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memory

AI agent long-term memory management skill (openmemory-py based). Store and retrieve conversation context, user preferences, and important information via semantic search. Note: Automatic context management is handled by the context-manager plugin. This skill is used when the agent **explicitly** manipulates memory. When to use: (1) When user requests memory save/retrieve → /memory add, /memory query (2) When agent determines additional context is needed → /memory query (3) When deleting unnecessary memories → /memory delete

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Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

View source ↗Guides

Skill instructions

Memory Skill

OpenMemory-based AI agent long-term memory management.

Relationship with Plugin

┌─────────────────────────────────────────────────────┐
│         memory Skill (Explicit Memory Ops)          │
│     • /memory add - Manually add memory             │
│     • /memory query - Manually search memories      │
│     • /memory list - List memories                  │
│     • /memory delete - Delete memory                │
│     • /memory status - Show memory system status    │
└─────────────────────────────────────────────────────┘

When to Use?

✅ Use This Skill
  1. On User Request

    • "Remember this" → /memory add
    • "Find what we analyzed before" → /memory query
    • "Delete that memory" → /memory delete
  2. Agent Autonomous Decision

    • When additional context is needed for current task → /memory query
    • When saving important learnings permanently → /memory add
    • When discovering user preferences or patterns → /memory add
🔄 Plugin Handles Automatically (Implicit)
  • Load related context at task start → context_start tool
  • Mid-task checkpoints → context_checkpoint tool
  • Save results at task end → context_end tool

CLI Usage

# Add memory
/memory add "User prefers Python development" --tags=preferences,programming

# Search memory (semantic search)
/memory query "areas of interest" --limit=5

# List memories
/memory list --limit=10

# Delete memory
/memory delete <memory_id>

# Show status
/memory status

# Clear all memories (caution!)
/memory clear --confirm

Python API

from scripts.memory_client import MemoryClient

with MemoryClient() as mem:
    # Add
    mem.add("Important info", tags=["tag1"], metadata={"source": "chat"})
    
    # Search
    results = mem.query("search term", limit=5)
    
    # Delete
    mem.delete(memory_id)

Tag Guide

Tag Purpose
preferences User preferences
topics Topics of interest
projects Projects of interest
context Conversation context
decision Decision history
checkpoint Task checkpoint (auto)
result Task result (auto)

Storage Location

OpenMemory data stored in data/memory/ directory. Shares the same DB with Context Manager.

Dependencies

openmemory-py>=1.3.0
langchain>=1.0.0
langchain-core>=1.0.0

Examples

Read the usage examples in the source repository.

View source ↗

Source history

Indexed on
2026-01-20
Index updated
2026-01-26
Branch
main

Index and sync dates describe this directory. View the source repository for code history.

GitHub ↗

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