pug-brain 3.1.2

Reflex-based memory system for AI agents - retrieval through activation, not search

🐶 PugBrain: Reflex-based Neural Memory

Reflex-based memory system for AI agents — retrieval through activation, not search.

PugBrain is a hybrid neural-vector memory system designed for long-term agent continuity. It combines graph relationships with vector embeddings to simulate "reflexive" recall.

🚀 Quick Start

42 MCP tools · 14 memory types · 24 synapse types · Schema v22 · 3500+ tests · Cognitive reasoning layer

pip install neural-memory

🛠 Features

• Hybrid Retrieval: Neural graph + Vector similarity.
• Built-in Dashboard: Visualize memory clusters and agent habits.
• MCP Support: Ready for Claude Desktop and OpenClaw.
• Pluggable Backends: SQLite (default), Neo4j, FalkorDB, ChromaDB.

🖥 Dashboard

Serve the memory system with a built-in UI:

pugbrain serve --port 18790

Visit: http://localhost:18790/ui

🧠 Core Philosophy

Memory shouldn't be a database query; it should be an activation signal. PugBrain treats entities as neurons that "fire" based on context.

---

Installation

pip install neural-memory

With optional features:

pip install neural-memory[server]       # FastAPI server + dashboard
pip install neural-memory[extract]      # PDF/DOCX/PPTX/HTML/XLSX extraction
pip install neural-memory[nlp-vi]       # Vietnamese NLP
pip install neural-memory[embeddings]   # Local embedding (cross-language recall)
pip install neural-memory[all]          # All features

Optional: Embedding for Cross-Language Recall

Core recall works without embeddings. Enable embeddings to recall memories across languages (e.g., search in Vietnamese, find English memories):

# ~/.pugbrain/config.toml
[embedding]
enabled = true
provider = "auto"    # Auto-detects: Ollama → sentence-transformers → Gemini → OpenAI

Or pick a specific provider: sentence_transformer (free/local), ollama (local via Ollama API), gemini (Google free tier), openai (paid). See the Embedding Setup Guide for details.

Quick Setup

Claude Code (Plugin — Recommended)

/plugin marketplace add nhadaututtheky/neural-memory
/plugin install neural-memory@neural-memory-marketplace

That's it. MCP server, skills, commands, and agent are all configured automatically via uvx.

OpenClaw (Plugin)

pip install neural-memory
npm install -g pugbrain

Then set the memory slot in ~/.openclaw/openclaw.json:

{ "plugins": { "slots": { "memory": "pugbrain" } } }

Restart the gateway. See the full setup guide.

Cursor / Windsurf / Other MCP Clients

pip install neural-memory

Then add to your editor's MCP config (Cursor: .cursor/mcp.json, Windsurf: ~/.codeium/windsurf/mcp_config.json):

{
  "mcpServers": {
    "pugbrain": {
      "command": "pug-mcp"
    }
  }
}

The editor spawns pug-mcp automatically via stdio — no manual server start needed. No pugbrain init needed — auto-initializes on first use.

Usage

CLI

# Store memories (type auto-detected)
pugbrain remember "Fixed auth bug with null check in login.py:42"
pugbrain remember "We decided to use PostgreSQL" --type decision
pugbrain todo "Review PR #123" --priority 7

# Recall memories
pugbrain recall "auth bug"
pugbrain recall "database decision" --depth 2

# Shortcuts
pugbrain a "quick note"           # Short for remember
pugbrain q "auth"                 # Short for recall
pugbrain last 5                   # Last 5 memories
pugbrain today                    # Today's memories

# Get context for AI injection
pugbrain context --limit 10 --json

# Brain management
pugbrain brain list
pugbrain brain create work
pugbrain brain use work
pugbrain brain health
pugbrain brain export -o backup.json
pugbrain brain import backup.json

# Codebase indexing
pugbrain index src/               # Index code into neural memory

# Memory lifecycle
pugbrain decay                    # Apply forgetting curve
pugbrain consolidate              # Prune, merge, summarize
pugbrain cleanup                  # Remove expired memories

# Visual tools
pugbrain serve                    # Start FastAPI server
# Then open http://localhost:8000/dashboard

# Telegram backup
pugbrain telegram status          # Show Telegram config status
pugbrain telegram test            # Send test message
pugbrain telegram backup          # Send brain .db to Telegram

