---

name: memori-extension description: Memory augmentation and LLM call interception using the Memori Python library with optional Zhipu API integration. metadata: { "openclaw": { "emoji": "🧠", "user-invocable": false, "disable-model-invocation": false, "requires": { "pip": ["memori"], "anyBins": ["python3"], "env": { "optional": ["MEMORI_TECH_TERMS", "MEMORI_TECH_TERMS_FILE", "ZHIPUAI_API_KEY", "ZHIPUAI_MODEL", "MEMORI_DB_PATH"] } } } }

Memori Extension Skill

Memory augmentation and LLM call interception using the Memori Python library with optional Zhipu AI API integration.

Overview

This skill provides memory augmentation and LLM call interception capabilities using the Memori Python library. It enables agents to retrieve relevant knowledge from a memory database and inject it into conversations.

Security & Privacy Notice

⚠️ Important: This skill performs the following operations:

File Operations (Always Enabled)

• Read/Write: Local SQLite database (default: ./memori.db)
• Read/Write: Optional tech terms configuration file (default: ./config/tech_terms.txt)
• These operations are entirely local and do not transmit data externally.

External API Calls (Optional - Requires Explicit Configuration)

• Condition: Only if ZHIPUAI_API_KEY environment variable is set
• Data Transmitted: Conversation text (user messages, system prompts, and assistant responses) is sent to Zhipu AI's servers for analysis and augmentation
• Purpose: To enhance conversation understanding using Zhipu AI's language models
• Control: If ZHIPUAI_API_KEY is NOT set, the skill operates 100% locally with NO external network calls

Recommendations

1. Start Local-Only: Test the skill without ZHIPUAI_API_KEY first to verify local functionality
2. Explicit Consent: Only set ZHIPUAI_API_KEY if you explicitly consent to sending conversation data to external services
3. Review Data: Consider what conversation data you're comfortable transmitting before enabling external API features
4. Sandbox Testing: If providing API keys, test in a sandbox environment first to understand the data flow

Dependencies

This skill requires the following Python packages:

pip install memori

The memori library is licensed under Apache License Version 2.0.

Optional Dependencies

For Zhipu API augmentation (optional):

pip install zhipuai

Warning: Installing zhipuai and providing ZHIPUAI_API_KEY enables conversation content to be sent to external servers.

Environment Variables

All Supported Environment Variables

Variable	Description	Required	Default	Privacy Note
ZHIPUAI_API_KEY	Zhipu AI API key for conversation augmentation	No	-	⚠️ Enables external API calls - conversation text will be sent to Zhipu AI servers
ZHIPUAI_MODEL	Zhipu AI model name	No	glm-4.7	Only used if ZHIPUAI_API_KEY is set
MEMORI_TECH_TERMS	Comma-separated technical terms for LLM interception	No	-	Local only
MEMORI_TECH_TERMS_FILE	Path to file containing technical terms (one per line)	No	./config/tech_terms.txt	Local only - read/write
MEMORI_DB_PATH	Path to Memori database	No	./memori.db	Local only - read/write

Privacy & Data Flow Notes:

• ⚠️ External API: Only ZHIPUAI_API_KEY enables external network calls. All other variables control local file operations.
• ✅ Local-Only Mode: If you omit ZHIPUAI_API_KEY, the skill operates 100% locally with no external data transmission.
• 📁 File Operations: The skill reads/writes the database (memori.db) and optionally the tech terms file (tech_terms.txt). These are stored on your local filesystem.
• 🔒 Best Practice: Start without ZHIPUAI_API_KEY to test local functionality. Only enable external API if you need enhanced features and consent to the data transmission.

Configuration Examples

System environment:

# Optional: Enable Zhipu API augmentation
export ZHIPUAI_API_KEY="your-api-key"
export ZHIPUAI_MODEL="glm-4.7"

# Optional: Customize technical terms
export MEMORI_TECH_TERMS="FFI,Rust,Linux,kernel,spinlock"

# Optional: Use custom database path
export MEMORI_DB_PATH="/path/to/memori.db"

OpenClaw configuration (openclaw.json):

{
  "skills": {
    "entries": {
      "memori-extension": {
        "enabled": true,
        "env": {
          "ZHIPUAI_API_KEY": "your-api-key",
          "ZHIPUAI_MODEL": "glm-4.7",
          "MEMORI_TECH_TERMS": "FFI,Rust,Linux,kernel",
          "MEMORI_DB_PATH": "./memori.db"
        }
      }
    }
  }
}

