custom_memory_instructions.py
"""
Custom Memory Capture Instructions
==================================
This example shows how to customize memory capture instructions and compare the
results with a default memory manager.
"""
from agno.db.postgres import PostgresDb
from agno.memory import MemoryManager
from agno.models.anthropic.claude import Claude
from agno.models.message import Message
from agno.models.openai import OpenAIChat
from rich.pretty import pprint
# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
memory_db = PostgresDb(db_url=db_url)
# ---------------------------------------------------------------------------
# Create Memory Managers
# ---------------------------------------------------------------------------
memory = MemoryManager(
model=OpenAIChat(id="gpt-4o"),
memory_capture_instructions="""\
Memories should only include details about the user's academic interests.
Only include which subjects they are interested in.
Ignore names, hobbies, and personal interests.
""",
db=memory_db,
)
# ---------------------------------------------------------------------------
# Run Memory Manager
# ---------------------------------------------------------------------------
if __name__ == "__main__":
john_doe_id = "john_doe@example.com"
memory.create_user_memories(
message="""\
My name is John Doe.
I enjoy hiking in the mountains on weekends,
reading science fiction novels before bed,
cooking new recipes from different cultures,
playing chess with friends.
I am interested to learn about the history of the universe and other astronomical topics.
""",
user_id=john_doe_id,
)
memories = memory.get_user_memories(user_id=john_doe_id)
print("John Doe's memories:")
pprint(memories)
memory = MemoryManager(model=Claude(id="claude-sonnet-4-5-20250929"), db=memory_db)
jane_doe_id = "jane_doe@example.com"
memory.create_user_memories(
messages=[
Message(role="user", content="Hi, how are you?"),
Message(role="assistant", content="I'm good, thank you!"),
Message(role="user", content="What are you capable of?"),
Message(
role="assistant",
content="I can help you with your homework and answer questions about the universe.",
),
Message(role="user", content="My name is Jane Doe"),
Message(role="user", content="I like to play chess"),
Message(
role="user",
content="Actually, forget that I like to play chess. I more enjoy playing table top games like dungeons and dragons",
),
Message(
role="user",
content="I'm also interested in learning about the history of the universe and other astronomical topics.",
),
Message(role="assistant", content="That is great!"),
Message(
role="user",
content="I am really interested in physics. Tell me about quantum mechanics?",
),
],
user_id=jane_doe_id,
)
memories = memory.get_user_memories(user_id=jane_doe_id)
print("Jane Doe's memories:")
pprint(memories)
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 anthropic openai psycopg-binary sqlalchemy
3
Export your API keys
export ANTHROPIC_API_KEY="your_anthropic_api_key_here"
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:ANTHROPIC_API_KEY="your_anthropic_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run PgVector
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql/data/pgdata \
-v pgvolume:/var/lib/postgresql/data \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:18
5
Run the example
Save the code above as
custom_memory_instructions.py, then run:python custom_memory_instructions.py