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MemOS Local Memory — Agent Guide

This skill describes how to use the MemOS memory tools so you can reliably search and use the user's long-term conversation history, query team-shared data, share tasks, and discover or pull reusable skills.

Two sharing planes exist and must not be confused:

• Local agent sharing: visible to agents in the same OpenClaw workspace only.
• Team sharing: visible to teammates through the configured team server.

How memory is provided each turn

• Automatic recall (hook): At the start of each turn, the system runs a memory search using the user's current message and injects relevant past memories into your context. You do not need to call any tool for that.
• When that is not enough: If the user's message is very long, vague, or the automatic search returns no memories , you should generate your own short, focused query and call memory_search yourself.
• Memory isolation: Each agent can only see its own local private memories and local public memories. Team-shared data only appears when you search with scope="group" or scope="all".

Tools — what they do and when to call

memory_search

• What it does: Search long-term conversation memory for past conversations, user preferences, decisions, and experiences. Returns relevant excerpts with chunkId and optionally task_id. Only returns memories belonging to the current agent or marked as public.
• When to call:
  • The automatic recall did not run or returned nothing.
  • The user's query is long or unclear — generate a short query yourself and call memory_search(query="...").
  • You need to search with a different angle (e.g. filter by role='user').
• Parameters:
  • query (string, required) — Natural language search query.
  • scope (string, optional) — 'local' (default) for current agent + local shared memories, or / to include team-shared memories.

memory_get

• What it does: Get the full original text of a memory chunk. Use to verify exact details from a search hit.
• When to call: A memory_search hit looks relevant but you need to see the complete original content, not just the summary/excerpt.
• Parameters:
  • chunkId (string, required) — The chunkId from a search hit.
  • maxChars (number, optional) — Max characters to return (default 4000, max 12000).

memory_write_public

• What it does: Create a brand new local shared memory. These memories are visible to all agents in the same OpenClaw workspace during memory_search. This does not publish anything to the team server.
• When to call: In multi-agent or collaborative scenarios, when you want to create a new persistent shared note from scratch (e.g. shared decisions, conventions, configurations, workflows). Do not use it if you already have a specific memory chunk to expose.
• Parameters:
  • content (string, required) — The content to write to local shared memory.
  • summary (string, optional) — Short summary of the content.

memory_share

• What it does: Share an existing memory either with local OpenClaw agents, to the team, or to both.
• When to call: You already have a useful memory chunk and want to expose it beyond the current agent.
• Do not use when: You are creating a new shared note from scratch. In that case use memory_write_public.
• Parameters:
  • chunkId (string, required) — Existing memory chunk ID.
  • target (string, optional) — 'agents' (default), 'hub', or 'both'.
  • visibility (string, optional) — Team visibility when target includes team: 'public' (default) or .

memory_unshare

• What it does: Remove an existing memory from local agent sharing, team sharing, or both.
• When to call: A memory should no longer be visible outside the current agent or should be removed from the team.
• Parameters:
  • chunkId (string, required) — Existing memory chunk ID.
  • target (string, optional) — 'agents', 'hub', or 'all' (default).
  • privateOwner (string, optional) — Rare fallback only for older public memories that have no recorded original owner.

task_summary

• What it does: Get the detailed summary of a complete task: title, status, narrative summary, and related skills. Use when memory_search returns a hit with a task_id and you need the full story. Preserves critical information: URLs, file paths, commands, error codes, step-by-step instructions.
• When to call: A memory_search hit included a task_id and you need the full context of that task.
• Parameters:
  • taskId (string, required) — The task_id from a memory_search hit.

skill_get

• What it does: Retrieve a proven skill (experience guide) by skillId or by taskId. If you pass a taskId, the system will find the associated skill automatically.
• When to call: A search hit has a task_id and the task has a "how to do this again" guide. Use this to follow the same approach or reuse steps.
• Parameters:
  • skillId (string, optional) — Direct skill ID.
  • taskId (string, optional) — Task ID — will look up the skill linked to this task.
  • At least one of skillId or taskId must be provided.

skill_search

• What it does: Search available skills by natural language. Searches your own skills, local shared skills, or both. It can also include team skills.
• When to call: The current task requires a capability or guide you don't have. Use skill_search to find one first; after finding it, use skill_get to read it, then skill_install to load it for future turns.
• Parameters:
  • query (string, required) — Natural language description of the needed skill.
  • scope (string, optional) — 'mix' (default, self + local shared), 'self', 'public' (local shared only), or / to include team results.

