pug-brain 2.26.1

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

🐶 PugBrain: The Hybrid Neural-Vector Intelligence Core

PugBrain is an advanced hybrid memory system that bridges the gap between high-performance semantic search and associative neural reasoning. It fuses the speed of RuVector (Rust) with the causal intelligence of a graph-based neural network.

🚀 The Neural Memory Architecture

graph TD
    User((User/Agent)) -->|Query/Remember| PugBrain[PugBrain Core Engine]
    
    subgraph "Neural Memory System"
        Encoder[Memory Encoder]
        Retrieval[Reflex Pipeline]
        Graph[Neural Graph Storage]
        
        PugBrain --> Encoder
        PugBrain --> Retrieval
        Encoder <--> Graph
        Retrieval <--> Graph
    end
    
    subgraph "Storage Backends"
        SQLite[(SQLite - Default)]
        FalkorDB[(FalkorDB - Optional)]
        Neo4j[(Neo4j - Optional)]
        
        Graph --> SQLite
        Graph -.-> FalkorDB
        Graph -.-> Neo4j
    end
    
    subgraph "Integration Layer"
        MCP[MCP Server]
        REST[FastAPI Server]
        CLI[CLI Interface]
        Dashboard[Web Dashboard]
        
        PugBrain --> MCP
        PugBrain --> REST
        PugBrain --> CLI
        REST --> Dashboard
    end

• Neural Core: Graph-based memory with spreading activation for contextual recall and associative reasoning.
• Multi-Backend: Default SQLite storage with optional FalkorDB or Neo4j for enhanced graph operations.
• Integration Ready: MCP server for AI assistants, REST API, CLI tools, and web dashboard.

🛠 Key Features

• ✅ Activatable Intelligence: Neural graph with spreading activation for context-aware memory recall.
• ✅ Hybrid Multiple Brains: Support for multiple independent brain instances with seamless switching.
• ✅ Stabilized Dashboard: A modernized UI with automatic brain detection and zero-config CORS for reliable remote access.
• ✅ Docker Native: Ready-to-use containerized deployment with persistent volume mapping.
• ✅ MCP Ready: Standard Model Context Protocol server for seamless integration with Claude Code, Cursor, and OpenClaw.
• ✅ Multi-format Training: PDF, DOCX, PPTX, HTML, JSON, XLSX, CSV, and Markdown document ingestion.
• ✅ Code Indexing: Index and recall from codebases with syntax awareness.
• ✅ Cross-Language Embedding: Optional embedding support for multi-language memory recall.

📦 Installation & Setup

1. Python Package Installation

Install the core pug-brain package:

pip install pug-brain[server]

This provides the pug CLI command and FastAPI server with dashboard.

2. One-Line Installer

For a complete setup with dependencies:

curl -sSL https://raw.githubusercontent.com/tannht/pug-brain/pug-master/install.sh | bash

3. Manual Requirements

• Python: 3.11+ (required)
• Node.js: v18+ (for dashboard development)

⌨️ CLI Usage

PugBrain provides a simple global command for all your memory needs:

# Remember a strategic decision
pug remember "Switched primary model to Gemini 3.1 Pro for better reasoning" --type decision --tags setup,ai

# Recall with context
pug recall "What is our current model configuration?"

# Check system health and stats
pug status

🐳 Docker Deployment

To run the PugBrain Core and Dashboard behind a proxy (like Caddy/Nginx) with persistent storage:

# Clone the repo
git clone -b pug-master https://github.com/tannht/pug-brain.git
cd pug-brain

# Build and start
docker compose up -d --build

The dashboard will be available at http://localhost:18790 (mapped from container port 8000).

🔄 Maintenance

Keep your brain sharp by running the update script:

/root/.openclaw/workspace/pug-brain/scripts/update-brain.sh

---

Installation

pip install pug-brain

With optional features:

pip install pug-brain[server]       # FastAPI server + dashboard
pip install pug-brain[extract]      # PDF/DOCX/PPTX/HTML/XLSX extraction
pip install pug-brain[nlp-vi]       # Vietnamese NLP
pip install pug-brain[embeddings]   # Local embedding (cross-language recall)
pip install pug-brain[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):

# ~/.neuralmemory/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/pug-brain
/plugin install pug-brain@pug-brain-marketplace

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

OpenClaw (Plugin)

pip install pug-brain
npm install -g neuralmemory

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

{ "plugins": { "slots": { "memory": "neuralmemory" } } }

Restart the gateway. See the full setup guide.

Cursor / Windsurf / Other MCP Clients

pip install pug-brain

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

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

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

Usage

CLI

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

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

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

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

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

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

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

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

# Telegram backup
pug telegram status          # Show Telegram config status
pug telegram test            # Send test message
pug 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 28 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

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_explain	Trace shortest path between two concepts — debug recall, verify connections
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

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

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

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

nmem 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 NMEM_TELEGRAM_BOT_TOKEN="your-bot-token"
# Add to config.toml:
# [telegram]
# enabled = true
# chat_ids = ["123456789"]

# CLI
nmem telegram status              # Check config
nmem telegram test                # Send test message
nmem telegram backup              # Send brain backup
nmem 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
nmem 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
nmem check "API_KEY=sk-xxx"

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

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

# Health check (freshness + sensitive scan)
nmem 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 pug-brain[server]
nmem serve                    # localhost:8000
nmem serve -p 9000            # Custom port
nmem 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

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

Development

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

# Run tests (2860+ 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 NeuralMemory useful, consider supporting development:

Solana: 5XVY6dZDeyuZJy6Co9KeLDxY5RZ6EwCpjsUVkacMz7HF

Contributing

Contributions welcome! See CONTRIBUTING.md.

License

MIT License — see LICENSE.

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