bannin 0.1.1

Universal monitoring platform — watch your machines, LLMs, notebooks, and coding tools from your phone.

Bannin

番人 (Japanese for "watchman") — a universal monitoring agent for your machine, your LLMs, and your cloud notebooks.

You hit run. You walk away. Then what?

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What It Does

Bannin runs on your machine (or inside a cloud notebook) and monitors everything: CPU, RAM, GPU, disk, running processes, LLM conversation health, API usage, and cloud session health. It predicts out-of-memory crashes before they happen, scores your AI conversation quality, fires smart alerts, and gives you plain-English summaries of how your system is doing.

It also works as an MCP server — meaning AI coding tools (Claude Code, Cursor, Windsurf) can query your system health through natural conversation.

Install

pip install bannin

With GPU monitoring and MCP server support:

pip install "bannin[all]"

Quick Start

Start the agent:

bannin start

Open the dashboard:

Visit localhost:8420 in your browser.

Use with Claude Code (MCP):

Add to your .mcp.json:

{
  "mcpServers": {
    "bannin": {
      "command": "python",
      "args": ["-m", "bannin.mcp"]
    }
  }
}

Then ask Claude things like:

• "How's my system doing?"
• "What processes are using the most memory?"
• "Am I going to run out of RAM?"
• "How healthy is this conversation?"

Features

System Monitoring

• CPU, RAM, disk, network -- real-time with 30-minute history
• GPU monitoring (NVIDIA via pynvml)
• Top processes by resource usage with friendly names and category badges
• Process descriptions and tooltips for 97 common applications
• Plain-English system health summaries

Intelligence

• OOM (out-of-memory) prediction with confidence scores and time-to-crash estimates
• Smart alerts with cooldowns (17+ configurable rules)
• Training progress detection (tqdm interception, stdout parsing)
• ETA estimation for long-running tasks
• L2 recommendations -- actionable suggestions from cross-signal analysis (12 rules)
• Built-in chatbot for natural language system health queries

LLM Health Monitoring

• Conversation health scoring (7 signals, 0-100 score) across Claude Code, Cursor, Windsurf, Ollama
• Real Claude Code session data via JSONL transcript reading (actual token counts)
• MCP session tracking with fatigue scoring and token estimation
• Ollama local LLM monitoring -- auto-detect, VRAM tracking, model load/unload
• LLM connection scanner -- detects 9 AI tool types running on your system
• Per-source health breakdown with combined worst-of scoring

LLM API Tracking

• Wrap OpenAI, Anthropic, and Google API clients to track token usage and cost
• Context window exhaustion prediction
• Latency degradation detection
• Cost calculation across 30+ models and 7 providers

Cloud Notebooks

• Google Colab: session time, GPU type changes, VRAM, storage, tier detection
• Kaggle: GPU quota tracking, session limits, output size, internet detection

Persistent Analytics

• Event pipeline with SQLite + FTS5 full-text search
• Query history by type, severity, or keyword
• 30-day retention with automatic pruning
• Separate analytics dashboard at port 8421

MCP Server (9 Tools)

• get_system_metrics -- full system snapshot (CPU, RAM, disk, network, GPU)
• get_running_processes -- top processes by CPU/memory with friendly names
• predict_oom -- memory exhaustion prediction with confidence
• get_training_status -- tracked task progress and ETAs
• get_active_alerts -- current monitoring alerts
• check_context_health -- conversation health score with component breakdown
• get_recommendations -- L2 actionable recommendations
• query_history -- search analytics event history
• search_events -- full-text search across stored events

Live Dashboard

• Real-time metrics with color-coded gauges (cyan/amber/red)
• Visual urgency indicators (amber glow at 60%, red pulse at 80%+)
• Conversation health accordion with per-source breakdown
• Connection badges for auto-detected LLM tools
• Memory trend chart (5-minute rolling)
• Process table with friendly names and hover tooltips
• OOM prediction display
• Alert banner with severity indicators
• Built-in chatbot ("Ask Bannin") with 15 intents
• Loading eye animation

API

The agent exposes a local REST API at localhost:8420:

Endpoint	Method	Purpose
/health	GET	Agent alive check
/status	GET	Agent identity (hostname, OS, version, uptime)
/metrics	GET	Full system snapshot (CPU, RAM, disk, network, GPU)
/processes	GET	Top processes with friendly names and categories
/summary	GET	Plain-English system health summary
/chat	POST	Chatbot (natural language system health assistant)
/alerts	GET	Full alert history for this session
/alerts/active	GET	Currently active alerts
/predictions/oom	GET	OOM prediction with confidence
/history/memory	GET	Memory usage history over last N minutes
/tasks	GET	Tracked tasks (training progress, ETAs)
/recommendations	GET	L2 actionable recommendations
/llm/usage	GET	LLM session health and usage summary
/llm/calls	GET	Recent LLM API call history
/llm/health	GET	Unified conversation health score (combined + per-source)
/llm/context	GET	Context window exhaustion prediction
/llm/latency	GET	Response latency trend analysis
/llm/connections	GET	Auto-detected LLM tools running on system
/ollama	GET	Ollama local LLM status and loaded models
/mcp/session	POST	Receive MCP session health push
/mcp/sessions	GET	List all live MCP sessions
/analytics/stats	GET	Analytics store statistics
/analytics/events	GET	Query stored events with filters
/analytics/search	GET	Full-text search across events
/analytics/timeline	GET	Event timeline
/platform	GET	Cloud notebook platform info (Colab/Kaggle)
/stream	GET	Server-Sent Events for live dashboard updates

Python API

import bannin

# Monitor system during a block of code
with bannin.watch():
    train_model()

# Wrap an LLM client to track tokens and cost
import openai
client = bannin.wrap(openai.OpenAI())

# Track a specific scope
with bannin.track("my-experiment"):
    response = client.chat.completions.create(...)

CLI

bannin start                    # Start the agent (dashboard at localhost:8420)
bannin start --port 9000        # Start on a custom port
bannin mcp                      # Start the MCP server (stdio transport)
bannin analytics                # Start the analytics dashboard (port 8421)
bannin history                  # Query stored event history
bannin history --since 2h       # Events from the last 2 hours
bannin history --search "OOM"   # Full-text search
bannin history --json           # Raw JSON output

What's Coming

• Phone alerts (push notifications when tasks finish or systems need attention)
• Browser extension for ChatGPT, Claude.ai, and Gemini monitoring
• Context transfer -- carry conversation state to a fresh chat when health degrades
• macOS Apple Silicon GPU support
• Shareable dashboards via bannin.dev

Requirements

• Python 3.9+
• Works on Windows, macOS, and Linux

License

MIT