Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Associative memory with spreading activation for persistent, intelligent recall. Use PROACTIVELY when: (1) You need to remember facts, decisions, errors, or context across sessions (2) User asks "do you remember..." or references past conversations (3) Starting a new task — inject relevant context from memory (4) After making decisions or encountering errors — store for future reference (5) User asks "why did X happen?" — trace causal chains through memory Zero LLM dependency. Neural graph with Hebbian learning, memory decay, contradiction detection, and temporal reasoning.
Associative memory with spreading activation for persistent, intelligent recall. Use PROACTIVELY when: (1) You need to remember facts, decisions, errors, or context across sessions (2) User asks "do you remember..." or references past conversations (3) Starting a new task — inject relevant context from memory (4) After making decisions or encountering errors — store for future reference (5) User asks "why did X happen?" — trace causal chains through memory Zero LLM dependency. Neural graph with Hebbian learning, memory decay, contradiction detection, and temporal reasoning.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Reflex-based memory system for AI agents — stores experiences as interconnected neurons and recalls them through spreading activation, mimicking how the human brain works.
Neural Memory gives AI agents persistent, associative memory across sessions. Instead of keyword search, it uses spreading activation through a neural graph — memories that fire together, wire together.
45 MCP tools for persistent memory + cognitive reasoning Spreading activation recall — not keyword search, memories activate related memories Cognitive reasoning — hypotheses, evidence, predictions, schema evolution Knowledge base training from PDF, DOCX, PPTX, HTML, JSON, XLSX, CSV Multi-device sync with neural-aware conflict resolution 4 embedding providers — Sentence Transformers, Gemini, Ollama, OpenAI Retrieval pipeline — RRF score fusion, graph expansion, Personalized PageRank Session intelligence — topic EMA tracking, LRU eviction, auto-expiry React dashboard — 7 pages: health, evolution, graph, timeline, settings VS Code extension — status bar, graph explorer, CodeLens, memory tree Fernet encryption for sensitive content Brain versioning — snapshots, rollback, export/import Telegram backup — send brain .db to chat/group/channel
pip install neural-memory Or with embeddings: pip install neural-memory[embeddings]
{ "mcpServers": { "neural-memory": { "command": "uvx", "args": ["--from", "neural-memory", "nmem-mcp"] } } }
Neural Memory works automatically once configured. RECALL — before responding to tasks that reference past work: New session → nmem_recall("current project context") Past decision/event → nmem_recall("<project> <topic>") Skip for purely new, self-contained questions SAVE — after completing each task, if you made a decision, fixed a bug, learned a preference, or discovered a pattern: nmem_remember(content="Chose X over Y because Z", type="decision", priority=7, tags=["project", "topic"]) Use causal language (not flat facts). Max 1-3 sentences. Do NOT save ephemeral file reads, things in git history, or duplicates. FLUSH — at session end: nmem_auto(action="process", text="brief summary")
TypeUse ForfactStable knowledgedecision"Chose X over Y because Z"insightPatterns discoverederrorBugs and root causesworkflowProcess stepspreferenceUser preferencesinstructionRules to follow
GitHub Documentation VS Code Extension
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.