Imported from EngramMemory/engram-memory (SKILL.md). Install upstream with npx skills add EngramMemory/engram-memory. Copyright stays with the author.

Engram for OpenClaw Agents

Persistent semantic memory that makes your agent remember across sessions.

What This Provides

Instead of starting fresh every session, your agent will:

• Remember your preferences, facts, and past decisions
• Automatically recall relevant context for new conversations
• Search through stored memories semantically
• Categorize and organize knowledge by type

Quick Start

# Install the skill
clawhub install engrammemory

# Setup (requires Docker — deploys Qdrant + FastEmbed)
bash scripts/setup.sh

# Store a memory
memory_store "I prefer direct communication style" --category preference

# Search memories
memory_search "communication preferences"

Core Functions

memory_store(text, category, importance)

Save information to long-term memory with semantic embedding.

# Save preferences
memory_store("User prefers TypeScript over JavaScript for new projects", 
            category="preference", importance=0.8)

# Save facts  
memory_store("Database migration completed on 2024-03-15, moved from SQLite to PostgreSQL",
            category="fact", importance=0.7)

# Save decisions
memory_store("Decided to use React Query for state management in the frontend",
            category="decision", importance=0.9)

memory_recall(query, limit, category)

Search stored memories using semantic similarity.

# Find relevant memories
memory_recall("database migration")
memory_recall("frontend preferences", category="preference")
memory_recall("recent decisions", limit=10)

memory_profile(action, key, value, category)

Manage user profile data (static preferences + dynamic context).

# View profile
memory_profile("view")

# Add static preference  
memory_profile("add", "communication_style", "Direct, no fluff", "static")

# Add dynamic context
memory_profile("add", "current_project", "Building memory system", "dynamic")

memory_forget(query, memory_id)

Remove memories by search or specific ID.

memory_forget("old project requirements")
memory_forget(memory_id="uuid-string")  

Memory Categories

• preference — User preferences, communication style, technical choices
• fact — Objective information, system states, completed work
• decision — Important decisions made, rationale, outcomes
• entity — People, projects, organizations, relationships
• other — Miscellaneous information that doesn't fit above

Context System

Engram includes a context management system that gives your agent structured knowledge about any codebase. Initialize a project, and your agent can search and query its architecture, patterns, and APIs.

Context Commands

engram-context — Core Management

# Initialize context for a project
engram-context init /path/to/project --template web-app
engram-context init /path/to/project --template python-api
engram-context init /path/to/project --template generic

# Build search index
engram-context index

# Search context files
engram-context find "authentication patterns"

# Check status
engram-context status

engram-ask — Natural Language Queries

engram-ask "How does authentication work?"
engram-ask "Where are the API endpoints defined?"
engram-ask interactive

engram-semantic — Embedding-Based Search

engram-semantic find "user login process"
engram-semantic index
engram-semantic status

Project Templates

Context Structure

Each project gets a .context/ directory:

.context/
├── metadata.yaml       # Project configuration
├── architecture.md     # System architecture
├── patterns.md         # Code patterns and standards
├── apis.md             # API documentation
├── development.md      # Development workflows
├── troubleshooting.md  # Common issues and solutions
└── index.db            # Search index (auto-generated)

All context queries are scoped to the current project.

Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   OpenClaw      │    │  FastEmbed      │    │     Qdrant      │
│   Agent         │───▶│  nomic-embed    │───▶│  Vector Store   │
│                 │    │  text-v1.5      │    │  Port 6333      │
└─────────────────┘    └─────────────────┘    └─────────────────┘

Components:

• Qdrant — Vector database for semantic storage/search
• FastEmbed — Local embedding model (nomic-embed-text-v1.5)
• Plugin — OpenClaw integration with auto-recall and capture

Installation

Prerequisites

• OpenClaw 2026.3.13+
• Docker
• 4GB+ RAM for embedding model
• 10GB+ storage for vector database

Automated Setup

# 1. Install the skill
clawhub install engrammemory

# 2. Run setup script  
cd ~/.openclaw/workspace/skills/engrammemory
bash scripts/setup.sh

# 3. Follow the configuration prompts
# (The script will generate the exact config to add to openclaw.json)

# 4. Restart OpenClaw gateway
openclaw gateway restart

Manual Setup

If you prefer manual installation or need custom configuration:

1. Deploy Engram (one container — Qdrant + FastEmbed + MCP bundled):

   docker run -d --name engram-memory --restart unless-stopped \
     -p 6333:6333 -p 11435:11435 -p 8585:8585 \
     -v engram_data:/data \
     engrammemory/engram-memory:latest

2. Configure OpenClaw plugin: Add to ~/.openclaw/openclaw.json:

   {
     "plugins": {
       "allow": ["engram"],
       "slots": {
         "memory": "engram"
       },
       "entries": {
         "engram": {
           "enabled": true,
           "config": {
             "qdrantUrl": "http://localhost:6333",
             "embeddingModel": "nomic-ai/nomic-embed-text-v1.5",
             "collection": "agent-memory", 
             "autoRecall": true,
             "autoCapture": true,
             "maxRecallResults": 5,
             "minRecallScore": 0.35,
             "embeddingUrl": "http://localhost:11435"
           }
         }
       }
     }
   }