skill_install

• What it does: Install a learned skill into the agent workspace so it becomes permanently available. After installation, the skill will be loaded automatically in future sessions.
• When to call: After skill_get when the skill is useful for ongoing use.
• Parameters:
  • skillId (string, required) — The skill ID to install.

skill_publish

• What it does: Share a skill with local agents, or publish it to the team.
• When to call: You have a useful skill that other agents or your team could benefit from.
• Parameters:
  • skillId (string, required) — The skill ID to publish.
  • target (string, optional) — 'agents' (default) or 'hub'.
  • visibility (string, optional) — When target='hub', use 'public' (default) or 'group'.
  • groupId (string, optional) — Optional team group ID when and .

skill_unpublish

• What it does: Stop local agent sharing, remove a team-published copy, or do both.
• When to call: You want to stop sharing a previously published skill.
• Parameters:
  • skillId (string, required) — The skill ID to unpublish.
  • target (string, optional) — 'agents' (default), 'hub', or 'all'.

network_memory_detail

• What it does: Fetches the full content behind a team search hit.
• When to call: A memory_search result came from the team and you need the full shared memory content.
• Parameters: remoteHitId.

task_share / task_unshare

• What they do: Share a local task to the team, or remove it later.
• When to call: A task is valuable to your group or to the whole team and should be discoverable via shared search.
• Parameters: taskId, plus sharing visibility/scope when required.

network_skill_pull

• What it does: Pulls a team-shared skill bundle down into local storage.
• When to call: skill_search found a useful team skill and you want to use it locally or offline.
• Parameters: skillId.

network_team_info

• What it does: Returns current team server connection information, user, role, and groups.
• When to call: You need to confirm whether team sharing is configured or which groups the current client belongs to.
• Call this first before: memory_share(... target='hub'|'both'), memory_unshare(... target='hub'|'all'), task_share, task_unshare, skill_publish(... target='hub'), skill_unpublish(... target='hub'|'all'), or network_skill_pull.
• Parameters: none.

memory_timeline

• What it does: Expand context around a memory search hit. Pass the chunkId from a search result to read the surrounding conversation messages.
• When to call: A memory_search hit is relevant but you need the surrounding dialogue.
• Parameters:
  • chunkId (string, required) — The chunkId from a memory_search hit.
  • window (number, optional) — Context window ±N messages, default 2.

memory_viewer

• What it does: Show the MemOS Memory Viewer URL. Call this when the user asks how to view, browse, manage, or check their memories. Returns the URL the user can open in their browser.
• When to call: The user asks where to see or manage their memories.
• Parameters: None.

Quick decision flow

1. No memories in context or auto-recall reported nothing → Call memory_search(query="...") with a self-generated short query.

2. Need to see the full original text of a search hit → Call memory_get(chunkId="...").

3. Search returned hits withtask_id and you need full context → Call task_summary(taskId="...").

4. Task has an experience guide you want to follow → Call skill_get(taskId="...") or skill_get(skillId="..."). Optionally skill_install(skillId="...") for future use.

Writing good search queries

• Prefer short, focused queries (a few words or one clear question).
• Use concrete terms : names, topics, tools, or decisions.
• If the user's message is long, derive one or two sub-queries rather than pasting the whole message.
• Use role='user' when you specifically want to find what the user said.

Memory ownership and agent isolation

Each memory is tagged with an owner (e.g. agent:main, agent:sales-bot). This is handled automatically — you do not need to pass any owner parameter.

• Your memories: All tools (memory_search, memory_get, memory_timeline) automatically scope queries to your agent's own memories.
• Local shared memories: Memories marked as local shared are visible to all agents in the same OpenClaw workspace. Use memory_write_public to create them, or memory_share(target='agents') to expose an existing chunk.
• Cross-agent isolation: You cannot see memories owned by other agents (unless they are public).
• How it works: The system identifies your agent ID from the OpenClaw runtime context and applies owner filtering automatically on every search, recall, and retrieval.

Weekly Installs

92

Repository

memtensor/memos

GitHub Stars

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First Seen

Mar 9, 2026

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