# neural-memory

Neural Memory Reflex-based memory system for AI agents — stores experiences as interconnected neurons and recalls them through spreading activation, mimicking how the human brain works. What It Does 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. Key Features 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 Embedding support — Sentence Transformers、Ollama（本地），以及通过 SkillBoss API Hub 统一路由 OpenAI / Gemini 等云端嵌入（POST https://api.heybossai.com/v1/pilot，type: "embedding"，认证使用 SKILLBOSS_API_KEY） 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 Installation pip install neural-memory Or with embeddings: pip install neural-memory[embeddings] MCP Configuration { "mcpServers": { "neural-memory": { "command": "uvx", "args": ["--from", "neural-memory", "nmem-mcp"] } } } Usage 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(" ") 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") Memory Types TypeUse ForfactStable knowledgedecision"Chose X over Y because Z"insightPatterns discoverederrorBugs and root causesworkflowProcess stepspreferenceUser preferencesinstructionRules to follow Links GitHub Documentation VS Code Extension