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MemSync Dual Memory System

Logs all OpenClaw conversations and events with role tags, saving to JSONL and Memvid for full context search and monthly sharded or single-file storage.

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Logs all OpenClaw conversations and events with role tags, saving to JSONL and Memvid for full context search and monthly sharded or single-file storage.

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: license.txt, TEMPLATE.md, README-clawhub.md, instructions.md, README.md, SKILL.md

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Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.2.6

Provenance

Publisher: stackBlock
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 34 sections Open source page

Unified Conversation Logger v1.2.5

Version: 1.2.5 (Critical Fixes Edition) Author: stackBlock License: MIT OpenClaw: >= 2026.2.12 A dual-output conversation logger for OpenClaw that captures everything - user messages, assistant responses, sub-agent conversations, tool calls, and system events - to both JSONL (backup) and Memvid (semantic search) formats. Memvid: A single-file memory layer for AI agents with instant retrieval and long-term memory. Persistent, versioned, and portable memory, without databases. "Replace complex RAG pipelines with a single portable file you own, and give your agent instant retrieval and long-term memory."

⚠️ Security & Privacy Notice

Before installing, please understand: This skill captures everything - by design. It logs all user messages, assistant responses, sub-agent conversations, tool outputs, and system events to local files. This enables powerful long-term memory but requires trust. What you should know: Broad capture scope: This is intentional - the skill's purpose is complete conversation logging Sensitive data risk: Tool outputs (commands, API responses, file contents) are logged. Review what tools expose. Continuous logging: Once installed, it runs automatically on every assistant response until removed Optional cloud mode: API mode with MEMVID_API_KEY sends data to memvid.com (third-party service). Free/local modes keep data on your machine only. Your responsibility: Secure the JSONL/.mv2 files, rotate logs regularly, and audit what gets captured. Mitigations available: Use Free/Sharding mode to keep data local (no API key needed) Change default paths to encrypted locations Review tools/log.py before installing to understand exactly what gets logged File permissions: restrict access to log files (chmod 600) This skill is for users who want complete conversation memory and accept the privacy trade-offs.

✨ What Makes This Different

📝 Dual Storage - Every message saved to JSONL + Memvid simultaneously 🔍 Semantic Search - Ask "What did the researcher agent find about Tesla?" not just keyword search 🤖 Full Context - Captures user input, assistant output, agent chatter, tool results 💾 Three Modes - API (unlimited), Free (50MB), or Sharding (multi-file) 🚀 Always On - Hooks into OpenClaw automatically

Critical Fixes

Memvid Tag Format Fixed: Updated to KEY=VALUE format for Memvid 2.0+ compatibility Old (broken): --tag "user,telegram" New (fixed): --tag "role=user" --tag "source=telegram" Environment Variable Documentation: Added /etc/environment instructions (.bashrc doesn't work for background services) Hook Handler Format: Documented JavaScript (.js) requirement for OpenClaw 2026.2.12+ Comprehensive Troubleshooting: Added detailed troubleshooting section for common setup issues

Compatibility

Verified with OpenClaw 2026.2.12 Verified with Memvid CLI 2.0+

v1.2.4

Neural Search Default: Updated search guidance to use --mode neural as default for maximum accuracy Performance Documentation: Clarified latency trade-offs (~200ms for neural vs ~8ms for lexical) Search Mode Policy: Recommends neural for semantic understanding, lexical only when speed is critical

v1.2.3

Version Cohesion: All files synchronized to v1.2.3 Documentation Consistency: README and SKILL.md now have matching content Security Improvements: Generic paths (no hardcoded user directories), install script asks permission Registry Compliance: Complete metadata (env vars, credentials, warnings) for ClawHub transparency Privacy Documentation: Comprehensive Security & Privacy Notice explaining data capture scope Role Tagging: Distinguishes user, assistant, agent:*, system, and tool messages Full Context: Captures sub-agent chatter, tool results, background processes Three Storage Modes: API mode (single file), Free mode (50MB), Sharding mode (monthly rotation) Semantic Search: Ask "What did the researcher agent find?" or "What did I say about X?"

Option 1: API Mode (Recommended) - Near Limitless Memory

Best for: Heavy users, unified search across everything Cost: $59-299/month via memvid.com # 1. Get API key from memvid.com ($59/month for 1GB, $299 for 25GB) export MEMVID_API_KEY="your_api_key_here" export MEMVID_MODE="single" # 2. Install npm install -g memvid git clone https://github.com/stackBlock/openclaw-memvid-logger.git cp -r openclaw-memvid-logger ~/.openclaw/workspace/skills/ # 3. Create unified memory file memvid create ~/memory.mv2 # 4. Start OpenClaw - everything logs to one searchable file Search everything at once: memvid ask memory.mv2 "What did we discuss about BadjAI?" memvid ask memory.mv2 "What did the researcher agent find about Tesla?" memvid ask memory.mv2 "Show me all the Python scripts I asked for"

