mem0

mem0

Author: yevanchen Version: 0.0.1 Type: tool

Description

mem0 is a memory management plugin that enables conversation history storage and retrieval for LLM applications.

Setup

1. Get your API key from mem0 dashboard
2. Install the package:

pip install mem0ai

3. Initialize the client:

from mem0 import MemoryClient
client = MemoryClient(api_key="your-api-key")

Memory Actions

add_memory

Stores conversation history and context for users.

messages = [
    {"role": "user", "content": "Hi, I'm Alex. I'm a vegetarian and I'm allergic to nuts."},
    {"role": "assistant", "content": "Hello Alex! I've noted your dietary preferences."}
]
client.add(messages, user_id="alex")

Backend logic:

• Messages are stored in user-specific partitions using user_id
• Supports conversation history and context storage
• Handles message format validation and processing
• Optimizes storage for efficient retrieval

retrieve_memory

Retrieves relevant conversation history based on queries.

query = "What can I cook for dinner tonight?"
memories = client.search(query, user_id="alex")

Backend logic:

• Semantic search across user's memory partition
• Returns relevant conversation snippets
• Handles context ranking and relevance scoring
• Optimizes query performance

Usage in Dify

1. In Dify workflows, place retrieve_memory before LLM calls to provide context
2. Add add_memory after LLM responses to store new interactions
3. user_id can be customized in workflow run API
4. Note: iframe and webapp modes currently don't support user_id due to lack of access control

Maybe Future Features

• Multimodal Support
• Memory Customization
• Custom Categories & Instructions
• Direct Import
• Async Client
• Memory Export
• Webhooks
• Graph Memory
• REST API Server
• OpenAI Compatibility
• Custom Prompts

For feature requests or discussions, contact evanchen@dify.ai