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This example demonstrates how to use max_tool_calls_from_history to limit tool calls in team context across multiple research queries.

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Code

filter_tool_calls_from_history.py
from textwrap import dedent

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
from agno.team.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools

# Create specialized research agents
tech_researcher = Agent(
    name="Alex",
    role="Technology Researcher",
    instructions=dedent("""
        You specialize in technology and AI research.
        - Focus on latest developments, trends, and breakthroughs
        - Provide concise, data-driven insights
        - Cite your sources
    """).strip(),
)

business_analyst = Agent(
    name="Sarah",
    role="Business Analyst",
    instructions=dedent("""
        You specialize in business and market analysis.
        - Focus on companies, markets, and economic trends
        - Provide actionable business insights
        - Include relevant data and statistics
    """).strip(),
)

# Create research team with tools and context management
research_team = Team(
    name="Research Team",
    model=OpenAIChat("gpt-4o"),
    members=[tech_researcher, business_analyst],
    tools=[DuckDuckGoTools()],  # Team uses DuckDuckGo for research
    description="Research team that investigates topics and provides analysis.",
    instructions=dedent("""
        You are a research coordinator that investigates topics comprehensively.
        
        Your Process:
        1. Use DuckDuckGo to search for information on the topic
        2. Delegate detailed analysis to the appropriate specialist
        3. Synthesize research findings with specialist insights
        
        Guidelines:
        - Always start with web research using your DuckDuckGo tools
        - Choose the right specialist based on the topic (tech vs business)
        - Combine your research with specialist analysis
        - Provide comprehensive, well-sourced responses
    """).strip(),
    db=SqliteDb(db_file="tmp/research_team.db"),
    session_id="research_session",
    add_history_to_context=True,
    num_history_runs=6,  # Load last 6 research queries
    max_tool_calls_from_history=3,  # Keep only last 3 research results
    markdown=True,
)

research_team.print_response("What are the latest developments in AI agents?", stream=True)
research_team.print_response("How is the tech market performing this quarter?", stream=True)
research_team.print_response("What are the trends in LLM applications?", stream=True)
research_team.print_response("What companies are leading in AI infrastructure?", stream=True)

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Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2

Install libraries

pip install -U agno openai ddgs sqlalchemy
3

Export your OpenAI API key

  export OPENAI_API_KEY="your_openai_api_key_here"
4

Create a Python file

Create a Python file and add the above code.
touch filter_tool_calls_from_history.py
5

Run Team

python filter_tool_calls_from_history.py
6

Find All Cookbooks

Explore all the available cookbooks in the Agno repository. Click the link below to view the code on GitHub:Agno Cookbooks on GitHub