Python API

import asyncio
from neural_memory import Brain
from neural_memory.storage import InMemoryStorage
from neural_memory.engine.encoder import MemoryEncoder
from neural_memory.engine.retrieval import ReflexPipeline

async def main():
    storage = InMemoryStorage()
    brain = Brain.create("my_brain")
    await storage.save_brain(brain)
    storage.set_brain(brain.id)

    # Encode memories
    encoder = MemoryEncoder(storage, brain.config)
    await encoder.encode("Met Alice to discuss API design")
    await encoder.encode("Decided to use FastAPI for backend")

    # Query through activation
    pipeline = ReflexPipeline(storage, brain.config)
    result = await pipeline.query("What did we decide about backend?")
    print(result.context)  # "Decided to use FastAPI for backend"

asyncio.run(main())

MCP Tools (Claude Code / Cursor)

Once configured, these 42 tools are available to your AI assistant:

Core Memory:

Tool	Description
pugbrain_remember	Store a memory (auto-detects type: fact, decision, insight, error, etc.)
pugbrain_recall	Query with spreading activation (4 depth levels: instant/context/habit/deep)
pugbrain_context	Get recent memories as session context
pugbrain_todo	Quick TODO with 30-day expiry
pugbrain_auto	Auto-capture memories from conversation text
pugbrain_suggest	Autocomplete suggestions from brain neurons
pugbrain_edit	Edit memory type, content, or priority (preserves connections)
pugbrain_forget	Soft delete (set expiry) or hard delete (permanent removal)

Workflow:

Tool	Description
pugbrain_session	Track session state: task, feature, progress
pugbrain_eternal	Save project context, decisions, instructions
pugbrain_recap	Load saved context at session start
pugbrain_stats	Brain statistics and health metrics
pugbrain_habits	Workflow habit suggestions from usage patterns

Knowledge Base:

Tool	Description
pugbrain_train	Train brain from docs (PDF, DOCX, PPTX, HTML, JSON, XLSX, CSV, MD)
pugbrain_train_db	Train brain from database schema
pugbrain_index	Index codebase for code-aware recall
pugbrain_pin	Pin/unpin memories (pinned = permanent, skip decay/prune)

Advanced:

Tool	Description
pugbrain_health	Brain health: purity score, grade (A-F), top penalties with fix actions
pugbrain_review	Spaced repetition reviews (Leitner box system)
pugbrain_conflicts	Memory conflicts: list, resolve, or pre-check
pugbrain_narrative	Generate narratives: timeline, topic, or causal chain
pugbrain_alerts	Brain health alerts: list or acknowledge
pugbrain_version	Brain version control: snapshot, list, rollback, diff
pugbrain_transplant	Transplant memories between brains by tags/types
pugbrain_import	Import from ChromaDB, Mem0, Cognee, Graphiti, LlamaIndex
pugbrain_sync	Multi-device sync (push/pull/full)
pugbrain_sync_status	Sync status and pending changes
pugbrain_sync_config	Configure sync settings
pugbrain_telegram_backup	Send brain database backup to Telegram

Cognitive Reasoning:

Tool	Description
pugbrain_hypothesize	Create and manage hypotheses with Bayesian confidence tracking
pugbrain_evidence	Submit evidence for/against hypotheses — auto-updates confidence
pugbrain_predict	Make falsifiable predictions with deadlines, linked to hypotheses
pugbrain_verify	Verify predictions as correct/wrong — propagates to linked hypotheses
pugbrain_cognitive	Hot index: ranked summary of active hypotheses + predictions
pugbrain_gaps	Knowledge gaps: detect, track, and resolve what the brain doesn't know
pugbrain_schema	Schema evolution: evolve hypotheses into new versions with SUPERSEDES links
pugbrain_explain	Trace shortest path between two concepts — debug recall, verify connections

VS Code Extension

Install from the VS Code Marketplace.

• Memory tree view in the sidebar
• Interactive graph explorer with Cytoscape.js
• Encode from editor selections or comment triggers
• CodeLens memory counts on functions and classes
• Recap, eternal context, and codebase indexing commands
• Real-time WebSocket sync

How It Works

Query: "What did Alice suggest?"
         │
         ▼
┌─────────────────────┐
│ 1. Decompose Query  │  → time hints, entities, intent
└─────────────────────┘
         │
         ▼
┌─────────────────────┐
│ 2. Find Anchors     │  → "Alice" neuron
└─────────────────────┘
         │
         ▼
┌─────────────────────┐
│ 3. Spread Activation│  → activate connected neurons
└─────────────────────┘
         │
         ▼
┌─────────────────────┐
│ 4. Find Intersection│  → high-activation subgraph
└─────────────────────┘
         │
         ▼
┌─────────────────────┐
│ 5. Extract Context  │  → "Alice suggested rate limiting"
└─────────────────────┘

