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By mem0ai
Add persistent memory to Claude Code tasks and AI apps via Mem0: retrieve relevant past decisions, strategies, and session states on new tasks; store user data for personalization; enable semantic search across long-term memories using Python/TS SDKs, hooks, and MCP tools.
npx claudepluginhub mem0ai/mem0 --plugin mem0Mem0 persistent memory integration for Codex. Automatically retrieve relevant memories at the start of each task, store key learnings when tasks complete, and capture session state before context is lost. Use the mem0 MCP tools (add_memory, search_memories, get_memories, etc.) for all memory operations.
Integrate Mem0 Platform into AI applications for persistent memory, personalization, and semantic search. Use this skill when the user mentions "mem0", "memory layer", "remember user preferences", "persistent context", "personalization", or needs to add long-term memory to chatbots, agents, or AI apps. Covers Python and TypeScript SDKs, framework integrations (LangChain, CrewAI, Vercel AI SDK, OpenAI Agents SDK, Pipecat), and the full Platform API. Use even when the user doesn't explicitly say "mem0" but describes needing conversation memory, user context retention, or knowledge retrieval across sessions.
Modifies files
Hook triggers on file write and edit operations
External network access
Connects to servers outside your machine
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Use Memind as persistent memory for Claude Code sessions.
Universal memory runtime — cross-session cognitive memory for Claude Code. Remembers decisions, patterns, and context across coding sessions.
Long-term semantic memory for Claude Code, powered by OpenViking. Auto-recall relevant memories at session start and capture important information during conversations.
Give your AI a memory — mine projects and conversations into a searchable palace. 19 MCP tools, auto-save hooks, and guided setup.
Battle-tested Claude Code plugin for engineering teams — 60 agents, 232 skills, 75 legacy command shims, production-ready hooks, and selective install workflows evolved through continuous real-world use
Core skills library for Claude Code: TDD, debugging, collaboration patterns, and proven techniques
Learn more · Join Discord · Demo
📄 Building Production-Ready AI Agents with Scalable Long-Term Memory →
⚡ +26% Accuracy vs. OpenAI Memory • 🚀 91% Faster • 💰 90% Fewer Tokens
🎉 mem0ai v1.0.0 is now available! This major release includes API modernization, improved vector store support, and enhanced GCP integration. See migration guide →
Mem0 ("mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. It remembers user preferences, adapts to individual needs, and continuously learns over time—ideal for customer support chatbots, AI assistants, and autonomous systems.
Core Capabilities:
Applications:
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Install the sdk via pip:
pip install mem0ai
Install sdk via npm:
npm install mem0ai
Manage memories from your terminal:
npm install -g @mem0/cli # or: pip install mem0-cli
mem0 init
mem0 add "Prefers dark mode and vim keybindings" --user-id alice
mem0 search "What does Alice prefer?" --user-id alice
See the CLI documentation for the full command reference.
Mem0 requires an LLM to function, with `gpt-4.1-nano-2025-04-14 from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our Supported LLMs documentation.
First step is to instantiate the memory:
from openai import OpenAI
from mem0 import Memory
openai_client = OpenAI()
memory = Memory()
def chat_with_memories(message: str, user_id: str = "default_user") -> str:
# Retrieve relevant memories
relevant_memories = memory.search(query=message, user_id=user_id, limit=3)
memories_str = "\n".join(f"- {entry['memory']}" for entry in relevant_memories["results"])