filter_tool_calls_from_history.py
"""
Filter Tool Calls From History
==============================
Demonstrates limiting historical tool call results in team context.
"""
from textwrap import dedent
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses
from agno.team import Team
from agno.tools.websearch import WebSearchTools
# ---------------------------------------------------------------------------
# Create Members
# ---------------------------------------------------------------------------
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 Team
# ---------------------------------------------------------------------------
research_team = Team(
name="Research Team",
model=OpenAIResponses(id="gpt-5.2"),
members=[tech_researcher, business_analyst],
tools=[WebSearchTools()], # 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 a lot of 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. Try to get as much information as possible.
- 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,
max_tool_calls_from_history=3,
markdown=True,
show_members_responses=True,
)
# ---------------------------------------------------------------------------
# Run Team
# ---------------------------------------------------------------------------
if __name__ == "__main__":
research_team.print_response(
"What are the latest developments in AI agents? Which companies dominate the market? Find the latest news and reports on the companies.",
stream=True,
)
research_team.print_response(
"How is the tech market performing this quarter? How about last year? Find the latest news and reports on Mag 7.",
stream=True,
)
research_team.print_response(
"What are the trends in LLM applications for enterprises? Find the latest news and reports on the trends.",
stream=True,
)
research_team.print_response(
"What companies are leading in AI infrastructure? Find reports on the companies and their products.",
stream=True,
)
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno ddgs openai sqlalchemy
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run the example
Save the code above as
filter_tool_calls_from_history.py, then run:python filter_tool_calls_from_history.py