Persistent long-term memory for AI agents. Store, recall, reason, and seamlessly switch sessions with zero context loss.
--- slug: memorylayer name: MemoryLayer description: Semantic memory for AI agents. 95% token savings with vector search. homepage: https://memorylayer.clawbot.hk metadata: clawdbot: emoji: "�
Profile and optimize application performance. Use when diagnosing slow code, measuring CPU/memory usage, generating flame graphs, benchmarking functions, load testing APIs, finding memory leaks, or op
--- name: reclaw description: "Use when accessing memory, recording information, searching prior context, or managing subjects." read_when: - You need to find something from a previous session - Y
Local-first agent memory with Ebbinghaus decay, hybrid search, and MCP tools. Import files, extract facts, search with BM25 + semantic, track confidence over...
AI agent fleet memory system — Qdrant + Mem0 + Neo4j/Graphiti. Composite scoring, compaction engine, temporal knowledge graph, multi-claw federation, sleep-t...
Pure-Python recursive memory recall for persistent AI agents. Manager→workers→synthesis RLM loop — no Deno, no fast-rlm, just HTTP calls to any OpenAI-compat...
Advanced thinking model that improves decision-making speed and accuracy. Integrates with memory system to compare and integrate previous thinking models for continuous enhancement.
Audit and improve OpenClaw workspaces across SOUL, AGENTS, TOOLS, USER, MEMORY, and skills for persona tuning, proactive behavior, recall, and workflow fit....
Your agent always comes back. Anchor identity and memory on-chain so any new instance can resurrect from just an address — no local state, no single point of...
Seven biologically-inspired memory systems for OpenClaw agents. Gives your agent overnight learning (nightly consolidation), metacognition (confidence tracki...
Structured long-term memory for AI agents with fact curation, conflict detection, importance scoring, timeline reconstruction, and OpenClaw integration.
Compile multi-format documents into local queryable knowledge bases and manage persistent AI memory tiers using Aura Core with zero network requests.
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...
Semantic search tool to quickly find answers across multiple code repositories with AI memory of your preferences for faster documentation lookup.
The only memory skill that watches on its own. No database. No vectors. No manual saves. Just an LLM observer that compresses your conversations into priorit...
Neuroscience-based multi-layer memory system for OpenClaw agents that improves context efficiency using semantic schemas, vector stores, and sleep cycle cons...
Cultivate aesthetic judgment through conversation and memory. Build and maintain TASTES.md — the missing layer between SOUL.md and SKILL.md.
Cloud memory for AI agents. Writes are free, pay only for reads. First 25 calls free, 7-day persistence.
Provides persistent two-tier memory with semantic fact search and raw content retrieval, plus automatic cleanup of temporary session files.
Auto context management - monitors usage and triggers memory transfer at 95% threshold to prevent overflow and ensure session continuity. Trigger on "context...
Kimi (Moonshot AI) agent tool-use policy ported to OpenClaw. Covers step limits, web search, image search, data sources, ipython, memory, content display, an...
🔥 Memphis - Complete AI Brain for OpenClaw Agents ALL-IN-ONE meta-package with everything you need: 🧠 Core Features: - Local-first memory chains (journal,...
Structured memory system for AI agents. Context death resilience (checkpoint/recover), structured storage, Obsidian-compatible markdown, and local semantic search.