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Hivemind Memory

OrganizationPopular
activeloopai
hivemind-memory

Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.

Overview

Publisheractiveloopai
Repositoryhivemind
Skill namehivemind-memory
Stars
1.6K
Forks
107
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by activeloopai on GitHub. Read the source before you install it.

Installation

Install the Hivemind Memory AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/activeloopai/hivemind.git /tmp/hivemind
mkdir -p .claude/skills
cp -r /tmp/hivemind/harnesses/claude-code/skills/hivemind-memory .claude/skills/hivemind-memory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hivemind Memory in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hivemind Memory on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hivemind Memory is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Hivemind Memory

You have TWO memory sources. ALWAYS check BOTH when the user asks you to recall, remember, or look up ANY information:

  1. Your built-in memory (~/.claude/) — personal per-project notes
  2. Hivemind global memory (~/.deeplake/memory/) — global memory shared across all sessions, users, and agents in the org

Memory Structure

~/.deeplake/memory/
├── index.md                          ← START HERE — table of all sessions
├── summaries/
│   ├── session-abc.md                ← AI-generated wiki summary
│   └── session-xyz.md
└── sessions/
    └── username/
        ├── user_org_ws_slug1.jsonl   ← raw session data
        └── user_org_ws_slug2.jsonl

How to Search

  1. First: Read ~/.deeplake/memory/index.md — quick scan of all sessions with dates, projects, descriptions
  2. If you need details: Read the specific summary at ~/.deeplake/memory/summaries/<session>.md
  3. If you need raw data: Read the session JSONL at ~/.deeplake/memory/sessions/<user>/<file>.jsonl
  4. Keyword search: Grep pattern="keyword" path="~/.deeplake/memory"

Do NOT jump straight to reading raw JSONL files. Always start with index.md and summaries.

Organization Management

The auth command path is injected at session start. Use the exact path from the session context. Each argument is separate — do NOT quote subcommands together:

  • node "<AUTH_CMD>" login — SSO login
  • node "<AUTH_CMD>" whoami — show current user/org
  • node "<AUTH_CMD>" org list — list organizations
  • node "<AUTH_CMD>" org switch <name-or-id> — switch organization
  • node "<AUTH_CMD>" workspaces — list workspaces
  • node "<AUTH_CMD>" workspace <id> — switch workspace
  • node "<AUTH_CMD>" invite <email> <ADMIN|WRITE|READ> — invite member (ALWAYS ask user which role first)
  • node "<AUTH_CMD>" members — list members
  • node "<AUTH_CMD>" remove <user-id> — remove member
  • node "<AUTH_CMD>" --help — show all commands

Skill Management (skillify)

Hivemind can mine reusable skills from agent session logs and share them across your team. Each argument is separate — do NOT quote subcommands together.

  • hivemind skillify — show current scope, team, install location, per-project state
  • hivemind skillify pull — sync project skills from the org table to local FS
  • hivemind skillify pull --user <email> — only skills authored by that user
  • hivemind skillify pull --users <a,b,c> — multiple authors (CSV)
  • hivemind skillify pull --all-users — explicit "no author filter" (default)
  • hivemind skillify pull --to <project|global> — install location (project=cwd/.claude/skills, global=~/.claude/skills)
  • hivemind skillify pull --dry-run — preview without touching disk
  • hivemind skillify pull --force — overwrite local files even if up-to-date (creates .bak)
  • hivemind skillify pull <skill-name> — pull only that one skill (combines with --user)
  • hivemind skillify push <skill-name> — upload a local skill to the org table (inverse of pull; re-push lands a new version)
  • hivemind skillify push --from <project|global> — which local skills dir to read (default: project)
  • hivemind skillify push --dry-run — preview without writing to the org table
  • hivemind skillify unpull — remove every skill previously installed by pull
  • hivemind skillify unpull --user <email> — remove only that author's pulls
  • hivemind skillify unpull --not-mine — remove all pulls except your own
  • hivemind skillify unpull --dry-run — preview without touching disk
  • hivemind skillify scope <me|team> — sharing scope for newly mined skills
  • hivemind skillify install <project|global> — default install location for new skills
  • hivemind skillify promote <skill-name> — move a project skill to the global location
  • hivemind skillify team add|remove|list <username> — manage team member list
  • hivemind skillify mine-local — one-shot: mine skills from local sessions, no auth needed

Embeddings (semantic memory search)

Opt-in, persisted in ~/.deeplake/config.json.

  • hivemind embeddings install — download deps (~600MB), symlink agents, set enabled:true
  • hivemind embeddings enable — flip enabled:true (run install first if deps missing)
  • hivemind embeddings disable — flip enabled:false + SIGTERM daemon (deps stay on disk)
  • hivemind embeddings uninstall [--prune] — remove agent symlinks + disable; --prune wipes deps too
  • hivemind embeddings status — show config + deps + per-agent link state

Important: Bash Only

Only use bash commands (cat, ls, grep, echo, jq, head, tail, sed, awk, etc.) to interact with ~/.deeplake/memory/. Do NOT use python, python3, node, curl, or other interpreters — they are not available in the memory filesystem. If a task seems to require Python, rewrite it using bash tools (e.g., cat file.json | jq 'keys | length').

Limits

If a file returns empty after 2 attempts, skip it and move on. Report what you found rather than exhaustively retrying.

Getting Started

After installing the plugin:

  1. Run /hivemind:login to authenticate
  2. Start using memory — ask questions, Claude automatically captures and searches

Configuration

  • HIVEMIND_DEBUG=1 claude — enable verbose logging to ~/.deeplake/hook-debug.log
  • HIVEMIND_CAPTURE=false claude — disable session capture

Frequently asked questions

What does the Hivemind Memory AI skill do?

Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.

Why use Hivemind Memory on TypingMind?

Because you install it once and use it with any model. Hivemind Memory is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hivemind Memory in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/activeloopai/hivemind/tree/main/harnesses/claude-code/skills/hivemind-memory. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hivemind Memory?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hivemind Memory?

As many as you like. As long as a model supports skills, you can use Hivemind Memory with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hivemind Memory AI skill free?

Yes. It is published on GitHub by activeloopai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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