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")

Security Scan

Status

clean

Open VirusTotal

OpenClaw

gpt-5-mini

clean

OpenClaw analysis

This skill is internally coherent: it implements a local SQLite-backed agent memory (reads/writes ~/.agent-memory/memory.db by default), requires no credentials or external network access, and its files match the SKILL.md usage.

Confidence: high

VirusTotal

Type: OpenClaw Skill Name: agent-memory Version: 1.0.0 The OpenClaw AgentMemory skill bundle is a self-contained, SQLite-based persistent memory system for AI agents. All code and documentation are aligned with its stated purpose of storing facts, lessons, and entities. There is no evidence of data exfiltration, malicious execution, persistence mechanisms, obfuscation, or prompt injection attempts against the analyzing agent. The skill uses standard Python libraries, manages its database in a user-specific directory (`~/.agent-memory/memory.db`), and explicitly states 'No external dependencies' in `requirements.txt`, indicating a low supply chain risk.

Agent Memory