Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Persistent Memory
Three-layer persistent memory system (Markdown + ChromaDB vectors + NetworkX knowledge graph) for long-term agent recall across sessions. One-command setup w...
skill openclawclawhub Free
Three-layer persistent memory system (Markdown + ChromaDB vectors + NetworkX knowledge graph) for long-term agent recall across sessions. One-command setup w...
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0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/architecture.md, scripts/auto_retrieve.py, scripts/configure_openclaw.py, scripts/graph.py, scripts/indexer.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 3.0.0
Provenance
- Publisher
- Jakebot-ops
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Persistent Memory
Adds persistent three-layer memory to any OpenClaw workspace. The agent gains semantic recall across sessions β decisions, facts, lessons, and institutional knowledge survive restarts.
Architecture
LayerTechnologyPurposeL1: MarkdownMEMORY.md + daily logs + reference/Human-readable curated knowledgeL2: VectorChromaDB + all-MiniLM-L6-v2Semantic search across all memoriesL3: GraphNetworkXRelationship traversal between concepts All three layers sync together. The indexer updates L2 and L3 from L1 automatically.
β οΈ Critical Integration: OpenClaw Memory Configuration
Problem: OpenClaw has its own built-in memory search system, but by default it only indexes MEMORY.md and memory/*.md files. Critical workspace files like SOUL.md (agent directives), AGENTS.md (behavior rules), and PROJECTS.md (active work) are ignored. Impact: Agents can violate explicit directives because they're not found in memory searches. This causes operational failures where agents ignore their own rules. Solution: The configure_openclaw.py script adds a memorySearch configuration block to OpenClaw that indexes all critical workspace files. This makes directive compliance automatic rather than optional.
Setup
One command from workspace root: bash skills/persistent-memory/scripts/unified_setup.sh This automatically: β Creates 3-layer memory system (Markdown + Vector + Graph) β Installs all Python dependencies (ChromaDB, NetworkX, sentence-transformers) β Configures OpenClaw memorySearch integration (directive compliance) β Indexes existing MEMORY.md if present β Sets up daily maintenance automation No manual configuration needed. The script handles everything including OpenClaw integration that prevents agents from ignoring workspace directives (SOUL.md, AGENTS.md, etc.).
Writing Memories
MEMORY.md β Curated long-term knowledge (decisions, architecture, lessons learned). Update after significant events. memory/YYYY-MM-DD.md β Daily logs. Raw notes of what happened each day. reference/*.md β Institutional facts (people, repos, infrastructure, business rules). The agent's encyclopedia.
Indexing (after editing any memory file)
vector_memory/venv/bin/python vector_memory/indexer.py The indexer parses MEMORY.md, reference/.md, and memory/.md into vector embeddings and rebuilds the knowledge graph. Run after every edit to keep layers in sync.
Searching
vector_memory/venv/bin/python vector_memory/search.py "your query" Returns top-3 semantically similar chunks with source file and section.
Sync Status Check
vector_memory/venv/bin/python vector_memory/auto_retrieve.py --status Reports sync health: MEMORY.md hash vs indexed state, chunk count, graph size. Use in heartbeats to detect drift.
Agent Behavior Rules
Add these to AGENTS.md or SOUL.md:
Pre-Response (mandatory)
Before answering questions about prior work, decisions, dates, people, or preferences β search memory first. Use memory_search or run auto_retrieve.py with the query. Never say "I don't remember" without checking. CRITICAL: OpenClaw's built-in memory search should now automatically find directive files (SOUL.md, AGENTS.md) if configure_openclaw.py was run. If memory searches are not finding agent rules or workspace directives, the OpenClaw integration is missing or broken.
Pre-Action (mandatory)
Before executing any action that references an external identifier (URL, handle, email, repo name, address) β query reference/ files for the exact value. If not found, query vector memory. If still not found, ask the user. Never fabricate identifiers.
Post-Edit (mandatory)
After editing MEMORY.md or any file in reference/ or memory/ β re-index: vector_memory/venv/bin/python vector_memory/indexer.py
Heartbeat Integration
Add to HEARTBEAT.md: ## Memory Sync Check Run `vector_memory/venv/bin/python vector_memory/auto_retrieve.py --status` and if status is OUT_OF_SYNC, re-index with `vector_memory/venv/bin/python vector_memory/indexer.py`.
Reference Directory (Optional but Recommended)
Create reference/ in the workspace root as the agent's institutional knowledge base: reference/ βββ people.md β Contacts, roles, communication details βββ repos.md β GitHub repositories, URLs, status βββ infrastructure.md β Hosts, IPs, ports, services βββ business.md β Company info, strategies, rules βββ properties.md β Domain-specific entities (deals, products, etc.) These files are vector-indexed alongside MEMORY.md. The agent queries them before any action involving external identifiers. Facts accumulate over time β the agent that never forgets.
File Structure After Setup
workspace/ βββ MEMORY.md β Curated long-term memory (L1) βββ memory/ β βββ 2026-02-17.md β Daily log β βββ heartbeat-state.json β Sync tracking βββ reference/ β Institutional knowledge (optional) β βββ people.md β βββ ... βββ vector_memory/ βββ indexer.py β Index all markdown into vectors + graph βββ search.py β Semantic search CLI βββ graph.py β NetworkX knowledge graph βββ auto_retrieve.py β Status checker + auto-retrieval βββ chroma_db/ β Vector database (gitignored) βββ memory_graph.json β Knowledge graph (auto-generated) βββ venv/ β Python venv (gitignored)
Troubleshooting
"No module named chromadb" β Run setup.sh again or activate the venv: source vector_memory/venv/bin/activate OUT_OF_SYNC status β Run the indexer: vector_memory/venv/bin/python vector_memory/indexer.py Empty search results β Check that MEMORY.md has content and the indexer has been run at least once SIGSEGV on indexing β Usually caused by incompatible ML libs. The setup script pins known-good versions. Agent ignoring SOUL.md/AGENTS.md directives β OpenClaw integration missing. Run python skills/persistent-memory/scripts/configure_openclaw.py to fix. Memory searches not finding workspace files β Check OpenClaw configuration: openclaw config get | grep memorySearch "Configuration verification failed" β Restart OpenClaw manually: openclaw gateway restart
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Scripts2 Docs
- SKILL.md
- references/architecture.md
- scripts/auto_retrieve.py
- scripts/configure_openclaw.py
- scripts/graph.py
- scripts/indexer.py