5-Minute Quick Start

Experience NeoMind's core loop — device ingress → data visualization → AI conversation — as fast as possible. Each step has a ✓ checkpoint and troubleshooting tips.

For full installation options and troubleshooting see Install & Setup.

What You'll Achieve

By the end of this guide you will have:

✅ A NeoMind service running locally
✅ A large language model connected (local or cloud)
✅ Your first device connected and pushing data via webhook
✅ Live data visible on a dashboard
✅ Asked the AI a question in natural language and gotten an answer

Prerequisites

A macOS / Windows / Linux machine (4 GB+ RAM)
No need to pre-install databases, message brokers, or any other infrastructure — everything is built into NeoMind
For a local LLM (recommended): an extra 4–8 GB RAM; otherwise you can use a cloud API

Step 1: Install (1 min)

Option A: Desktop App (recommended)

Download the installer for your platform from GitHub Releases, double-click to install, then launch.

Option B: Server Deploy

curl -fsSL https://raw.githubusercontent.com/camthink-ai/NeoMind/main/scripts/install.sh | sh

After startup, open http://localhost:9375 in your browser.

✓ Checkpoint: you see the login / register page = the server is running. Register an account and log in.

Can't see the page?

Port in use? Default port is 9375; you can change it in the config file
macOS security block? First launch shows "cannot verify developer" — go to System Settings → Privacy & Security → Open Anyway
Server without a browser? Use SSH port forwarding: ssh -L 9375:localhost:9375 user@server

Step 2: Configure LLM Backend (1 min)

On first login you'll enter the setup wizard. NeoMind needs an LLM backend as its "brain" — pick one of three options:

Option	Best for	Latency	Privacy	Requires
Ollama (local)	Recommended, offline use	Low	Never leaves LAN	8 GB+ RAM
Cloud API	Most powerful models	Medium	Data goes to cloud	API Key
Skip for now	Just look around	—	—	—

Option A: Ollama Local (recommended)

# 1. Install Ollama (if you haven't): https://ollama.com
# 2. Pull the model
ollama pull qwen3.5:4b

In the wizard:

Backend type → Ollama
URL → http://localhost:11434 (default)
Model → qwen3.5:4b

Option B: Cloud API

Pick OpenAI / Anthropic / GLM etc., enter your API Key and model name (e.g. gpt-4o, claude-sonnet-4-6).

✓ Checkpoint: the wizard shows "LLM backend connected" = the brain is ready.

Connection failed?

Ollama not running? Start it with ollama serve in a terminal
Model not pulled? Run ollama list to confirm qwen3.5:4b exists
Cloud 401? Check that your API Key is valid and has credit

See Configure LLM Backend.

Prefer the CLI?

Skip the UI — one command does it:

# Ollama local
neomind llm create --name local --type ollama --endpoint http://localhost:11434 --model qwen3.5:4b

# Cloud API (GLM example)
neomind llm create --name glm --type openai \
  --endpoint https://open.bigmodel.cn/api/paas/v4 \
  --model glm-4-flash --api-key YOUR_API_KEY

# Test connection → set as default
neomind llm test local && neomind llm activate local

Full command reference: neomind llm --help

Step 3: Connect Your First Device (1 min)

The fastest ingress method is HTTP Webhook — no MQTT client needed, a single curl simulates a device.

3.1 Create the device

In the Web UI Devices page → click Add Device → select Webhook → name it demo-sensor.

After creation you'll get a dedicated Webhook URL (like /api/devices/<DEVICE_ID>/webhook).

3.2 Push data

Copy the command below, replace <DEVICE_ID> with your device's ID, and run it:

curl -X POST http://localhost:9375/api/devices/<DEVICE_ID>/webhook \
  -H 'Content-Type: application/json' \
  -d '{"temperature": 25.6, "humidity": 60}'

A {"success": true} response means it worked. Open the device detail page to see the latest telemetry values.

✓ Checkpoint: the device detail page shows temperature: 25.6 and humidity: 60 = data is in the database.

Connect real devices too

NeoMind has a built-in MQTT broker (localhost:1883) that supports real devices like ESP32, Raspberry Pi, industrial sensors, and more. See Onboard Devices.

webhook returned 404?

Wrong device ID? Click into the device in the device list — the ID is in the URL
Forgot Content-Type? You must include -H 'Content-Type: application/json'

Step 4: See Data on the Dashboard (30 sec)

Go to the Dashboard page — a default dashboard is auto-created. Click Edit, then add a Value Card widget:

Click Add Widget → choose Value Card
Data source → device:demo-sensor:temperature
Save

DataSourceId Format

The unified data source reference format is {type}:{id}:{field}:

device:demo-sensor:temperature — device telemetry
extension:weather:temp — extension metric
agent:guard:status — agent status

Dashboards, rules, and data pushes all use this format. See the Glossary.

The temperature value appears live on the card. Push another webhook payload and watch it update instantly:

curl -X POST http://localhost:9375/api/devices/<DEVICE_ID>/webhook \
  -H 'Content-Type: application/json' \
  -d '{"temperature": 28.3, "humidity": 55}'

✓ Checkpoint: you see a live temperature reading on the dashboard = visualization loop is working.

See Use Dashboard.

Step 5: Ask AI Chat (30 sec)

Open AI Chat and type:

What devices do I have? What's the temperature of demo-sensor?

The AI Agent will query the device list and latest telemetry, then answer in natural language.

Now try something more ambitious — let the AI create an automation for you:

Notify me when temperature exceeds 30

The AI will understand your intent, create a rule, and configure a notification channel.

✓ Checkpoint: the AI answered your question = the intelligence loop is complete.

AI not responding?

LLM backend not connected? Go back to Settings → LLM Config and check status
First reply is slow? Local models need to warm up on first inference — 10–20 seconds is normal

See AI Chat.

🎉 Core Loop Complete

Here's the pipeline you just verified end-to-end:

Every link is verified: data comes in, gets stored, is visualized, and can be queried.

Next Steps

Congratulations! You've completed the NeoMind core loop. From here:

I want to...	Go to
Understand the architecture	Core Concepts — process model, data flow, extension mechanism
Look up a term	Glossary — Device / Extension / Agent / Rule
Configure more LLM backends	Configure LLM Backend — Ollama / cloud API
Connect real devices	Onboard Devices — MQTT / BLE / Webhook
Automate with AI Agents	AI Agent — scheduled / event-triggered autonomous agents
Set up automation rules	Automation Rules — threshold alerts / linked control
Configure notifications	Notifications — email / Slack / Telegram / Webhook
Install extensions (YOLO/OCR)	Extensions — install and configure vision AI extensions
See an end-to-end example	Use Cases — full object detection solution
Troubleshoot issues	Troubleshooting — common problems and solutions

Last updated: 2026-06-15

What You'll Achieve​

Step 1: Install (1 min)​

Option A: Desktop App (recommended)​

Option B: Server Deploy​

Step 2: Configure LLM Backend (1 min)​

Option A: Ollama Local (recommended)​

Option B: Cloud API​

Step 3: Connect Your First Device (1 min)​

3.1 Create the device​

3.2 Push data​

Step 4: See Data on the Dashboard (30 sec)​

Step 5: Ask AI Chat (30 sec)​

🎉 Core Loop Complete​

Next Steps​