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wenshuangl

wenshuangl/agent-mem

by wenshuangl · GitHub ↗ · v1.0.3 · MIT-0
cross-platform ⚠ pending
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Install in OpenClaw
/install agent-mem
Description
Multi-Agent Memory + Dispatch System. 4-tier memory (HOT/WARM/COLD/ARCHIVE), cross-channel sharing, dispatch loop with auto-learning.
README (SKILL.md)

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

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

Capability Tags
cryptorequires-sensitive-credentials
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install agent-mem
  3. After installation, invoke the skill by name or use /agent-mem
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.3
- Initial public release of agent-mem version 1.0.3. - Introduced 18 new modules including memory tiers, knowledge graph, active recall, forgetting mechanism, and contradiction detection. - Features a four-tier memory system (HOT/WARM/COLD/ARCHIVE) with natural memory decay. - Enables cross-channel memory sharing and a dispatch loop with auto-learning. - Memory modules cover extraction, search, feedback, self-review, and more—all fully local with minimal dependencies.
v1.0.1
- Initial public release of agent-mem (v1.0.1) - Added four-tier memory system (HOT/WARM/COLD/ARCHIVE) with natural decay - Implemented cross-channel memory sharing for agents - Introduced dispatch and memory loop system with auto-learning and optimization - Provided 17 modular memory features including fact extraction, vector search, contradiction detection, and more - Included quick start scripts, setup files, and essential documentation
v1.0.0
agent-mem 1.0.0 - Initial release of a multi-agent memory and dispatch system. - Features a four-tier memory system (HOT/WARM/COLD/ARCHIVE) with natural memory decay. - Enables cross-channel memory sharing for agents across platforms (webchat, Feishu, Slack, Telegram). - Includes a dispatch and memory loop for automated learning and optimization. - Bundles 17 memory modules (fact extraction, search, contradiction detection, knowledge graph, forgetting, active recall, feedback, self-review, and more). - Designed to be local-first with minimal dependencies (Python 3.10+, chromadb, no external APIs).
Metadata
Slug agent-mem
Version 1.0.3
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 3
Frequently Asked Questions

What is wenshuangl/agent-mem?

Multi-Agent Memory + Dispatch System. 4-tier memory (HOT/WARM/COLD/ARCHIVE), cross-channel sharing, dispatch loop with auto-learning. It is an AI Agent Skill for Claude Code / OpenClaw, with 91 downloads so far.

How do I install wenshuangl/agent-mem?

Run "/install agent-mem" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is wenshuangl/agent-mem free?

Yes, wenshuangl/agent-mem is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does wenshuangl/agent-mem support?

wenshuangl/agent-mem is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created wenshuangl/agent-mem?

It is built and maintained by wenshuangl (@wenshuangl); the current version is v1.0.3.

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