memory_summarize_strategy.py
"""
Optimize Memories With Summarize Strategy
=========================================
This example demonstrates memory optimization using the summarize strategy,
which combines all memories into one summary for token reduction.
"""
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.memory import MemoryManager, SummarizeStrategy
from agno.memory.strategies.types import MemoryOptimizationStrategyType
from agno.models.openai import OpenAIChat
# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
db_file = "tmp/memory_summarize_strategy.db"
db = SqliteDb(db_file=db_file)
user_id = "user2"
# ---------------------------------------------------------------------------
# Create Agent and Memory Manager
# ---------------------------------------------------------------------------
agent = Agent(
model=OpenAIChat(id="gpt-4o-mini"),
db=db,
update_memory_on_run=True,
)
memory_manager = MemoryManager(
model=OpenAIChat(id="gpt-4o-mini"),
db=db,
)
# ---------------------------------------------------------------------------
# Run Optimization
# ---------------------------------------------------------------------------
if __name__ == "__main__":
print("Creating memories...")
agent.print_response(
"I have a wonderful pet dog named Max who is 3 years old. He's a golden retriever and he's such a friendly and energetic dog. "
"We got him as a puppy when he was just 8 weeks old. He loves playing fetch in the park and going on long walks. "
"Max is really smart too - he knows about 15 different commands and tricks. Taking care of him has been one of the most "
"rewarding experiences of my life. He's basically part of the family now.",
user_id=user_id,
)
agent.print_response(
"I currently live in San Francisco, which is an amazing city despite all its challenges. I've been here for about 5 years now. "
"I work in the tech industry as a product manager at a mid-sized software company. The tech scene here is incredible - "
"there are so many smart people working on interesting problems. The cost of living is definitely high, but the opportunities "
"and the community make it worthwhile. I live in the Mission district which has great food and a vibrant culture.",
user_id=user_id,
)
agent.print_response(
"On weekends, I really enjoy hiking in the beautiful areas around the Bay Area. There are so many amazing trails - "
"from Mount Tamalpais to Big Basin Redwoods. I usually go hiking with a group of friends and we try to explore new trails every month. "
"I also love trying new restaurants. San Francisco has such an incredible food scene with cuisines from all over the world. "
"I'm always on the lookout for hidden gems and new places to try. My favorite types of cuisine are Japanese, Thai, and Mexican.",
user_id=user_id,
)
agent.print_response(
"I've been learning to play the piano for about a year and a half now. It's something I always wanted to do but never had time for. "
"I finally decided to commit to it and I practice almost every day, usually for 30-45 minutes. "
"I'm working through classical pieces right now - I can play some simple Bach and Mozart compositions. "
"My goal is to eventually be able to play some jazz piano as well. Having a creative hobby like this has been great for my mental health "
"and it's nice to have something completely different from my day job.",
user_id=user_id,
)
print("\nBefore optimization:")
memories_before = agent.get_user_memories(user_id=user_id)
print(f" Memory count: {len(memories_before)}")
strategy = SummarizeStrategy()
tokens_before = strategy.count_tokens(memories_before)
print(f" Token count: {tokens_before} tokens")
print("\nIndividual memories:")
for i, memory in enumerate(memories_before, 1):
print(f" {i}. {memory.memory}")
print("\nOptimizing memories with 'summarize' strategy...")
memory_manager.optimize_memories(
user_id=user_id,
strategy=MemoryOptimizationStrategyType.SUMMARIZE,
apply=True,
)
print("\nAfter optimization:")
memories_after = agent.get_user_memories(user_id=user_id)
print(f" Memory count: {len(memories_after)}")
tokens_after = strategy.count_tokens(memories_after)
print(f" Token count: {tokens_after} tokens")
if tokens_before > 0:
reduction_pct = ((tokens_before - tokens_after) / tokens_before) * 100
tokens_saved = tokens_before - tokens_after
print(f" Reduction: {reduction_pct:.1f}% ({tokens_saved} tokens saved)")
if memories_after:
print("\nSummarized memory:")
print(f" {memories_after[0].memory}")
else:
print("\n No memories found after optimization")
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 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
memory_summarize_strategy.py, then run:python memory_summarize_strategy.py