Agent skill · writing content · mem0ai
mem0
Mem0 Platform SDK for adding persistent memory to AI applications. TRIGGER when: user mentions "mem0", "MemoryClient", "memory layer", "remember user preferences", "persistent context", "personalization", or needs to add long-term memory to chatbots, agents, or AI apps. Covers Python SDK (mem0ai), TypeScript SDK (mem0ai), and framework integrations (LangChain, CrewAI, OpenAI Agents SDK, Pipecat, LlamaIndex, AutoGen, LangGraph). Also covers the open-source self-hosted Memory class. This is the DEFAULT mem0 skill for ambiguous queries. DO NOT TRIGGER when: user asks about CLI commands, terminal usage, or shell scripts (use mem0-cli), or Vercel AI SDK / @mem0/vercel-ai-provider / createMem0 (use mem0-vercel-ai-sdk).
Why this skill is useful
Provides specific commands and integration patterns for using the Mem0 SDK to manage persistent memory in AI applications, which the AI wouldn't reliably generate on its own.
What it needs
Requires node, python installed locally. Requires mem0 account access. About 4k tokens when loaded. Last updated 2026-08-06. 62,725 stars on the source repository.
What this skill does
Mem0 Platform Integration Skill Graph: This skill is part of the Mem0 skill graph: - mem0 (this skill) -- Platform Client SDK + OSS (Python + TypeScript) - mem0-cli (GitHub) -- Command-line interface - mem0-vercel-ai-sdk (GitHub) -- Vercel AI SDK provider Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy. For self-hosted usage, see the OSS section in the client references below. Step 1: Install and authenticate Python: TypeScript/JavaScript: Get an API key at: https://app.mem0.ai/dashboard/api-keys?utmsource=oss&utmmedium=skill-mem0 Don't have a MEM0APIKEY? Run mem0 init --agent --agent-caller <your-name> --json (after pip install mem0-cli or npm install -g @mem0/cli), substituting your agent identity (e.g. claude-code, cursor). If you forgot to pass --agent-caller, run mem0 identify <your-name> after init. The human can claim later with mem0 init --email <your-email>. Step 2: Initialize the client Python: TypeScript: For async Python, use AsyncMemoryClient. Step 3: Core operations Every Mem0 integration follows the same pattern: retrieve → generate → store. Add memories Search memories Get all memories Update a memory Delete a memory Common integration pattern Common edge cases Search returns empty: Memories process asynchronously. Wait 2-3s after add() before searching. Also verify userid matches exactly (case-sensitive) and use filters={"userid": "..."} syntax. AND filter with userid + agentid returns empty: Entities are stored separately. Use OR instead, or query separately. Duplicate memories: Don't mix infer=True (default) and infer=False for the same data. Stick to one mode. Wrong import: Always use from mem0 import MemoryClient (or AsyncMemoryClient for async). Do not use from mem0 import Memory. v3 defaults: topk=20, threshold=0.1, rerank=False. Adjust as needed for your use case. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills mem0ai/mem0--fce3da