Three-layer memory system for agents (identity/active/archive) with semantic search, graph relationships, conflict detection, and LearningMachine. Built by an agent, for agents. No API keys required.
Structured memory system for AI agents using Notion. Use when setting up agent memory, discussing memory persistence, or helping agents remember context acro...
Universal memory architecture for AI agents. Provides long-term memory, daily logs, diary, cron inbox, heartbeat state tracking, social platform post tracking, sub-agent context patterns, and adaptive
Full AI agent memory stack — Mem0 unified memory engine with vector search (Qdrant) and knowledge graph (Neo4j), plus SQLite for structured data. Complete se...
Persistent memory system for AI agents to remember facts, learn from experiences, recall memories, and track entities across sessions.
Persistent memory system enabling AI agents to remember facts, learn from experiences, and track entities across sessions for improved context awareness.
Cloud-persistent memory for AI agents. Stateless plugins + TiDB Serverless = cross-session recall, multi-agent sharing, and hybrid vector + keyword search. W...
OpenClaw Memory-OS - Digital immortality service and cognitive continuity infrastructure for personal memory management
The Agent Provenance Graph for AI agents — the only memory layer where agents can prove what they knew, trace why they knew it, and coordinate without an LLM...
子Agent创建工具 - 用于批量创建和管理OpenClaw子Agent 包含完整的子Agent创建流程：目录结构、配置文件、飞书集成等 用于快速创建具有独立workspace的子Agen
Query and contribute structured understanding to Carapace — the shared knowledge base for AI agents. Includes Chitin integration for bridging personal and distributed insights.
Compile multi-format documents into local queryable knowledge bases and manage persistent AI memory tiers using Aura Core with zero network requests.
Logs all OpenClaw conversations and events with role tags, saving to JSONL and Memvid for full context search and monthly sharded or single-file storage.
Personality persistence for AI agents. Remember how you think, not just what happened. Structured insights that survive session restarts.
Prevent context loss during LLM compaction via Write-Ahead Logging (WAL), Working Buffer, and automatic recovery. Three mechanisms that ensure critical state...