Awesome-OpenClaw-Memory

👋 Introduction

OpenClaw is an open-source autonomous AI virtual assistant originally developed by Austrian software engineer Peter Steinberger. First released on GitHub as "Clawdbot" in November 2025, later renamed to "Moltbot" and finally "OpenClaw" in January 2026, the project has since grown to over 200K GitHub stars and sparked a global wave of adoption. OpenClaw can be deployed locally on macOS/Windows, calls various AI models and APIs, and receives user instructions via WhatsApp, Telegram, Signal, Discord and other messaging platforms to autonomously handle tasks like scheduling, messaging, file management, and coding.

A key differentiator of OpenClaw is its persistent memory — it stores configuration data and interaction history locally, enabling cross-session continuity. However, the design, security, and scalability of this memory system remain an active frontier for the community.

Awesome-OpenClaw-Memory is a curated collection dedicated to memory systems, architectures, and best practices within the OpenClaw ecosystem. This repository gathers research papers, open-source memory frameworks, and practical implementations relevant to enhancing OpenClaw's memory capabilities.

Whether you're extending OpenClaw's built-in local memory, integrating external frameworks like Mem0 or Zep, or researching agent memory security (e.g., memory poisoning attacks) — this is your one-stop resource.

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🎯 Goal of Repository

Our mission is to build the most comprehensive knowledge base for OpenClaw memory systems, serving both researchers exploring agent memory frontiers and practitioners deploying memory-enhanced OpenClaw agents in production. We aim to accelerate the development of OpenClaw's persistent memory capabilities — from local interaction history to scalable, secure, cross-session knowledge management.

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📏 Project Scope

This repository focuses on memory mechanisms and system designs that extend or augment OpenClaw's cognitive capabilities, encompassing both theoretical research and engineering practices.

🌀 Included Content (In Scope)

• Memory architecture designs for OpenClaw agents
• OpenClaw-specific memory skills and plugins
• External memory frameworks integrable with OpenClaw (Mem0, Zep, Letta, LangMem, etc.)
• Memory management strategies (writing, retrieval, updating, forgetting, compression)
• Memory security (memory poisoning, integrity checks, sandboxing)
• Multi-agent shared memory in OpenClaw workflows
• Benchmarks and evaluation methods for agent memory
• Best practices and production patterns

🌀 Excluded Content (Out of Scope)

• General LLM pre-training research without agent memory relevance
• Traditional databases or retrieval systems unrelated to AI agents
• Non-agent memory systems without transfer value to OpenClaw

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🔔 Recent Updates

• 2026-03-22 - 🎉 Added OpenViking (17K+ stars, ByteDance/Volcengine context database for AI Agents)
• 2026-03-22 - 🎉 Added mem9 (ContextEngine-native persistent memory by PingCAP/TiDB founder)
• 2026-03-21 - 🎉 Added 5 OpenClaw memory plugins (memory-lancedb-pro, openclaw-supermemory, MemOS-Cloud, graph-memory, openclaw-memory-mem0)
• 2026-03-21 - 🎉 Initial release with 2 OpenClaw papers, 22 open-source memory systems, and the Adam Framework

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🗺️ Table of Contents

• Introduction
• Goal of Repository
• Project Scope
• Recent Updates
• Core Concepts
• OpenClaw Related Papers
• Resource
  • Systems and Open Sources
  • OpenClaw Memory Plugins
  • OpenClaw Memory Frameworks
• Make a Contribution
• Star Trends

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🧠 Core Concepts

• OpenClaw's Built-in Memory: OpenClaw stores configuration data and interaction history locally on the user's device, providing persistent memory across sessions. This local-first design ensures privacy but introduces challenges in scalability, retrieval accuracy, and memory management as interaction history grows.

• Memory Layers:

  • Session Memory: The active context during a single conversation session, including current task state and intermediate reasoning.
  • Interaction History: Locally stored records of past conversations and task executions across messaging platforms (WhatsApp, Telegram, Signal, Discord), forming OpenClaw's native long-term memory.
  • Configuration Memory: Persistent user preferences, API credentials, skill configurations, and behavioral settings.

• Memory Challenges for OpenClaw:

  • Scalability: As interaction history accumulates over months of use, efficient retrieval becomes critical.
  • Cross-platform Consistency: Maintaining coherent memory across multiple messaging platforms.
  • Memory Security: Autonomous agents face threats like memory poisoning (injecting malicious persistent data via crafted messages), intent drift (memory-induced behavioral changes), and privacy leakage — as highlighted in the "Taming OpenClaw" paper.
  • Memory Enhancement: Integrating external memory frameworks (Mem0, Zep, Letta, etc.) to augment OpenClaw's native local storage with vector search, knowledge graphs, and semantic retrieval.

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📄 OpenClaw Related Papers

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📦 Resource

💻 Systems and Open Sources

Systems below are ordered by publication date:

System	Time	Stars	GitHub & Website
Zep	2023-05-19		https://github.com/getzep/zephttps://www.getzep.com/
Agentmemory	2023-07-07		https://github.com/elizaOS/agentmemoryNo official website
Cognee	2023-10-09		https://github.com/topoteretes/cogneehttps://www.cognee.ai/
Letta	2023-10-26		https://github.com/letta-ai/lettahttps://www.letta.com/
Supermemory	2024-02-22		https://github.com/supermemoryai/supermemoryhttps://supermemory.ai/
Memary	2024-04-26		https://github.com/kingjulio8238/MemaryNo official website
Second-Me	2024-06-26		https://github.com/mindverse/Second-Me