Multi-Agent Memory + Dispatch System. 4-tier memory (HOT/WARM/COLD/ARCHIVE), cross-channel sharing, dispatch loop with auto-learning.
---
name: agent-mem
description: Multi-Agent Memory + Dispatch System. 4-tier memory (HOT/WARM/COLD/ARCHIVE), cross-channel sharing, dispatch loop with auto-learning.
emoji: 🧠🔄
metadata:
{
"openclaw":
{
"requires": {},
"install": ["pip install -e ."]
}
}
---
# AgentMem
Multi-Agent Memory + Dispatch System
## Core Capabilities
### 1. Four-Tier Memory (HOT → WARM → COLD → ARCHIVE)
Memories decay naturally over time instead of being treated equally.
### 2. Cross-Channel Memory Sharing
Same agent shares memory across different channels (webchat/Feishu/Slack/Telegram).
### 3. Dispatch + Memory Loop
```
User request → Intent recognition → Agent dispatch → Execution → Auto-log → Optimize next dispatch
```
### 4. 17 Memory Modules
Fact extraction, BM25+vector fusion search, contradiction detection, knowledge graph, forgetting mechanism, active recall, memory feedback, self-review.
## Quick Start
```bash
pip install -e .
# Write a memory
python -m agent_mem.core.hot_cache write --agent main --channel webchat --text "User prefers concise answers" --importance 7
# Cross-channel query
python -m agent_mem.core.hot_cache query --agent main --limit 5
# Dispatch stats
python -m agent_mem.core.dispatch_logger stats
# Run memory engine
python -m agent_mem.core.engine_v2 --mode daily
```
## Requirements
- Python 3.10+
- chromadb (single dependency)
- Zero external API dependencies, fully local
## Links
- GitHub: https://github.com/wenshuangl/agent-mem
- License: MIT
don't have the plugin yet? install it then click "run inline in claude" again.