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:
Load recent lessons: mem.get_lessons(limit=5)
Check entity context for current task
Recall relevant facts
On session end:
Extract durable facts from conversation
Record any lessons learned
Update entity information

Database Location

Default: ~/.agent-memory/memory.db

Custom: AgentMemory(db_path="/path/to/memory.db")