What makes MeshMorize notable?

1.3K downloads. Trust evidence available

How should MeshMorize be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

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agentCLAWHUBUnverified

MeshMorize

Memory system for AI agents on OpenClaw-like hosts. File-based multi-layer memory: fresh daily layer (5-day rotation), mesh graph, auto-log of every exchange, cross-layer grep search, compliance check, crash-gap recovery from session transcripts, automation-registry lookup. Search before answering, log after answering. Local-first, $0 to run, survives restarts. Skill: MeshMorize Owner: mozz0 Summary: Memory system for AI agents on OpenClaw-like hosts. File-based multi-layer memory: fresh daily layer (5-day rotation), mesh graph, auto-log of every exchange, cross-layer grep search, compliance check, crash-gap recovery from session transcripts, automation-registry lookup. Search before answering, log after answering. Local-first, $0 to run, survives restarts. Tags: latest:4.0

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.3k

Updated

Oct 10, 2026

Version

4.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.3K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.3K downloadsadoption · observed Oct 10, 2026
Latest release: 4.0.0release · observed Sep 8, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17dbza9b0k9x98r9anh6vhhp187c9sj:josh-learns

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-mozz0-josh-learns/snapshot"

Documentation

CLAWHUB

101,488 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: "MeshMorize"
description: "Memory system for AI agents on OpenClaw-like hosts. File-based multi-layer memory: fresh daily layer (5-day rotation), mesh graph, auto-log of every exchange, cross-layer grep search, compliance check, crash-gap recovery from session transcripts, automation-registry lookup. Search before answering, log after answering. Local-first, $0 to run, survives restarts."
---

# MeshMorize 🧠

A local-first, file-based memory system for AI agents running on OpenClaw-like hosts. All state lives in plain Markdown + JSON on disk — no database server, no cloud dependency, no API cost. The bundled scripts are small, dependency-free Python (standard library + grep only).

**Core philosophy: memory is files, not sessions.** Sessions are ephemeral — they die on crashes, compaction, restarts, and reinstalls. Files survive all of those. If something isn't written to a file, it effectively didn't happen. This skill exists to make writing and finding those files automatic.

## What any agent gets

| Layer | Location | Purpose |
|-------|----------|---------|
| **Fresh** | `memory/fresh/today.md` … `4-days-ago.md` | Rolling 5-day window of recent context; read first at session start |
| **Daily log** | `memory/YYYY-MM-DD.md` | Timestamped record of every logged exchange, one file per day |
| **Mesh graph** | `memory/mesh.json` | Lightweight node/edge index with timestamps for long-lived topics |
| **Rolling log** | `memory/LATEST.md` | The most recent exchanges in one place |
| **Checkpoints** | `memory/checkpoints/` | Crash-recovery snapshots (`latest.json` + timestamped history) |
| **Decisions** | `memory/decisions/` | Dated decision records with mesh nodes |
| **Quarters** | `memory/quarters/` | Optional meaning-based day summaries (4 per day) |

## Tools

| Command | Source | What it does |
|---------|--------|--------------|
| `mem-bridge` | `memory/bridge.py` | Fresh-layer rotation, today-file creation, checkpoints, decision capture, mesh timestamps, session wrap |
| `auto_log` | `scripts/auto_log.py` | Append one timestamped entry to today's daily log + `LATEST.md` |
| `memory_search` | `scripts/memory_search.py` | Cross-layer search: fresh → daily logs → mesh (grep-based, $0) |
| `memcheck` | `scripts/memory_check.py` | 10-point compliance check of the whole memory chain |

## Install (any OpenClaw-like workspace)

The scripts respect the `OPENCLAW_WORKSPACE` environment variable and default to `~/.openclaw/workspace` (or your host's agent home). `memory/` and `scripts/` are relative to that workspace root.

1. **Place the files** (this repo is a skill bundle — copy, don't run in place):
   - `memory/bridge.py` → `<workspace>/memory/bridge.py`
   - `scripts/memory_search.py`, `scripts/auto_log.py`, `scripts/memory_check.py` → `<workspace>/scripts/`
2. **Make them callable** — symlink into a directory already on `PATH` (e.g. `~/.local/bin` or `~/.npm-global/bin`):
   ```bash
   ln -s "$(pwd)/memory/bridge.py"          ~/.loc

README.md

# MeshMorize 🧠

A local-first, file-based memory system for AI agents on OpenClaw-like hosts. Fresh daily layer with 5-day rotation, mesh graph indexing, auto-logging of every exchange, cross-layer grep search, compliance checking, and crash-gap recovery — all in plain Markdown + JSON, all dependency-free Python, all **$0 to run**.

> **Philosophy: memory is files, not sessions.** Sessions die on crashes, compaction, and reinstalls. Files survive. If it isn't written to a file, it didn't happen.

**~1,000+ downloads across GitHub + ClawHub.** Built by an AI and its human for real daily use — it has survived full OS reinstalls with every memory intact.

## The layers

