Memory
Agentic Memory Search
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
Agentic Memory Search
Code
cookbook/agent_concepts/memory/05_memory_search_agentic.py
from agno.memory.v2.memory import Memory, UserMemory
from agno.models.google.gemini import Gemini
memory = Memory(model=Gemini(id="gemini-2.0-flash-exp"))
john_doe_id = "john_doe@example.com"
memory.add_user_memory(
memory=UserMemory(memory="The user enjoys hiking in the mountains on weekends"),
user_id=john_doe_id,
)
memory.add_user_memory(
memory=UserMemory(
memory="The user enjoys reading science fiction novels before bed"
),
user_id=john_doe_id,
)
# This searches using a model
memories = memory.search_user_memories(
user_id=john_doe_id,
query="What does the user like to do on weekends?",
retrieval_method="agentic",
)
print("John Doe's found memories:")
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 your API key
export GOOGLE_API_KEY=xxx
3
Install libraries
pip install -U agno google-generativeai
4
Run Example
python cookbook/agent_concepts/memory/05_memory_search_agentic.py