Technical terms file (optional):

# Create config directory
mkdir -p config

# Create terms file
cat > config/tech_terms.txt << EOF
FFI
Rust
Linux
kernel
spinlock
mutex
unsafe
EOF

# Set environment variable
export MEMORI_TECH_TERMS_FILE="config/tech_terms.txt"

Quick Start

Method 1: Direct Memori Library (Recommended)

from memori import Memori

memori = Memori(
    db_path="memori.db",
    entity_id="knowledge-base"
)

# Search memories
memories = memori.search("query", limit=5)

# Augment query
context = memori.augment("How to handle spinlock conflicts?", limit=3)

# Store memory
memory_id = memori.store("New content")

# Get stats
stats = memori.get_stats()

# Close
memori.close()

Method 2: Skill Convenience API

from skills.memori_extension import search, augment, intercept_llm

# Search
memories = search("FFI bindings", limit=5)

# Augment
enhanced = augment("How to handle spinlock conflicts?")
if enhanced:
    print(enhanced)

# Intercept LLM
messages = [{"role": "user", "content": "FFI question"}]
enhanced = intercept_llm(messages)

API Reference

Memori Class

__init__(db_path, entity_id)

Initialize Memori instance.

Parameters:

• db_path (str | Path, optional): Database path
• entity_id (str, optional): Entity ID, default "default"

search(query, limit, entity_id)

Search for relevant memories.

Returns: List[Memory]

augment(query, limit, entity_id)

Augment query with retrieved memories.

Returns: AugmentedContext

store(content, entity_id, metadata)

Store a new memory.

Returns: int - Memory ID

get_stats(entity_id)

Get statistics.

Returns: dict

close()

Close database connection.

Memory Class

Memory object with attributes:

• id (int): Memory ID
• entity_id (str): Entity ID
• content (str): Memory content
• created_at (str): Creation timestamp
• metadata (dict, optional): Metadata

AugmentedContext Class

Augmented context with:

• original_query (str): Original query
• retrieved_memories (List[Memory]): Retrieved memories
• enhanced_prompt (str): Augmented prompt
• has_memories (bool): Whether memories were retrieved
• memories_count (int): Number of retrieved memories

Configuration

Database

Default database path: ./memori.db

The skill will:

• ✅ Read from the database to retrieve memories
• ✅ Write to the database when storing new memories
• ✅ Create the database file if it doesn't exist

Technical Terms

The skill uses configurable technical terms for LLM call interception. You can customize these via:

1. Environment variable (comma-separated):

export MEMORI_TECH_TERMS="FFI,Rust,Linux,kernel,spinlock,mutex,unsafe"

2. Configuration file (one term per line):

# Create config file
mkdir -p config
cat > config/tech_terms.txt << EOF
FFI
Rust
Linux
kernel
spinlock
mutex
unsafe
EOF

# Set environment variable
export MEMORI_TECH_TERMS_FILE="config/tech_terms.txt"

Note: If neither is set, the skill will still work but may not intercept technical queries as effectively.

Security Considerations

File Operations

This skill performs the following file operations:

Operation	File	Description
Read	./memori.db (default)	Retrieve stored memories
Write	./memori.db (default)	Store new memories
Read	MEMORI_TECH_TERMS_FILE	Load technical terms
Write	MEMORI_TECH_TERMS_FILE	Persist terms (if enabled)

External API Calls

⚠️ Important: If ZHIPUAI_API_KEY is set, this skill may send conversation text to Zhipu AI's servers for augmentation.

To disable external API calls:

• Simply don't set ZHIPUAI_API_KEY
• The skill will work normally using local memory retrieval only

Recommendations

1. Review the code before enabling external API features
2. Test in a sandbox first if providing API keys
3. Use file permissions to protect database and config files
4. Only enable features you explicitly need

File Structure

skills/memori_extension/
├── __init__.py               # Skill entry
├── memori_extension.py       # Skill implementation
├── SKILL.md                  # This file
└── README.md                 # Quick start guide

License

This skill is licensed under Apache License Version 2.0.

This skill uses the memori Python library, which is also licensed under Apache License Version 2.0.

Attribution

This skill incorporates the Memori Python library, which is licensed under the Apache License 2.0. See the LICENSE file for the complete license text.

Memori Library:

• Copyright 2025 Memori Team
• License: Apache License 2.0
• Repository: https://github.com/MemoriLabs/Memori