Introduction
# AgentMemory Skill
Persistent memory system for AI agents. Remember facts, learn from experience, and track entities across sessions.
## Installation
```bash clawdhub install agent-memory ```
## Usage
```python from src.memory import AgentMemory
mem = AgentMemory()
# Remember facts mem.remember("Important information", tags=["category"])
# Learn from experience mem.learn( action="What was done", context="situation", outcome="positive", # or "negative" insight="What was learned" )
# Recall memories facts = mem.recall("search query") lessons = mem.get_lessons(context="topic")
# Track entities mem.track_entity("Name", "person", {"role": "engineer"}) ```
## When to Use
- **Starting a session**: Load relevant context from memory - **After conversations**: Store important facts - **After failures**: Record lessons learned - **Meeting new people/projects**: Track as entities
## Integration with Clawdbot
Add to your AGENTS.md or HEARTBEAT.md:
```markdown ## Memory Protocol
On session start: 1. Load recent lessons: `mem.get_lessons(limit=5)` 2. Check entity context for current task 3. Recall relevant facts
On session end: 1. Extract durable facts from conversation 2. Record any lessons learned 3. Update entity information ```
## Database Location
Default: `~/.agent-memory/memory.db`
Custom: `AgentMemory(db_path="/path/to/memory.db")`