code Example

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import os
from quantalogic import Agent
from quantalogic.console_print_events import console_print_events
from quantalogic.console_print_token import console_print_token
from quantalogic.tools import (
    LLMTool, 
    MarkitdownTool
)

# Set API key
os.environ["OPENAI_API_KEY"] = "your-openai-key"

# Initialize agent with task automation tools
MODEL_NAME = "gpt-4o-mini"
agent = Agent(
    model_name=MODEL_NAME,
    tools=[
        MarkitdownTool(),
        LLMTool(
            model_name=MODEL_NAME, 
            on_token=console_print_token
        ),
    ],
)

# Configure event monitoring
agent.event_emitter.on(
    event=[
        "task_complete",
        "task_think_start",
        "task_think_end",
        "tool_execution_start",
        "tool_execution_end",
    ],
    listener=console_print_events
)

# Execute a complex task
result = agent.solve_task(
    """
    1. Read the latest news about AI from arxiv.org
    2. Select the top 5 articles based on impact
    3. Summarize key points of each article
    """,
    streaming=True
)
print(result)

Task Complexity

Start with simple tasks and gradually increase complexity.