advanced_compression.py
"""
Advanced Compression
=============================
This example shows how to set a context token based limit for tool call compression.
"""
from agno.agent import Agent
from agno.compression.manager import CompressionManager
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses
from agno.tools.websearch import WebSearchTools
compression_prompt = """
You are a compression expert. Your goal is to compress web search results for a competitive intelligence analyst.
YOUR GOAL: Extract only actionable competitive insights while being extremely concise.
MUST PRESERVE:
- Competitor names and specific actions (product launches, partnerships, acquisitions, pricing changes)
- Exact numbers (revenue, market share, growth rates, pricing, headcount)
- Precise dates (announcement dates, launch dates, deal dates)
- Direct quotes from executives or official statements
- Funding rounds and valuations
MUST REMOVE:
- Company history and background information
- General industry trends (unless competitor-specific)
- Analyst opinions and speculation (keep only facts)
- Detailed product descriptions (keep only key differentiators and pricing)
- Marketing fluff and promotional language
OUTPUT FORMAT:
Return a bullet-point list where each line follows this format:
"[Company Name] - [Date]: [Action/Event] ([Key Numbers/Details])"
Keep it under 200 words total. Be ruthlessly concise. Facts only.
Example:
- Acme Corp - Mar 15, 2024: Launched AcmeGPT at $99/user/month, targeting enterprise market
- TechCo - Feb 10, 2024: Acquired DataStart for $150M, gaining 500 enterprise customers
"""
compression_manager = CompressionManager(
model=OpenAIResponses(id="gpt-5-mini"),
compress_token_limit=5000,
compress_tool_call_instructions=compression_prompt,
)
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
model=OpenAIResponses(id="gpt-5-mini"),
tools=[WebSearchTools()],
description="Specialized in tracking competitor activities",
instructions="Use the search tools and always use the latest information and data.",
db=SqliteDb(db_file="tmp/token_based_tool_call_compression.db"),
compression_manager=compression_manager,
add_history_to_context=True, # Add history to context
num_history_runs=3,
session_id="token_based_tool_call_compression",
)
# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
agent.print_response(
"""
Use the search tools and always use the latest information and data.
Research recent activities (last 3 months) for these AI companies:
1. OpenAI - product launches, partnerships, pricing
2. Anthropic - new features, enterprise deals, funding
3. Google DeepMind - research breakthroughs, product releases
4. Meta AI - open source releases, research papers
For each, find specific actions with dates and numbers.""",
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
advanced_compression.py, then run:python advanced_compression.py