Managing Tool Calls

This example demonstrates how to use max_tool_calls_from_history to limit the number of tool calls included in the agent’s context.

This helps manage context size and reduce token costs while still maintaining complete history in your database.

Code

filter_tool_calls_from_history.py

import random

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses


def get_weather_for_city(city: str) -> str:
    """Get weather for a city"""
    conditions = ["Sunny", "Cloudy", "Rainy", "Snowy", "Foggy", "Windy"]
    temperature = random.randint(-10, 35)
    condition = random.choice(conditions)
    return f"{city}: {temperature}°C, {condition}"


agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[get_weather_for_city],
    instructions="You are a weather assistant. Get the weather using the get_weather_for_city tool.",
    # Only keep 3 most recent tool calls in context
    max_tool_calls_from_history=3,
    db=SqliteDb(db_file="tmp/weather_data.db"),
    add_history_to_context=True,
    markdown=True,
)

cities = [
    "Tokyo",
    "Delhi",
    "Shanghai",
    "São Paulo",
    "Mumbai",
    "Beijing",
    "Cairo",
    "London",
]

print(
    f"{'Run':<5} | {'City':<15} | {'History':<8} | {'Current':<8} | {'In Context':<11} | {'In DB':<8}"
)
print("-" * 90)

for i, city in enumerate(cities, 1):
    run_response = agent.run(f"What's the weather in {city}?")

    # Count tool calls in context
    history_tool_calls = sum(
        len(msg.tool_calls)
        for msg in run_response.messages
        if msg.role == "assistant"
        and msg.tool_calls
        and getattr(msg, "from_history", False)
    )

    # Count tool calls from current run
    current_tool_calls = sum(
        len(msg.tool_calls)
        for msg in run_response.messages
        if msg.role == "assistant"
        and msg.tool_calls
        and not getattr(msg, "from_history", False)
    )

    total_in_context = history_tool_calls + current_tool_calls

    # Total tool calls stored in database (unfiltered)
    saved_messages = agent.get_session_messages()
    saved_tool_calls = (
        sum(
            len(msg.tool_calls)
            for msg in saved_messages
            if msg.role == "assistant" and msg.tool_calls
        )
        if saved_messages
        else 0
    )

    print(
        f"{i:<5} | {city:<15} | {history_tool_calls:<8} | {current_tool_calls:<8} | {total_in_context:<11} | {saved_tool_calls:<8}"
    )

Usage

1. Create a Python file

   Create filter_tool_calls_from_history.py with the code above.

2. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   

   uv venv --python 3.12
   .venv\Scripts\activate
   
3. Install dependencies

   uv pip install -U agno openai sqlalchemy
   
4. Export your OpenAI API key

   Mac/Linux

   export OPENAI_API_KEY="your_openai_api_key_here"
   

   $Env:OPENAI_API_KEY="your_openai_api_key_here"
   
5. Run Agent

   python filter_tool_calls_from_history.py