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Documentation Index

Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt

Use this file to discover all available pages before exploring further.

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Prerequisites

The Mem0 toolkit requires the mem0ai Python package and either a Mem0 API key for cloud usage or local configuration for self-hosted deployments. 
pip install mem0ai
For cloud usage with the Mem0 app: 
export MEM0_API_KEY=your_api_key
export MEM0_ORG_ID=your_org_id          # Optional
export MEM0_PROJECT_ID=your_project_id  # Optional

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Example

The following example demonstrates how to create an agent with access to Mem0 memory:
cookbook/tools/mem0_tools.py
from textwrap import dedent

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.mem0 import Mem0Tools

USER_ID = "jane_doe"
SESSION_ID = "agno_session"

# Initialize the Agent with Mem0Tools
agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    tools=[Mem0Tools()],
    user_id=USER_ID,
    session_id=SESSION_ID,
    add_state_in_messages=True,
    markdown=True,
    instructions=dedent(
        """
        You have an evolving memory of this user. Proactively capture new 
        personal details, preferences, plans, and relevant context the user 
        shares, and naturally bring them up in later conversation. Before 
        answering questions about past details, recall from your memory 
        to provide precise and personalized responses. Keep your memory concise: store only meaningful 
        information that enhances long-term dialogue. If the user asks to start fresh,
        clear all remembered information and proceed anew.
        """
    ),
    show_tool_calls=True,
)

# Interact with the Agent to store memories
agent.print_response("I live in NYC")
agent.print_response("I lived in San Francisco for 5 years previously")
agent.print_response("I'm going to a Taylor Swift concert tomorrow")

# Query the stored memories
agent.print_response("Summarize all the details of the conversation")

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Toolkit Params

ParameterTypeDefaultDescription
configdictNoneConfiguration dictionary for self-hosted Mem0 instance.
api_keystrNoneMem0 API key. If not provided, uses MEM0_API_KEY env var.
user_idstrNoneDefault user ID for memory operations.
org_idstrNoneOrganization ID. If not provided, uses MEM0_ORG_ID env var.
project_idstrNoneProject ID. If not provided, uses MEM0_PROJECT_ID env var.
inferboolTrueWhether to enable automatic memory inference and extraction.

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Toolkit Functions

FunctionDescription
add_memoryAdds facts to the user’s memory. Supports both text strings and structured dictionaries. Returns success confirmation or error message.
search_memoryPerforms semantic search across the user’s stored memories. Takes query (str) to find relevant facts. Returns list of search results or error message.
get_all_memoriesRetrieves all memories for the current user. Returns list of all stored memories.
delete_all_memoriesDeletes all memories associated with the current user. Returns success confirmation or error message.

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Configuration Options

from agno.tools.mem0 import Mem0Tools

config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "test",
            "path": "db",
        }
    },
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-4o-mini",
            "temperature": 0.1,
            "max_tokens": 1000,
        }
    },
    "embedder": {
        "provider": "openai",
        "config": {
            "model": "text-embedding-ada-002",
        }
    }
}

mem0_tools = Mem0Tools(config=config)

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Developer Resources