Persistent memory for AI agents to store facts, learn from actions, recall information, and track entities across sessions.
Install
Documentation
AgentMemory Skill
Persistent memory system for AI agents. Remember facts, learn from experience, and track entities across sessions.
Installation
clawdhub install agent-memory
Usage
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:
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")
Launch an agent with Agent Memory on Termo.