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What are Agents?

Agents are autonomous programs that use language models to achieve tasks. They solve problems by running tools, accessing knowledge and memory to improve responses.

Instead of a rigid binary definition, let’s think of Agents in terms of agency and autonomy.

  • Level 0: Agents with no tools (basic inference tasks).
  • Level 1: Agents with tools for autonomous task execution.
  • Level 2: Agents with knowledge, combining memory and reasoning.
  • Level 3: Teams of agents collaborating on complex workflows.

If you haven’t built your first agent yet, follow this guide and then dive into more advanced concepts.

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Example: Research Agent

Let’s create a research agent that can search the web using DuckDuckGo, scrape the top links using Newspaper4k and write a research report for us. Ideally we’ll use specialized tools (like Exa) but let’s start with the free tools first.

The description and instructions are converted to the system message and the input is passed as the user message. Set debug_mode=True to view logs behind the scenes.

1

Create Research Agent

Create a file research_agent.py

research_agent.py
from datetime import datetime
from pathlib import Path
from textwrap import dedent

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.exa import ExaTools

today = datetime.now().strftime("%Y-%m-%d")

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    tools=[ExaTools(start_published_date=today, type="keyword")],
    description=dedent("""\
        You are Professor X-1000, a distinguished AI research scientist with expertise
        in analyzing and synthesizing complex information. Your specialty lies in creating
        compelling, fact-based reports that combine academic rigor with engaging narrative.

        Your writing style is:
        - Clear and authoritative
        - Engaging but professional
        - Fact-focused with proper citations
        - Accessible to educated non-specialists\
    """),
    instructions=dedent("""\
        Begin by running 3 distinct searches to gather comprehensive information.
        Analyze and cross-reference sources for accuracy and relevance.
        Structure your report following academic standards but maintain readability.
        Include only verifiable facts with proper citations.
        Create an engaging narrative that guides the reader through complex topics.
        End with actionable takeaways and future implications.\
    """),
    expected_output=dedent("""\
    A professional research report in markdown format:

    # {Compelling Title That Captures the Topic's Essence}

    ## Executive Summary
    {Brief overview of key findings and significance}

    ## Introduction
    {Context and importance of the topic}
    {Current state of research/discussion}

    ## Key Findings
    {Major discoveries or developments}
    {Supporting evidence and analysis}

    ## Implications
    {Impact on field/society}
    {Future directions}

    ## Key Takeaways
    - {Bullet point 1}
    - {Bullet point 2}
    - {Bullet point 3}

    ## References
    - [Source 1](link) - Key finding/quote
    - [Source 2](link) - Key finding/quote
    - [Source 3](link) - Key finding/quote

    ---
    Report generated by Professor X-1000
    Advanced Research Systems Division
    Date: {current_date}\
    """),
    markdown=True,
    show_tool_calls=True,
    add_datetime_to_instructions=True,
)

# Example usage
if __name__ == "__main__":
    # Generate a research report on a cutting-edge topic
    agent.print_response(
        "Research the latest developments in brain-computer interfaces", stream=True
    )

# More example prompts to try:
"""
Try these research topics:
1. "Analyze the current state of solid-state batteries"
2. "Research recent breakthroughs in CRISPR gene editing"
3. "Investigate the development of autonomous vehicles"
4. "Explore advances in quantum machine learning"
5. "Study the impact of artificial intelligence on healthcare"
"""
2

Run the agent

Install libraries

pip install openai exa-py agno

Run the agent

python research_agent.py
CommunityAgent.run()