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xmemo

XMemo

by XMemo · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
24
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Install in OpenClaw
/install xmemo
Description
Persistent, user-owned memory for AI agents over hosted MCP. Remember decisions, recall project context, manage TODOs, and govern memory lifecycle across ses...
README (SKILL.md)

\r \r

XMemo Memory\r

\r Give your agent durable memory that survives across sessions, projects, and tools. XMemo is a hosted MCP memory service — no local database, no self-hosting required.\r \r

When to use\r

\r

  • The task depends on prior decisions, preferences, or project context.\r
  • The user asks to remember something for later.\r
  • You need to recall conventions, architecture notes, or past fixes before acting.\r
  • The user wants TODOs, reminders, or follow-ups tracked across sessions.\r \r

Workflow\r

\r

  1. Recall before assuming. Search or recall XMemo context before making decisions that prior memory could inform.\r
  2. Save what matters. Store durable facts: decisions, conventions, preferences, architecture notes, action items. Skip transient chat.\r
  3. Keep it concise. One clear memory per concept. Prefer structured facts over verbose narratives.\r
  4. Confirm before destroying. Always confirm the exact target before delete, forget, or overwrite operations.\r
  5. On auth failure, tell the user: "Visit https://xmemo.dev to sign in and get your token, or run xmemo login if the CLI is installed. Set XMEMO_KEY environment variable." Never request raw tokens in chat.\r \r

Available tools\r

\r Core memory operations provided by the XMemo MCP server:\r \r | Tool | Purpose |\r |------|---------|\r | remember | Save a new memory |\r | recall / recall_context | Retrieve relevant memories before answering |\r | search_memory | Search by query |\r | update_memory | Revise existing memory |\r | forget / forget_memory | Delete a memory |\r | redact_memory | Remove sensitive content while keeping audit trail |\r | explain_memory | Show why a memory exists or matched |\r | create_memory_todo | Create a follow-up task |\r | list_memory_todos | List pending TODOs |\r | complete_memory_todo | Mark a TODO done |\r | record_event | Log a milestone or decision |\r | get_timeline | Show recent events |\r | add_expense | Record a ledger entry |\r \r

Good memory candidates\r

\r

  • Repository conventions, build/test/deploy commands.\r
  • Architecture decisions and their rationale.\r
  • Coding style preferences approved by the user.\r
  • Release procedures and deployment notes.\r
  • TODOs and follow-ups for future sessions.\r
  • Bug fix context that might recur.\r \r

Never save\r

\r

  • Secrets, tokens, API keys, OAuth codes, cookies.\r
  • Private customer data or sensitive PII.\r
  • Temporary debugging output.\r
  • Large code blocks (link to files instead).\r
Usage Guidance
Install only if you intend to let the agent help with Convex and ClawHub maintainer workflows. Review the moderation and autoreview commands before running them, use --no-yolo for autoreview when you do not want a nested reviewer to have full local access, and avoid sending sensitive uncommitted diffs to fallback review CLIs.
Capability Tags
requires-oauth-tokenrequires-sensitive-credentials
Capability Assessment
Purpose & Capability
The artifacts provide Convex setup, auth, migration, performance, ClawHub moderation, PR-maintainer, UI-proof, and autoreview workflows that match their stated purposes.
Instruction Scope
Some workflows can perform high-impact actions, including moderation commands, PR comments, publishing UI proof, and code review through external CLIs, but the instructions include explicit target, confirmation, auth, audit-log, and verification requirements where appropriate.
Install Mechanism
No installer or startup hook was found; files are static skill instructions plus one helper script for autoreview.
Credentials
The autoreview helper discloses that nested Codex review runs with full sandbox bypass by default and offers --no-yolo/AUTOREVIEW_YOLO=0 to opt out; this is broad for review work but not hidden or automatically triggered.
Persistence & Privilege
No background persistence, credential harvesting, profile modification, or automatic privilege escalation was found; temporary files are cleaned and optional output is user-selected.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install xmemo
  3. After installation, invoke the skill by name or use /xmemo
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial release of xmemo-memory. - Provides persistent, user-owned memory for AI agents via the hosted XMemo MCP service. - Supports durable storage and retrieval of decisions, project context, TODOs, and architecture notes. - Includes a suite of core memory management tools: save, recall, search, update, forget, redact, and explain memories, as well as TODO and event tracking. - Guides on what to remember, what not to store, and privacy best practices. - Requires authentication via OAuth or XMEMO_KEY environment variable.
Metadata
Slug xmemo
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is XMemo?

Persistent, user-owned memory for AI agents over hosted MCP. Remember decisions, recall project context, manage TODOs, and govern memory lifecycle across ses... It is an AI Agent Skill for Claude Code / OpenClaw, with 24 downloads so far.

How do I install XMemo?

Run "/install xmemo" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is XMemo free?

Yes, XMemo is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does XMemo support?

XMemo is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created XMemo?

It is built and maintained by XMemo (@xmemo); the current version is v1.0.0.

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