Agent Mesh Mission Control

Real-time dashboard for multi-agent AI systems — built for the Claude Code + Hermes + OpenClaw stack. One command to run. No cloud. No API keys.

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What it shows

Most local AI setups are invisible — agents running in terminals, logs scattered, no idea what's happening across the mesh. This fixes that.

• KPI strip — live Agents Online, MLX server status, system RAM, AMP queue depth, CPU — always visible at the top
• Agent status — every agent (Atlas, Hermes, iriseye) with current state, pulsing dot, color-coded
• Service pills — OpenViking, MLX, MemMCP, OpenClaw, Ollama — up/down at a glance
• Mesh graph — animated canvas topology: hexagonal agent nodes, radial service nodes, animated pulse dots on active edges
• AMP events feed — live routing log from Hermes + iriseye bridges (route=mlx, route=hermes, reply sent)
• AMP inbox — messages in/out of AI Maestro
• MLX tab — arc gauge showing MLX RAM pressure, model stats, PID, inference engine
• Memory monitor — OpenViking recall activity
• Cron/schedule — Hermes scheduled tasks with progress bars
• Mesh insights — cross-agent pattern analysis
• Activity feed — unified event stream

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Design

Premium dark-mode terminal aesthetic. No scroll, no hidden panels.

• Full-bleed layout — header → KPI strip → body, zero overflow
• Vertical tab rail — right sidebar with single-click tabs: MLX / Memory / Schedule / Insights / Activity
• Square mesh graph viewport — Math.min(W, H) × 0.92 ensures the graph never stretches regardless of window shape
• Unified color palette — #08080f background, cyan/purple/green/yellow/red accent system consistent across every component
• No duplicates — CPU and RAM in KPI strip only; MLX tab focuses on inference-specific metrics

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Quick Start

git clone https://github.com/iriseye931-ai/mission-control-dashboard
cd mission-control-dashboard
docker-compose up --build

• Dashboard: http://localhost:3000
• Backend API: http://localhost:8000
• WebSocket: ws://localhost:8000/ws

No config required — the backend polls your local services and shows live status immediately.

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Manual Setup

Backend:

cd backend
pip install -r requirements.txt
uvicorn main:app --reload --host 0.0.0.0 --port 8000

Frontend:

cd frontend
npm install
npm run dev

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Connecting your agents

The backend polls your local services and broadcasts state over WebSocket. Push events directly from any agent via the REST API.

From Python (any agent):

import httpx

httpx.post("http://localhost:8000/api/event", json={
    "event_type": "progress_update",
    "source": "my-agent",
    "data": {"agent_id": "my-agent", "progress": 72, "task": "processing data"}
})

Via WebSocket (browser / any client):

const ws = new WebSocket("ws://localhost:8000/ws");
ws.onmessage = (e) => console.log(JSON.parse(e.data)); // full mesh state on every update

Event types:

Event	Payload	Use for
thought	{agent_id, content}	Agent reasoning steps
tool_call	{agent_id, tool, args}	Tool invocations
tool_result	{agent_id, tool, result}	Tool outputs
progress_update	{agent_id, progress, task}	Task progress 0–100
output_chunk	{agent_id, content}	Streamed output
task_assigned	{agent_id, task}	New task assignment
system_metric	{metric, value}	System-wide metrics

REST API:

GET  /api/health        — health check
GET  /api/agents        — active agents + status
GET  /api/system        — CPU, RAM, MLX RAM, PID
GET  /api/logs          — recent log buffer
GET  /api/cron          — Hermes scheduled jobs
GET  /api/memories      — recent memory recalls
GET  /api/amp/messages  — AMP messages from AI Maestro
GET  /api/amp/events    — live routing events from bridge logs
POST /api/amp/send      — send AMP message to any agent

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Stack

Layer	Tech
Frontend	React 19, Vite, TypeScript, Tailwind CSS, Zustand
Backend	FastAPI, uvicorn, WebSockets, Pydantic
Inference	MLX (Qwen3.5 35B-A3B 4-bit, Apple Silicon)
Deploy	Docker Compose (one command)

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Built for local AI meshes

This dashboard was built alongside the iriseye local AI mesh, and works with any similar setup:

Role	Examples
Lead agent	Claude Code (Atlas), any CLI agent
Task runner	Hermes, any cron-capable agent
File/web agent	OpenClaw / iriseye, browser-use
Memory store	OpenViking, mem0
Local LLM	MLX (Apple Silicon), Ollama, llama.cpp

The backend auto-detects running processes and polls local service endpoints — no instrumentation required to get a working dashboard.

Want the full mesh setup? → iriseye repo

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License

MIT