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Agent Memory

Persistent memory system for AI agents.

Rating: 4.5 (172 reviews)
Downloads: 24,670 downloads
Version: 1.0.0

Overview

Persistent memory system for AI agents.

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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")

Installation

Terminal bash


openclaw install agent-memory

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💻Code Examples

mem.track_entity("Name", "person", {"role": "engineer"})

memtrackentityname-person-role-engineer.txt

## 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:

example.py

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"})

example.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