3. Restart gateway:

   openclaw gateway restart

Configuration Options

Example Configuration

{
  "qdrantUrl": "http://localhost:6333",
  "embeddingUrl": "http://localhost:11435",
  "collection": "agent-memory",
  "autoRecall": true,
  "autoCapture": true
}

Multi-Agent Setup

For multiple agents sharing memory:

{
  "agents": {
    "list": [
      {
        "id": "main",
        "plugins": {
          "memory": {
            "collection": "main-agent-memory"
          }
        }
      },
      {
        "id": "coding-assistant", 
        "plugins": {
          "memory": {
            "collection": "coding-agent-memory"
          }
        }
      }
    ]
  }
}

Usage Examples

Research Assistant

# Save research findings
memory_store("Found that React 18 concurrent rendering improves performance by 15-30% for large lists", 
            category="fact", importance=0.8)

# Later, when discussing performance:
memories = memory_recall("React performance improvements")
# Auto-recalls the research finding

Project Manager

# Save project decisions
memory_store("Team decided to use PostgreSQL over MongoDB for better ACID compliance in financial app",
            category="decision", importance=0.9)

# Track preferences
memory_profile("add", "deployment_preference", "Docker with Kubernetes", "static")

# Later project planning auto-recalls relevant decisions and preferences

Customer Support

# Remember customer preferences
memory_store("Customer prefers email over phone calls for non-urgent issues",
            category="preference", importance=0.7)

# Track issue patterns
memory_store("Billing module API timeout issue affects 15% of enterprise customers",
            category="fact", importance=0.8)

Backup and Migration

Export Memory

# Export all memories as JSON
curl "http://localhost:6333/collections/agent-memory/points/scroll" \
  -H "Content-Type: application/json" \
  -d '{"limit": 10000}' > memory_backup.json

Import Memory

# Import memories from backup
# Re-import using memory_store
python scripts/memory_store.py --json '{"text": "...", "category": "fact"}'

Performance Tuning

Vector Quantization (New Feature)

Engram now includes automatic scalar quantization for 4x memory reduction with no recall loss:

• Memory Usage: Reduces vector storage by ~75% (32-bit → 8-bit per dimension)
• Search Speed: Unchanged (quantized vectors stay in RAM for fast search)
• Quality: No degradation (99th percentile quantile preserves accuracy)
• Automatic: Enabled by default in new installations

# Quantization is automatically applied when you run:
bash scripts/setup.sh

Technical Details:

• Uses int8 scalar quantization with 99th percentile
• Compresses 768-dimension vectors from ~3KB to ~768 bytes each
• Enables storing 4x more memories in the same RAM
• Fully compatible with existing memory collections

For Large Memory Sets (10K+ memories)

{
  "config": {
    "maxRecallResults": 3,
    "minRecallScore": 0.45,
    "autoCapture": false
  }
}

For High-Frequency Agents

{
  "config": {
    "profileFrequency": 50,
    "autoRecall": false
  }
}

Troubleshooting

Common Issues

Memory not persisting:

• Check Qdrant is running: curl http://localhost:6333/collections
• Verify plugin config in openclaw status

Poor recall quality:

• Lower minRecallScore (try 0.25)
• Check embedding model is loaded: curl http://localhost:11435/models

High memory usage:

• Increase Docker memory limits
• Reduce maxRecallResults
• Enable auto-cleanup in config

Debug Mode

{
  "config": {
    "debug": true
  }
}

Enables detailed logging for memory operations.

Advanced Usage

Custom Categories

memory_store("Customer uses advanced React patterns", category="customer_tech_profile")
memory_recall("customer tech preferences", category="customer_tech_profile")

Importance-Based Filtering

# Only recall highly important memories  
memory_recall("project decisions", min_importance=0.8)

Time-Based Queries

# Recent memories (last 30 days)
memory_recall("recent changes", days_back=30)

Security Considerations

• Local-first: All data stays on your infrastructure
• No external APIs: Embeddings generated locally
• Encryption: Use encrypted storage for sensitive data
• Access control: Configure Qdrant authentication if needed

Contributing

Found a bug or want to add features?

• GitHub: engram-memory
• Issues: Report bugs and feature requests
• Docs: Help improve documentation
• Examples: Share usage patterns

License

MIT License - Use freely in personal and commercial projects.

Transform your agent from stateless to stateful. Install Engram today.

OpenClaw Integration

🟢 Fully Integrated - Engram is now available as native OpenClaw tools.

Available Tools

• memory_search - Search memories using semantic similarity
• memory_store - Store text with embeddings in long-term memory

How to Use

Once Engram is set up (FastEmbed service + Qdrant running), these tools are automatically available in OpenClaw sessions:

// Search stored memories
memory_search("FastEmbed integration status", 10, 0.3)

// Store new memories
memory_store("User prefers detailed explanations", "preference", 0.8)

Implementation

• Plugin Type: Native Python plugin
• Backend: FastEmbed (localhost:11435) + Qdrant (localhost:6333)
• No MCP Server Required: Direct integration through OpenClaw's plugin system

See OPENCLAW_INTEGRATION.md for complete technical details.