Memory
MongoDB Memory Storage
Examples
- Introduction
- Getting Started
- Agents
- Teams
- Workflows
- Applications
Agent Concepts
- Multimodal
- RAG
- Knowledge
- Memory
- Basic Memory Operations
- Persistent Memory with SQLite
- Agentic Memory Creation
- Basic Memory Search
- Agentic Memory Search
- Agent Memory Creation
- Agent Memory Management
- Agent with Session Summaries
- Multiple Agents Sharing Memory
- Multi-User Multi-Session Chat
- MongoDB Memory Storage
- PostgreSQL Memory Storage
- Redis Memory Storage
- Mem0 Memory
- Async
- Hybrid Search
- Storage
- Tools
- Vector Databases
- Embedders
Models
- Anthropic
- AWS Bedrock
- AWS Bedrock Claude
- Azure AI Foundry
- Azure OpenAI
- Cohere
- DeepInfra
- DeepSeek
- Fireworks
- Gemini
- Groq
- Hugging Face
- Mistral
- NVIDIA
- Ollama
- OpenAI
- Perplexity
- Together
- xAI
- IBM
- LM Studio
- LiteLLM
- LiteLLM OpenAI
Memory
MongoDB Memory Storage
Code
cookbook/agent_concepts/memory/mongodb_memory.py
"""
This example shows how to use the Memory class with MongoDB storage.
"""
import asyncio
import os
from agno.agent.agent import Agent
from agno.memory.v2.db.mongodb import MongoMemoryDb
from agno.memory.v2.memory import Memory
from agno.models.openai.chat import OpenAIChat
# Get MongoDB connection string from environment
# Format: mongodb://username:password@localhost:27017/
mongo_url = "mongodb://localhost:27017/"
database_name = "agno_memory"
# Create MongoDB memory database
memory_db = MongoMemoryDb(
connection_string=mongo_url,
database_name=database_name,
collection_name="memories" # Collection name to use in the database
)
# Create memory instance with MongoDB backend
memory = Memory(db=memory_db)
# This will create the collection if it doesn't exist
memory.clear()
# Create agent with memory
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
memory=memory,
enable_user_memories=True,
)
async def run_example():
# Use the agent with MongoDB-backed memory
await agent.aprint_response(
"My name is Jane Smith and I enjoy painting and photography.",
user_id="jane@example.com",
)
await agent.aprint_response(
"What are my creative interests?",
user_id="jane@example.com",
)
# Display the memories stored in MongoDB
memories = memory.get_user_memories(user_id="jane@example.com")
print("Memories stored in MongoDB:")
for i, m in enumerate(memories):
print(f"{i}: {m.memory}")
if __name__ == "__main__":
asyncio.run(run_example())
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 pymongo
4
Run Example
python cookbook/agent_concepts/memory/mongodb_memory.py