Tool Call Compression

Demonstrates team-level tool result compression in both sync and async workflows.

tool_call_compression.py
"""
Tool Call Compression
=============================

Demonstrates team-level tool result compression in both sync and async workflows.
"""

import asyncio
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
# ---------------------------------------------------------------------------
sync_tech_researcher = Agent(
    name="Alex",
    role="Technology Researcher",
    model=OpenAIResponses(id="gpt-5.2"),
    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(),
)

sync_business_analyst = Agent(
    name="Sarah",
    role="Business Analyst",
    model=OpenAIResponses(id="gpt-5.2"),
    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(),
)

async_tech_specialist = Agent(
    name="Tech Specialist",
    role="Technology Researcher",
    model=OpenAIResponses(id="gpt-5.2"),
    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(),
)

async_business_analyst = Agent(
    name="Sarah",
    role="Business Analyst",
    model=OpenAIResponses(id="gpt-5.2"),
    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
# ---------------------------------------------------------------------------
sync_research_team = Team(
    name="Research Team",
    model=OpenAIResponses(id="gpt-5.2"),
    members=[sync_tech_researcher, sync_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"),
    compress_tool_results=True,
    show_members_responses=True,
)

async_research_team = Team(
    name="Research Team",
    model=OpenAIResponses(id="gpt-5.2"),
    members=[async_tech_specialist, async_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 for analysis (tech vs business)
        - Combine your research with specialist analysis
        - Provide comprehensive, well-sourced responses
    """).strip(),
    db=SqliteDb(db_file="tmp/research_team2.db"),
    markdown=True,
    show_members_responses=True,
    compress_tool_results=True,
)


async def run_async_tool_compression() -> None:
    await async_research_team.aprint_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,
    )


# ---------------------------------------------------------------------------
# Run Team
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    # --- Sync ---
    sync_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,
    )

    # --- Async ---
    asyncio.run(run_async_tool_compression())

The default manager triggers after three uncompressed tool-result messages. Compression only runs when its threshold is reached, so a short answer may not demonstrate it. The script executes separate sync and async team runs with separate SQLite files; this is not a benchmark or a guarantee that the model stays within its context limit.

Run the Example

Set up your virtual environment

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

Install dependencies

uv pip install -U agno ddgs openai sqlalchemy

Export your OpenAI API key

export OPENAI_API_KEY="your_openai_api_key_here"

Run the example

Save the code above as tool_call_compression.py, then run:

python tool_call_compression.py

Full source: cookbook/03_teams/10_context_compression/tool_call_compression.py