Key Concepts

Concept	What it is
Neuron	A memory unit (concept, entity, action, time, state, spatial, sensory, intent)
Synapse	A weighted, typed connection between neurons (CAUSED_BY, LEADS_TO, RESOLVED_BY, etc.)
Fiber	A memory trace — an ordered sequence of neurons forming a coherent experience
Spreading activation	Signal propagates from anchor neurons through synapses, decaying with distance
Reflex pipeline	Query → decompose → anchor → activate → intersect → extract context
Decay	Memories lose activation over time following the Ebbinghaus forgetting curve
Consolidation	Prune weak synapses, merge overlapping fibers, summarize topic clusters

Features

Memory Types

pugbrain remember "Objective fact" --type fact
pugbrain remember "We chose X over Y" --type decision
pugbrain remember "User prefers dark mode" --type preference
pugbrain todo "Review the PR" --priority 7 --expires 30
pugbrain remember "Pattern: always validate input" --type insight
pugbrain remember "Meeting notes from standup" --type context --expires 7
pugbrain remember "Always run tests before push" --type instruction
pugbrain remember "Import failed: missing column" --type error
pugbrain remember "Deploy process: build → test → push" --type workflow
pugbrain remember "API docs: https://..." --type reference

Knowledge Base Training

# Train from documents (permanent knowledge)
pugbrain_train(action="train", path="/docs/", domain_tag="project-docs")

# Supported formats: PDF, DOCX, PPTX, HTML, JSON, XLSX, CSV, MD, TXT, RST
# Trained memories are pinned — they never decay, prune, or compress

# Pin/unpin specific memories
pugbrain_pin(fiber_ids=["abc123"], pinned=True)

Install extraction dependencies:

pip install neural-memory[extract]

Brain Health & Diagnostics

pugbrain_health()                       # Purity score, grade (A-F), top penalties
pugbrain_alerts(action="list")          # Active health alerts
pugbrain_review(action="queue")         # Spaced repetition review queue

Health reports include top_penalties — a ranked list of what's hurting your score most, with exact fix actions. Always fix the highest penalty first.

7 components: Connectivity (25%), Diversity (20%), Freshness (15%), Consolidation (15%), Orphan Rate (10%), Activation (10%), Recall Confidence (5%).

See the Brain Health Guide for detailed explanations and improvement roadmap.

Connection Tracing

Trace the shortest path between two concepts in your neural graph:

# CLI
pugbrain explain "Redis" "auth outage"

# MCP tool
pugbrain_explain(entity_a="Redis", entity_b="auth outage")

Returns the path with evidence: Redis → USED_BY → session-store → CAUSED_BY → auth outage. Use this to debug recall, verify brain connections, or discover unexpected relationships between concepts.

Cognitive Reasoning

Hypothesize, predict, verify, and evolve beliefs — the brain reasons about what it knows:

# Create a hypothesis with initial confidence
pugbrain_hypothesize(action="create", content="Redis is causing the latency spike", confidence=0.6)

# Submit evidence
pugbrain_evidence(hypothesis_id="h-1", evidence_type="for", content="Redis latency at 200ms")
pugbrain_evidence(hypothesis_id="h-1", evidence_type="against", content="Network latency was 500ms")

# Make a falsifiable prediction
pugbrain_predict(action="create", content="Switching to Valkey will fix latency",
             hypothesis_id="h-1", deadline="2026-04-01")

# Verify prediction outcome — propagates to linked hypothesis
pugbrain_verify(prediction_id="p-1", outcome="correct")

# Evolve hypothesis when understanding changes (creates SUPERSEDES chain)
pugbrain_schema(action="evolve", hypothesis_id="h-1",
            content="Network config was root cause, not Redis",
            reason="New evidence from network team")

# Track what the brain doesn't know
pugbrain_gaps(action="detect", topic="Why does latency spike at 3am?", source="recall_miss")

# View cognitive dashboard
pugbrain_cognitive(action="summary")    # Hot index of active hypotheses + predictions
pugbrain_schema(action="history", hypothesis_id="h-2")  # Version evolution chain

Auto-resolution: hypotheses with confidence ≥0.9 + 3 supporting evidence → auto-confirmed. Confidence ≤0.1 + 3 against → auto-refuted. Calibration score tracks prediction accuracy.

