Agent skill
mem0-codex
Mem0 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.
Install this agent skill to your Project
npx add-skill https://github.com/mem0ai/mem0/tree/main/mem0-plugin/skills/mem0-codex
SKILL.md
Mem0 Memory Protocol for Codex
You have access to persistent memory via the mem0 MCP tools. Follow this protocol to maintain context across sessions.
On every new task
- Call
search_memorieswith a query related to the current task or project to load relevant context. - Review returned memories to understand what has been learned in prior sessions.
- If appropriate, call
get_memoriesto browse all stored memories for this user.
After completing significant work
Extract key learnings and store them using the add_memory tool:
- Decisions made -> Include metadata
{"type": "decision"} - Strategies that worked -> Include metadata
{"type": "task_learning"} - Failed approaches -> Include metadata
{"type": "anti_pattern"} - User preferences observed -> Include metadata
{"type": "user_preference"} - Environment/setup discoveries -> Include metadata
{"type": "environmental"} - Conventions established -> Include metadata
{"type": "convention"}
Memories can be as detailed as needed -- include full context, reasoning, code snippets, file paths, and examples. Longer, searchable memories are more valuable than vague one-liners.
Before losing context
If context is about to be compacted or the session is ending, store a comprehensive session summary:
## Session Summary
### User's Goal
[What the user originally asked for]
### What Was Accomplished
[Numbered list of tasks completed]
### Key Decisions Made
[Architectural choices, trade-offs discussed]
### Files Created or Modified
[Important file paths with what changed]
### Current State
[What is in progress, pending items, next steps]
Include metadata: {"type": "session_state"}
Memory hygiene
- Do NOT write to MEMORY.md or any file-based memory. Use mem0 MCP tools exclusively.
- Only store genuinely useful learnings. Skip trivial interactions.
- Use specific, searchable language in memory content.
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Memory consolidation protocol. Reviews all stored memories, merges duplicates, removes noise and credentials, rewrites unclear entries, and enforces TTL expiration. Use when the user asks to clean up, consolidate, or review their memories. Also triggers automatically after sufficient activity (configurable).
memory-triage
Persistent long-term memory protocol powered by mem0. Evaluate conversations for durable facts worth storing via memory_add. Handles identity, preferences, decisions, configurations, rules, projects, and relationships. Loaded by the openclaw-mem0 plugin when skills mode is active.
mem0
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.
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).
mem0-cli
Mem0 CLI -- the command-line interface for mem0 memory operations. TRIGGER when: user mentions "mem0 cli", "mem0 command line", "@mem0/cli", "mem0-cli", "pip install mem0-cli", "npm install -g @mem0/cli", or is running mem0 commands in a terminal/shell (mem0 add, mem0 search, mem0 list, mem0 get, mem0 init, mem0 config, mem0 import). Also triggers when query includes CLI flags like --user-id, --output, --json, --agent, or describes bash/zsh/terminal/shell usage. DO NOT TRIGGER when: user asks about programmatic SDK integration in Python/TS code (use mem0 skill), or Vercel AI SDK provider (use mem0-vercel-ai-sdk skill).
mem0-vercel-ai-sdk
Mem0 provider for Vercel AI SDK (@mem0/vercel-ai-provider). TRIGGER when: user mentions "vercel ai sdk", "@mem0/vercel-ai-provider", "createMem0", "retrieveMemories", "addMemories", "getMemories", "searchMemories", "mem0 vercel", "AI SDK provider", "AI SDK memory", or is using generateText/streamText with mem0. Also triggers for Next.js apps needing memory-augmented AI. DO NOT TRIGGER when: user asks about direct Python/TS SDK calls without Vercel (use mem0 skill), or CLI terminal commands (use mem0-cli skill).
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