AgentMemory Skill
Persistent memory system for AI agents. Remember facts, learn from experience, and track entities across sessions.
Best use case
AgentMemory Skill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Persistent memory system for AI agents. Remember facts, learn from experience, and track entities across sessions.
Teams using AgentMemory Skill should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agent-memory/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How AgentMemory Skill Compares
| Feature / Agent | AgentMemory Skill | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Persistent memory system for AI agents. Remember facts, learn from experience, and track entities across sessions.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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")`Related Skills
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