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What are Agents?

Agents are AI programs that operate autonomously.

The core of an Agent is the model, tools and instructions:

  • Model: is the brain of an Agent, helping it reason, act, and respond to the user.
  • Tools: are the body of an Agent, enabling it to interact with the real world.
  • Instructions: guide the Agent’s behavior. Better the model, better it is at following instructions.

Agents also have memory, knowledge, storage and the ability to reason.

  • Reasoning: lets Agents “think” before responding and “analyze” the results of their actions (i.e. tool calls). Reasoning improves the Agents ability to solve problems that require multi-step tool use. Reasoning improves quality, but also increases latency and cost.
  • Knowledge: is domain-specific information that the Agent can search on demand to make better decisions (dynamic few-shot learning) and provide accurate responses (agentic RAG). Knowledge is stored in a vector database and this search on demand pattern is known as Agentic RAG. Agno (is aiming to) have first class support for the popular Agentic Search pattern, Hybrid Search + Reranking, for every major vector database.
  • Storage: is used by Agents to save session history and state in a database. Model APIs are stateless and storage enables us to continue conversations across runs using a session_id. This makes Agents stateful and enables multi-turn conversations.
  • Memory: gives Agents the ability to store and recall information from previous interactions, allowing them to learn user preferences and personalize their responses. This is an evolving field and Agno is aiming to support the popular Memory patterns.

If this is your first time building agents, follow these examples before diving into advanced concepts.

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Example: Research Agent

Let’s build a research agent using Exa to showcase how to guide the Agent to produce the report in a specific format. In advanced cases, we should use Structured Outputs instead.

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