Brain Versioning

pugbrain_version(action="create", name="v1-stable")  # Snapshot
pugbrain_version(action="list")                       # List versions
pugbrain_version(action="rollback", version_id="...")  # Restore
pugbrain_version(action="diff", from_version="...", to_version="...")

Web Dashboard

pugbrain serve                         # Start server on localhost:8000
# Open http://localhost:8000/dashboard  # React dashboard (7 pages)
# Open http://localhost:8000/docs       # API docs (Swagger)

Pages:

• Overview — KPI cards (neurons, synapses, fibers, brains) + brain table with click-to-switch and delete
• Health — Radar chart + health warnings + recommendations
• Graph — Sigma.js WebGL neural graph with ForceAtlas2 layout, color-coded by type, node inspector
• Timeline — Chronological memory feed with type badges
• Evolution — Brain maturity, plasticity, stage distribution charts
• Mindmap — ReactFlow interactive fiber mindmap (dagre tree, zoom/pan, MiniMap)
• Settings — Brain files, Telegram backup config

Light/Dark/System theme toggle with warm cream light mode.

Telegram Backup

Send brain .db files to Telegram for offsite backup:

# Setup: set env var + config
export PUGBRAIN_TELEGRAM_BOT_TOKEN="your-bot-token"
# Add to config.toml:
# [telegram]
# enabled = true
# chat_ids = ["123456789"]

# CLI
pugbrain telegram status              # Check config
pugbrain telegram test                # Send test message
pugbrain telegram backup              # Send brain backup
pugbrain telegram backup --brain work # Specific brain

# MCP tool
pugbrain_telegram_backup(brain_name="work")

Multi-Device Sync

pugbrain_sync(action="full")           # Bidirectional sync
pugbrain_sync_status()                 # Pending changes, devices
pugbrain_sync_config(action="set", hub_url="http://hub:8080")

External Memory Import

Import from existing memory systems:

# ChromaDB
pugbrain import backup.json --source chromadb

# Via MCP tool
pugbrain_import(source="mem0")           # Uses MEM0_API_KEY env var
pugbrain_import(source="chromadb", connection="/path/to/chroma")
pugbrain_import(source="cognee")         # Uses COGNEE_API_KEY env var
pugbrain_import(source="graphiti", connection="bolt://localhost:7687")
pugbrain_import(source="llamaindex", connection="/path/to/index")

Safety & Security

# Sensitive content detection
pugbrain check "API_KEY=sk-xxx"

# Auto-redact before storing
pugbrain remember "Config: API_KEY=sk-xxx" --redact

# Safe export (exclude sensitive neurons)
pugbrain brain export --exclude-sensitive -o safe.json

# Health check (freshness + sensitive scan)
pugbrain brain health

• Content length validation (100KB limit)
• ReDoS protection (text truncation before regex)
• Spreading activation queue cap (prevents memory exhaustion)
• API keys read from environment variables, never from tool parameters
• max_tokens clamped to 10,000

Server Mode

pip install neural-memory[server]
pugbrain serve                    # localhost:8000
pugbrain serve -p 9000            # Custom port
pugbrain serve --host 0.0.0.0    # Expose to network

API endpoints:

POST /memory/encode     - Store memory
POST /memory/query      - Query memories
POST /brain/create      - Create brain
GET  /brain/{id}/export - Export brain
WS   /sync/ws           - Real-time sync
POST /hub/sync          - Multi-device incremental sync
GET  /hub/devices/{id}  - List registered devices
GET  /dashboard         - Web dashboard
GET  /docs              - API documentation

Git Hooks

pugbrain hooks install          # Post-commit reminder to save commit messages
pugbrain hooks show             # Show installed hooks
pugbrain hooks uninstall        # Remove hooks

Development

git clone https://github.com/nhadaututtheky/neural-memory
cd neural-memory
pip install -e ".[dev]"

# Run tests (3500+ tests)
pytest tests/ -v

# Lint & format
ruff check src/ tests/
ruff format src/ tests/

Documentation

• Complete Guide — Full documentation
• Integration Guide — AI assistant & tool integration
• Safety & Limitations — Security best practices
• Architecture — Technical design

Support

If you find PugBrain useful, consider supporting development:

Solana: 5XVY6dZDeyuZJy6Co9KeLDxY5RZ6EwCpjsUVkacMz7HF

Contributing

Contributions welcome! See CONTRIBUTING.md.

License

MIT License — see LICENSE.