---
name: agentmemory-skill
description: "Persistent memory system for AI agents. Remember facts, learn from experience, and track entities across sessions."
license: "MIT"
metadata:
  decimalai-skill-id: "cf42205f-96e9-4305-b13e-6381b03eb72d"
  decimalai-version-id: "1aef0c74-df4b-4133-be73-502b529ddd5b"
  decimalai-version: "1"
  decimalai-body-sha256: "dad7628abf83f6d15d4873312bad8b5cab2a75625fa3a03df64f80cae221d703"
  decimalai-name: "agentmemory-skill"
  decimalai-format: "1"
  decimalai-invocation: "model"
  decimalai-category: "agent-tooling"
  decimalai-source: "https://app.decimal.ai/s/agentmemory-skill@1/SKILL.md"
---
# 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")`