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cookbook/agent_concepts/memory/redis_memory.py
"""
This example shows how to use the Memory class with Redis storage.
"""

from agno.agent.agent import Agent
from agno.memory.v2.db.redis import RedisMemoryDb
from agno.memory.v2.memory import Memory
from agno.models.openai import OpenAIChat
from agno.storage.redis import RedisStorage

# Create Redis memory database
memory_db = RedisMemoryDb(
    prefix="agno_memory",  # Prefix for Redis keys to namespace the memories
    host="localhost",      # Redis host address
    port=6379,             # Redis port number
)

# Create memory instance with Redis backend
memory = Memory(db=memory_db)

# This will clear any existing memories
memory.clear()

# Session and user identifiers
session_id = "redis_memories"
user_id = "redis_user"

# Create agent with memory and Redis storage
agent = Agent(
    model=OpenAIChat(id="gpt-4o-mini"),
    memory=memory,
    storage=RedisStorage(prefix="agno_test", host="localhost", port=6379),
    enable_user_memories=True,
    enable_session_summaries=True,
)

# First interaction - introducing personal information
agent.print_response(
    "My name is John Doe and I like to hike in the mountains on weekends.",
    stream=True,
    user_id=user_id,
    session_id=session_id,
)

# Second interaction - testing if memory was stored
agent.print_response(
    "What are my hobbies?", 
    stream=True, 
    user_id=user_id, 
    session_id=session_id
)

# Display the memories stored in Redis
memories = memory.get_user_memories(user_id=user_id)
print("Memories stored in Redis:")
for i, m in enumerate(memories):
    print(f"{i}: {m.memory}")

​
Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2

Set environment variables

export OPENAI_API_KEY=xxx
3

Install libraries

pip install -U agno openai redis
4

Run Redis

docker run --name my-redis -p 6379:6379 -d redis
5

Run Example

python cookbook/agent_concepts/memory/redis_memory.py
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