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Add Mnemon

OrganizationPopular
nanocoai
add-mnemon

Add persistent graph-based memory via mnemon. Agents recall past context before responding and remember insights after each turn.

Overview

Publishernanocoai
Repositorynanoclaw
Skill nameadd-mnemon
Stars
30.9K
Forks
12.8K
Bundled files
3
LicenseMIT
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Add Mnemon 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.
    https://github.com/nanocoai/nanoclaw/tree/main/.claude/skills/add-mnemon
  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/nanocoai/nanoclaw.git /tmp/nanoclaw
mkdir -p .claude/skills
cp -r /tmp/nanoclaw/.claude/skills/add-mnemon .claude/skills/add-mnemon
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Add Mnemon 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 Add Mnemon 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 Add Mnemon 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.

Add Mnemon — Persistent Memory

Installs mnemon in the agent container image. On each container start, mnemon setup registers Claude Code hooks that surface relevant memory before the agent responds and store new insights after each turn. Memory is written to the per-agent-group .claude/ mount and survives container restarts.

Provider Compatibility

mnemon hooks fire only under --target claude-code. Use this skill on agent groups that run the default Claude provider. The provider is the materialized provider key in each group's container.json (absent or claude = default Claude provider). Confirm it before applying:

bash
grep -H '"provider"' groups/*/container.json 2>/dev/null   # no match, or "provider": "claude" = Claude

If a group sets a different provider (e.g. "provider": "opencode"), it spawns its own process and never invokes the claude CLI, so the hooks registered by mnemon setup do not run for that group.

Phase 1: Pre-flight

Check if already applied

bash
grep -q 'MNEMON_VERSION' container/Dockerfile && echo "Already applied" || echo "Not applied"

If already applied, re-run Phase 2 anyway — every step is idempotent and skips work that is already in place — then continue to Phase 3 (Verify).

Check latest mnemon version

bash
curl -fsSL https://api.github.com/repos/mnemon-dev/mnemon/releases/latest | grep '"tag_name"'

Note the version (e.g. v0.1.1) — use it as MNEMON_VERSION in the next step.

Phase 2: Apply Changes

1. Dockerfile — install mnemon binary

Insert the mnemon block immediately above the # ---- Bun runtime section of container/Dockerfile (skip if grep -q 'MNEMON_VERSION' container/Dockerfile already matches):

dockerfile
# ---- mnemon — persistent agent memory ----------------------------------------
ARG MNEMON_VERSION=0.1.1
RUN ARCH=$(dpkg --print-architecture) && \
    curl -fsSL "https://github.com/mnemon-dev/mnemon/releases/download/v${MNEMON_VERSION}/mnemon_${MNEMON_VERSION}_linux_${ARCH}.tar.gz" \
    | tar -xz -C /usr/local/bin mnemon && \
    chmod +x /usr/local/bin/mnemon

ENV MNEMON_DATA_DIR=/home/node/.claude/mnemon

MNEMON_DATA_DIR points into the per-agent-group .claude/ mount, so memory persists across container restarts.

2. Entrypoint — run mnemon setup on each container start

mnemon setup is idempotent. Run it once per container/entrypoint.sh. First check whether the line is already present:

bash
grep -q 'mnemon setup' container/entrypoint.sh && echo "Already wired" || echo "Wire it"

If it prints Wire it, add the setup call right after set -e, before the cat that captures stdin, so the result looks like:

bash
#!/bin/bash
# NanoClaw agent container entrypoint.
#
# ...existing header comment...

set -e

mnemon setup --target claude-code --yes --global >/dev/stderr 2>&1

cat > /tmp/input.json

exec bun run /app/src/index.ts < /tmp/input.json

>/dev/stderr 2>&1 routes all mnemon output to stderr (docker logs) so it doesn't interfere with the JSON stdin handshake between host and agent-runner.

3. Copy the integration tests

Both reach-ins are into container build/runtime files that aren't importable or typed (a GitHub-release binary in the Dockerfile, a shell line in the entrypoint), so structural tests guard them. Copy them into the host test tree:

bash
cp .claude/skills/add-mnemon/mnemon-dockerfile.test.ts src/mnemon-dockerfile.test.ts
cp .claude/skills/add-mnemon/mnemon-entrypoint.test.ts src/mnemon-entrypoint.test.ts
pnpm exec vitest run src/mnemon-dockerfile.test.ts src/mnemon-entrypoint.test.ts

mnemon-dockerfile.test.ts asserts the MNEMON_VERSION ARG and MNEMON_DATA_DIR ENV are present (red if the install layer is dropped on an upgrade). mnemon-entrypoint.test.ts asserts the entrypoint invokes mnemon setup --target claude-code (red if the wiring is removed).

4. Rebuild and smoke-test the image

bash
./container/build.sh
docker run --rm --entrypoint mnemon nanoclaw-agent:latest --version

Phase 3: Restart and Verify

Restart the service

Run from your NanoClaw project root:

bash
source setup/lib/install-slug.sh
systemctl --user restart $(systemd_unit)              # Linux
# launchctl kickstart -k gui/$(id -u)/$(launchd_label)   # macOS

Confirm mnemon hooks are registered

After the next container starts, check that setup ran:

bash
docker logs $(docker ps --filter label=nanoclaw-session --format "{{.Names}}" | head -1) 2>&1 | grep -i mnemon

Then inspect the hooks inside the running container:

bash
docker exec $(docker ps --filter label=nanoclaw-session --format "{{.Names}}" | head -1) \
  cat /home/node/.claude/settings.json | grep -A5 mnemon

Test memory recall

Have a conversation with the agent, then start a new session and reference something from the earlier one. Mnemon should surface the relevant context automatically without you restating it.

Memory Storage

Mnemon writes to /home/node/.claude/mnemon/ inside the container, which maps to the per-agent-group .claude/ directory on the host. To find the exact host path:

bash
docker inspect $(docker ps --filter label=nanoclaw-session --format "{{.Names}}" | head -1) \
  --format '{{range .Mounts}}{{if eq .Destination "/home/node/.claude"}}{{.Source}}{{end}}{{end}}'

To reset all memory for an agent, stop the container and delete the mnemon/ subdirectory from that host path.

Troubleshooting

mnemon: command not found in container

The image wasn't rebuilt after adding the Dockerfile layer. Run ./container/build.sh and restart.

Memory not persisting across restarts

Verify MNEMON_DATA_DIR resolves to a mounted path (not an in-container ephemeral directory):

bash
docker exec <container> sh -c 'ls -la $MNEMON_DATA_DIR'

If the directory is empty after conversations, the mount is missing or the path is wrong. Check the host mount with the docker inspect command above.

Agent not using past memory

mnemon setup writes hooks into /home/node/.claude/settings.json. Verify:

bash
docker exec <container> cat /home/node/.claude/settings.json

If the hooks are absent, mnemon setup may have failed silently. Check container startup logs for errors from mnemon.

Setup fails at container start

Run setup manually inside a running container to see the full error:

bash
docker exec -it <container> mnemon setup --target claude-code --yes --global

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Add Mnemon AI skill do?

Add persistent graph-based memory via mnemon. Agents recall past context before responding and remember insights after each turn.

Why use Add Mnemon on TypingMind?

Because you install it once and use it with any model. Add Mnemon 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 Add Mnemon in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nanocoai/nanoclaw/tree/main/.claude/skills/add-mnemon. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Add Mnemon?

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 Add Mnemon?

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

Is the Add Mnemon AI skill free?

Yes. It is published on GitHub by nanocoai under the MIT 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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