Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
OpenClaw MongoDB Semantic Memory
MongoDB-backed long-term semantic memory for recalling, storing, searching, and managing facts, decisions, and user preferences across sessions.
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MongoDB-backed long-term semantic memory for recalling, storing, searching, and managing facts, decisions, and user preferences across sessions.
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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
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.
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.2.1
Provenance
- Publisher
- mrlynn
- Source page
- View original listing
- Canonical URL
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Documentation
OpenClaw Memory โ Agent Skill
MongoDB-backed long-term memory with Voyage AI semantic search
When to Use
Use OpenClaw Memory when: โ You need to recall prior conversations, decisions, or preferences โ Building context across multiple sessions โ Tracking facts, insights, or learnings over time โ Searching for relevant information semantically (not just keywords) โ Remembering user preferences, project details, or domain knowledge NOT for: โ Immediate/short-term context (use conversation history instead) โ Temporary scratch notes (use files in workspace) โ Large document storage (use file system or database)
memory_search
Semantically search long-term memory. Use this to recall prior decisions, preferences, context, or facts. memory_search({ query: "What did we decide about the database schema?", maxResults: 6 // optional, default: 6 }) Returns: Array of memories with similarity scores, text, tags, and metadata. When to use: Before answering questions about past work When user asks "remember when..." or "what did we say about..." To check for existing context before making new decisions When solving similar problems to past ones Example output: { "results": [ { "id": "507f1f77bcf86cd799439011", "text": "Decided to use MongoDB for vector storage with Atlas Search", "score": 0.89, "tags": ["decision", "database"], "createdAt": "2026-02-20T14:30:00Z" } ] }
memory_remember
Store a fact, decision, preference, or important context in long-term memory. memory_remember({ text: "User prefers TypeScript over JavaScript for new projects", tags: ["preference", "programming"], // optional ttl: 2592000 // optional, 30 days default }) Returns: Stored memory ID and confirmation. When to use: After important decisions are made When user states a preference ("I prefer X over Y") Key facts or insights discovered during work Context that should persist across sessions User explicitly asks you to remember something Best practices: Be specific and concise (1-2 sentences ideal) Include relevant tags for categorization Don't store temporary/ephemeral information Use structured format when possible (e.g., "Key: value")
memory_get
Read a specific memory file from the workspace. Use memory_search for semantic recall; use this for targeted file reads. memory_get({ path: "MEMORY.md", from: 1, // optional, starting line lines: 50 // optional, number of lines }) Returns: File contents (text). When to use: After memory_search to get full context Reading structured memory files (MEMORY.md, memory/YYYY-MM-DD.md) Targeted line-range reads for efficiency
memory_forget
Delete a specific memory by ID. Use memory_search first to find the memory ID. memory_forget({ memoryId: "507f1f77bcf86cd799439011" }) Returns: Confirmation or error. When to use: User explicitly asks to delete/forget something Correcting incorrect memories Removing outdated information Never use proactively without user request
memory_list
Browse stored memories by recency or tag. memory_list({ tags: "decision,database", // optional, comma-separated limit: 10, // optional, default: 10 sort: "desc" // optional, "desc" or "asc" }) Returns: Array of memories with metadata (no similarity scores). When to use: Browsing recent memories Filtering by specific tags Audit/review of stored memories When user asks "what have you remembered?"
memory_status
Check memory system health and stats. memory_status() Returns: Daemon status, MongoDB connection, Voyage AI status, total memories, uptime. When to use: Debugging memory system issues User asks about memory capacity or health Before relying on memory for critical tasks Rarely needed in normal operation
Configuration
Memory tools connect to a daemon at http://localhost:7654 by default. Configuration is set in ~/.openclaw/openclaw.json: { plugins: { entries: { "openclaw-memory": { enabled: true, config: { daemonUrl: "http://localhost:7654", agentId: "openclaw", maxResults: 6, minScore: 0.5, defaultTtl: 2592000 // 30 days } } } } }
Automatic Memory Capture
OpenClaw Memory includes lifecycle hooks that capture memories automatically:
auto-remember Hook
Fires after every agent response. Extracts facts, decisions, and preferences using pattern matching: "I prefer..." โ stored as preference "We decided..." โ stored as decision "Remember that..." โ stored as fact "Key: value" patterns (structured data) Limits: Max 5 extractions per message, min 10 chars, deduplicates.
session-to-memory Hook
Fires when starting a new session. Summarizes the ending session and stores it as a searchable memory.
memory-bootstrap Hook
Fires on agent startup. Queries for relevant memories (preferences, recent decisions, pinned items) and injects them into context.
memory-enriched-tools Hook
Fires before tool results are saved. Appends related memories as context annotations to Read/Grep/Glob/Bash outputs. To disable hooks: Set hooksEnabled: false in plugin config.
