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
Installation
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
View source ↗GuidesSkill 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
-
On User Request
- "Remember this" →
/memory add - "Find what we analyzed before" →
/memory query - "Delete that memory" →
/memory delete
- "Remember this" →
-
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
- When additional context is needed for current task →
🔄 Plugin Handles Automatically (Implicit)
- Load related context at task start →
context_starttool - Mid-task checkpoints →
context_checkpointtool - Save results at task end →
context_endtool
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 ↗chat Comments (0)
Sign in to join the discussion and leave a comment.
No comments yet. Be the first to share your thoughts!