Option 2: Free Mode (50MB Limit) - Complete Memory in One Place

Best for: Testing, light usage, single searchable file Cost: FREE # 1. Install (no API key needed) npm install -g memvid git clone https://github.com/stackBlock/openclaw-memvid-logger.git cp -r openclaw-memvid-logger ~/.openclaw/workspace/skills/ export MEMVID_MODE="single" # 2. Create memory file memvid create ~/memory.mv2 # 3. Start OpenClaw ⚠️ Limit: 50MB (~5,000 conversation turns). When you hit it: Archive and start fresh, OR Upgrade to API mode ($59-299/month), OR Switch to Sharding mode

Option 3: Sharding Mode - More Than 50MB, Free Forever

Best for: Long-term use, staying under free tier Cost: FREE Trade-off: Multi-file search # 1. Install (no API key needed) npm install -g memvid git clone https://github.com/stackBlock/openclaw-memvid-logger.git cp -r openclaw-memvid-logger ~/.openclaw/workspace/skills/ export MEMVID_MODE="monthly" # This is the default # 2. Start OpenClaw - auto-creates monthly files How it works: memory_2026-02.mv2 (February) memory_2026-03.mv2 (March - auto-created) Each file stays under 50MB ⚠️ Sharding Search Differences: Single-file search (API/Free modes): # One search gets everything memvid ask memory.mv2 "What car did I decide to buy?" # Returns: Results from ALL conversations across ALL time Sharding search (requires multiple queries): # Must search each month separately memvid ask memory_2026-02.mv2 "car decision" # Recent memvid ask memory_2026-01.mv2 "car decision" # January # Or use a wrapper script to search all files for file in memory_*.mv2; do echo "=== $file ===" memvid ask "$file" "car decision" 2>/dev/null | head -5 done # You must know which month the conversation happened # No cross-month context - "compare this month to last month" won't work Why sharding is harder: Can't ask "what did we discuss in the past 3 months?" in one query No unified timeline across months Must remember which month you talked about what No cross-file semantic comparison

Role Tags (Automatic)

RoleTagExample SearchUser[user]"What did I say about Mercedes?"Assistant[assistant]"What did you recommend?"Sub-agents[agent:researcher], [agent:coder]"What did the researcher find?"System[system]"When did the cron job run?"Tools[tool:exec], [tool:browser]"What commands were run?"

Everything Captured

✅ User messages (what you type) ✅ Assistant responses (what I say back) ✅ Sub-agent conversations (researcher, coder, vision, math, etc.) ✅ Tool executions (bash commands, browser actions, file edits) ✅ Background processes (cron jobs, heartbeats, scheduled tasks) ✅ System events (config changes, restarts, errors)

Architecture

┌─────────────────────────────────────────┐ │ OpenClaw Ecosystem │ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ │ │ User │ │Assistant│ │ Agents │ │ │ │ Messages│ │Responses│ │Research │ │ │ └────┬────┘ └────┬────┘ └────┬────┘ │ │ └─────────────┴─────────────┘ │ │ │ │ │ ┌──────▼──────┐ │ │ │ log.py │ │ │ │ (this skill)│ │ │ └──────┬──────┘ │ └─────────────────────┼───────────────────┘ │ ┌─────────────────┼─────────────────┐ ↓ ↓ ↓ ┌───────┐ ┌─────────────┐ ┌──────────┐ │ JSONL │ │ Memvid │ │ Search │ │ File │ │ Files │ │ Query │ └───────┘ └─────────────┘ └──────────┘ │ │ ↓ ↓ grep/jq memvid ask/find

Natural Language Search

# What did you say about...? memvid ask memory_2026-02.mv2 "What was your recommendation about the Mercedes vs Tesla?" # What did I ask for...? memvid ask memory_2026-02.mv2 "What Python scripts did I request last week?" # What did agents do...? memvid ask memory_2026-02.mv2 "What did the researcher agent find about options trading?" # System events...? memvid ask memory_2026-02.mv2 "When did the PowerSchool grades cron job run?"

Keyword Search

# Find specific terms memvid find memory_2026-02.mv2 --query "Mercedes" # With filters memvid find memory_2026-02.mv2 --query "script" --tag agent:coder

Temporal Queries

memvid when memory_2026-02.mv2 "yesterday" memvid when memory_2026-02.mv2 "last Tuesday" memvid when memory_2026-02.mv2 "3 days ago"

⚡ Search Performance Guide

Memvid has three search modes. This skill uses --mode neural by default for maximum accuracy:

Default: Neural Search (Recommended)

# Always use neural for semantic understanding and context memvid ask memory.mv2 "What supplements did Dr. Sinclair recommend?" --mode neural memvid ask memory.mv2 "What did we discuss about BadjAI?" --mode neural memvid ask memory.mv2 "Show me the Python scripts I requested" --mode neural Speed: ~200ms | Best for: Semantic understanding, context, synonyms, conceptual relationships

Alternative Modes (Use When Explicitly Requested)

Mode 1: Lexical Search (Fastest) # Use only for exact keyword matching when speed is critical memvid find memory.mv2 --mode lex --query "metformin" Speed: ~8ms | Use when: Exact word matching needed, latency is critical Mode 2: Hybrid Search (Balanced) # Combines lexical + neural memvid find memory.mv2 --mode hybrid --query "diabetes medications" Speed: ~300-500ms | Use when: You want both exact matches and semantic similarity

Why Neural as Default?