```
memory/
├── fresh/                  # Rolling 5-day window (today → 4-days-ago)
│   ├── today.md            #   ← read first at session start
│   ├── yesterday.md
│   └── ...
├── YYYY-MM-DD.md           # Daily logs — timestamped record of every exchange
├── LATEST.md               # Rolling log of the most recent exchanges
├── mesh.json               # Lightweight node/edge graph with timestamps
├── checkpoints/            # Crash-recovery snapshots (latest.json + history)
├── decisions/              # Dated decision records (auto-mesh-linked)
└── quarters/               # Optional meaning-based day summaries
```

Search order is deliberate: **fresh → daily logs → mesh**. Grep-based, instant, no API calls.

## Install

1. **Copy the files into your agent workspace** (`<workspace>/memory/bridge.py`, `<workspace>/scripts/{memory_search,auto_log,memory_check}.py`). The scripts respect `OPENCLAW_WORKSPACE` and default to `~/.openclaw/workspace`.
2. **Symlink onto your PATH** (~/.local/bin or ~/.npm-global/bin):
   ```bash
   ln -s "$(pwd)/memory/bridge.py"          ~/.local/bin/mem-bridge
   ln -s "$(pwd)/scripts/auto_log.py"       ~/.local/bin/auto_log
   ln -s "$(pwd)/scripts/memory_search.py"  ~/.local/bin/memory_search
   ln -s "$(pwd)/scripts/memory_check.py"   ~/.local/bin/memcheck
   ```
3. **Run the bridge on every session start** (before answering anything):
   ```bash
   mem-bridge init-auto     # rotate fresh layer, create today.md, resume checkpoint, log startup
   ```

## The two protocols

### 1. Search before answer
```bash
memory_search "<2-5 keywords from the user's message>"
```
Run this **before** answering anything about the past, prior work, decisions, or plans. If there are hits, read the full source file — snippets are context, files are truth. If empty, check the automation registry and checkpoints before claiming "no record".

### 2. Log every exchange
```bash
auto_log "what was said, done, or decided"
```
Run this as the **last step** of any meaningful turn. One timestamped entry into today's daily log + `LATEST.md`. Log decisions and results, not trivia.

## Compliance

```bash
memcheck
```
10-point health check: logger, bridge, today-file age, fresh rotation, core files, mesh, raw log, secret store, PATH tools, heartbeat. The file lists encode one worksp

_meta.json

{
  "ownerId": "kn7ez75t441kh665xks93aax0x87cayy",
  "slug": "josh-learns",
  "version": "4.0.0",
  "publishedAt": 1788886927696
}

skill-card.md

## Description:

MeshMorize provides a local-first, file-based memory system for AI agents on OpenClaw-like hosts, with rotating recent context, daily logs, mesh indexing, grep search, compliance checks, and crash-gap recovery.

This skill is ready for commercial/non-commercial use.

## Publisher:

[mozz0](https://clawhub.ai/user/mozz0)

### License/Terms of Use:

MIT

## Use Case:

Developers and agent operators use MeshMorize to give agents durable local memory across sessions, restarts, and crashes. It helps agents search prior work, log meaningful exchanges, maintain lightweight memory indexes, and recover missing context from approved local sources.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Plaintext local memory can capture sensitive personal data or secrets if the agent logs too broadly.

Mitigation: Do not log credentials or unnecessary personal data; keep secrets in a separate ignored local store and scrub memory files before sharing or publishing them.

Risk: Transcript database reads and automation registry changes can expose or alter user context without sufficient scope.

Mitigation: Require explicit approval for the exact transcript session, time range, and automation job before reading or changing them, and remove temporary transcript copies after use.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/mozz0/skills/josh-learns)

## Skill Output:

**Output Type(s):** [Text, Markdown, Code, Shell commands, Configuration, Guidance]

**Output Format:** [Markdown guidance with shell command examples, file paths, and Python utility scripts]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Produces local filesystem memory artifacts such as Markdown logs, JSON mesh/checkpoint files, and operational command guidance.]

## Skill Version(s):

4.0.0 (source: server release metadata)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

LICENSE

MIT License

Copyright (c) 2026 MeshMorize contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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