Example 1: Recall Prior Decision
User asks: "What did we decide about the API authentication?" Agent response: Call memory_search({ query: "API authentication decision" }) Review results Answer based on stored memory If no results, say "I don't have any memory of that decision"
Example 2: Store Preference
User says: "I prefer Material UI over Tailwind for all React projects" Agent response: Acknowledge the preference Call memory_remember({ text: "User prefers Material UI over Tailwind for React projects", tags: ["preference", "ui"] }) Confirm it's stored: "Got it, I'll remember that preference"
Example 3: Check Before Recommending
User asks: "What CSS framework should we use?" Agent response: Call memory_search({ query: "CSS framework preference" }) If match found: "You previously preferred Material UI over Tailwind" If no match: Provide recommendation based on context
Example 4: Session Continuity
New session starts: memory-bootstrap hook auto-runs Loads recent preferences, decisions, project context Agent has continuity without user repeating everything
Tips & Best Practices
Do: โ Use memory_search before answering questions about past work โ Store concise, specific facts (1-2 sentences) โ Tag memories for easy filtering (preference, decision, fact, project-name) โ Trust semantic search (it understands meaning, not just keywords) โ Let hooks handle routine memory capture (preferences, decisions) Don't: โ Store temporary/ephemeral information โ Duplicate conversation history (that's already stored) โ Store sensitive credentials (use secure storage instead) โ Forget without user permission (use memory_forget sparingly) โ Overwhelm with too many manual memory_remember calls (hooks handle most) Search Tips: Use natural language: "database preference" > "db pref" Be specific when possible: "TypeScript vs JavaScript decision" > "language" Results are ranked by semantic similarity (0-1 score) Default minScore: 0.5 filters low-relevance results TTL Guidelines: 7 days: Temporary project context 30 days (default): Most facts, decisions, preferences 90 days: Important long-term context 365 days: Critical knowledge that should persist long-term
Troubleshooting
"Memory daemon not reachable" Check daemon is running: curl http://localhost:7654/health Start daemon: cd openclaw-memory && pnpm dev:daemon Or use Docker: docker compose up -d "No memories found" Verify memories exist: memory_list({ limit: 5 }) Check agentId matches (openclaw by default) Try broader search queries Lower minScore threshold in config "Memory search returns irrelevant results" Be more specific in query Increase minScore threshold (default: 0.5) Check tags to filter results Verify Voyage AI embeddings are working (not mock mode) "Tools not available" Verify plugin is enabled in openclaw.json Restart OpenClaw gateway Check plugin installation: openclaw plugins list
Web Dashboard
Full installation includes a web dashboard at http://localhost:3002: Memory browser with semantic search Graph visualizer (relationship mapping) Conflict resolution (contradiction detection) Timeline and analytics
Reflection Pipeline
9-stage processing pipeline for: Duplicate detection (0.92 similarity threshold) Contradiction detection (heuristic + LLM) Confidence scoring Graph relationship extraction Entity extraction Temporal decay Trigger reflection: curl -X POST http://localhost:7654/reflect \ -H "Content-Type: application/json" \ -d '{"agentId":"openclaw"}'
Graph Relationships
Memories can be connected via edges: SUPPORTS โ reinforces/supports another memory CONTRADICTS โ conflicts with another memory DERIVES_FROM โ built upon another memory CO_OCCURS โ frequently appears together PRECEDES โ temporal sequence MENTIONS_ENTITY โ references an entity Access via web dashboard at /graph.
Requirements
MongoDB 8.0+ (local or Atlas) Node.js 18+ OpenClaw CLI Optional: Voyage AI API key (mock mode available)
Installation
# Install plugin openclaw plugins install openclaw-memory # Start daemon cd openclaw-memory pnpm install && pnpm dev:daemon # Or use Docker docker compose up -d
Summary
OpenClaw Memory gives agents persistent, searchable memory across sessions: Search semantically with memory_search Store facts with memory_remember Automatic capture via lifecycle hooks MongoDB-backed with Voyage AI embeddings Web dashboard for visualization and management Use it to build agents that remember, learn, and improve over time. ๐ง Version: 0.2.1 Author: Michael Lynn License: MIT Repository: https://github.com/mrlynn/openclaw-mongodb-memory
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
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Package contents
Included in package
1 Docs
- SKILL.md