ModeSpeedAccuracyUse Caseneural~200msHighestDefault - semantic understandinglex~8msKeyword onlySpeed-critical exact matcheshybrid~300-500msHighBalanced approach The ~200ms trade-off is worth it: Neural mode understands context, handles paraphrases, and finds conceptually related information that lexical search misses entirely.

JSONL Backup

# Quick grep grep "Mercedes" conversation_log.jsonl # Complex queries with jq jq 'select(.role_tag == "user" and .content | contains("Python"))' conversation_log.jsonl # Time range jq 'select(.timestamp >= "2026-02-01" and .timestamp < "2026-03-01")' conversation_log.jsonl

Environment Variables

VariableDefaultModeDescriptionMEMVID_API_KEY(none)APIYour memvid.com API keyMEMVID_MODEmonthlyAllsingle or monthlyJSONL_LOG_PATH~/workspace/conversation_log.jsonlAllBackup log fileMEMVID_PATH~/workspace/memory.mv2AllBase path for memory filesMEMVID_BIN~/.npm-global/bin/memvidAllPath to memvid CLI

OpenClaw Hooks (Advanced)

Add to openclaw.json: { "hooks": { "internal": { "enabled": true, "entries": { "conversation-logger": { "enabled": true, "command": "python3 ~/.openclaw/workspace/skills/unified-logger/tools/log.py" } } } } }

Mode 1: Single File (API or Free Mode)

memory.mv2 ├── [user] messages ├── [assistant] responses ├── [agent:researcher] findings ├── [agent:coder] code ├── [tool:exec] commands └── [system] events

Mode 2: Sharding (Monthly Rotation)

memory_2026-01.mv2 (January conversations) memory_2026-02.mv2 (February conversations) ← Current memory_2026-03.mv2 (March, auto-created on March 1)

"Free tier limit exceeded" (Free Mode)

# Option 1: Archive and start fresh mv memory.mv2 memory_archive.mv2 memvid create memory.mv2 # Option 2: Switch to monthly sharding export MEMVID_MODE="monthly" # Option 3: Get API key export MEMVID_API_KEY="your_key" # $59-299/month at memvid.com

"Cannot find memory file" (Sharding Mode)

Current month's file auto-creates. If missing: memvid create memory_$(date +%Y-%m).mv2

Missing agent conversations

Agents log to their own sessions. Ensure skill is installed in main agent workspace and sub-agents inherit it.

Search returns wrong speaker

Memvid uses semantic search. Be specific: ❌ "Mercedes" → Returns all mentions ✅ "What did I say about Mercedes" → Targets [user] frames ✅ "Your recommendation about Mercedes" → Targets [assistant] frames

Comparing the Three Modes

FeatureAPI ModeFree ModeSharding ModeCost$59-299/moFREEFREECapacity1-25GB+50MBUnlimited (files)Files11Multiple (monthly)Unified Search✅ Yes✅ Yes❌ Per-file onlyCross-Context Search✅ Full history✅ Full history❌ Month isolatedBest ForPower usersTestingLong-term free useComplexitySimpleSimpleMust track files

💸 The Pricing Gap (AKA Why Sharding Exists)

The situation: Memvid's pricing goes from $0 (50MB) straight to $59/month (25GB). The problem: That's like buying a Ferrari when you just need a Honda Civic for your commute. What we're doing about it: I reached out. While they consider it, Sharding Mode exists so you don't have to pay Ferrari prices for Honda Civic usage. You can help: If you also think $0 → $59 is a bit much, reach out to Memvid at memvid.com and tell them stackBlock sent you. The more voices, the faster we get that $10-20 middle tier for the rest of us. Until then: Sharding Mode. Because startups shouldn't have to choose between ramen and memory. 🍜

Future Enhancements

Auto-archive old months to cold storage Web UI for browsing conversations Cross-file search wrapper script Export to other formats (Markdown, PDF) Conversation threading visualization

Support

GitHub Issues: github.com/stackBlock/openclaw-memvid-logger OpenClaw Discord: discord.com/invite/clawd Memvid Support: memvid.com/docs

License

MIT - See LICENSE About Memvid: Memvid is a single-file memory layer for AI agents with instant retrieval and long-term memory. Persistent, versioned, and portable memory, without databases. Replace complex RAG pipelines with a single portable file you own, and give your agent instant retrieval and long-term memory.

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

5 Docs1 Files

SKILL.md Primary doc
instructions.md Docs
README-clawhub.md Docs
README.md Docs
TEMPLATE.md Docs
license.txt Files