First Run

What you’ll learn

• How to run the interactive setup wizard
• What each wizard step configures
• How to send your first classification query
• How to start the server and access the web dashboard

Step 1: Run the Setup Wizard

The setup wizard scans your hardware, suggests which modules to enable, and writes a profile to your data directory.

nabaos setup

Expected output:

Scanning hardware...

=== Hardware Report ===
  CPU:    8 cores (x86_64)
  RAM:    15.6 GB total, 11.2 GB available
  Disk:   128 GB free
  GPU:    None detected
  Docker: Available (v24.0.7)

=== Suggested Modules ===
  [x] core
  [x] web
  [ ] voice (disabled)
  [ ] browser
  [x] telegram
  [ ] latex
  [ ] mobile
  [ ] oauth

Saving suggested profile (interactive selection coming soon).
Profile saved to /home/you/.nabaos/profile.toml

The wizard does four things:

1. Scans hardware – detects CPU, RAM, disk, GPU, and whether Docker is available. This determines which modules your machine can comfortably run.

2. Suggests modules – enables core (always on), web (dashboard), and telegram (if a bot token is present). Disables resource-heavy modules like voice and browser if your hardware is constrained.

3. Writes the profile – saves the module configuration. You can edit this file later by hand or re-run nabaos setup.

4. Downloads models – if the ONNX model files are not already present, they are fetched on first use.

If you want to skip prompts and accept the suggested profile automatically:

nabaos setup --non-interactive

Step 2: Set Your LLM Provider

NabaOS needs at least one LLM API key for Tier 3 (cheap LLM) and Tier 4 (deep agent) requests. Cached requests (Tiers 0-2.5) never call an LLM.

Export your API key:

# Anthropic (default)
export NABA_LLM_PROVIDER=anthropic
export NABA_LLM_API_KEY=sk-ant-api03-your-key-here

# Or OpenAI
export NABA_LLM_PROVIDER=openai
export NABA_LLM_API_KEY=sk-your-key-here

Add these lines to your ~/.bashrc or ~/.zshrc so they persist across terminal sessions.

Step 3: Quick Test – Classify a Query

Run the classifier to verify that the ONNX model is loaded and working:

nabaos admin classify "check my email"

Expected output:

Query:      check my email
Intent:     check|email
Action:     check
Target:     email
Confidence: 94.2%
Latency:    4.7ms

The classifier maps natural language to a structured W5H2 intent (action + target) in under 5 ms, entirely on your local machine with no API call.

Try a few more:

nabaos admin classify "summarize this PDF"
nabaos admin classify "what is the weather in Tokyo"
nabaos admin classify "schedule a meeting with Alice tomorrow"

Step 4: Run the Full Pipeline

The ask command runs a request through the complete pipeline:

nabaos ask "check my email"

Expected output (first run – classification hit):

=== Tier 1: BERT Classification ===
Intent:     check|email
Confidence: 94.2%
Latency:    4.7ms
(Stored in fingerprint cache for future instant lookup)

=== Constitution Check ===
Enforcement: Allow
Allowed:     YES

=== Intent Cache MISS ===
No cached execution plan for 'check|email'. Would route to LLM.

Run the same query again to see the fingerprint cache in action:

nabaos ask "check my email"

Expected output (second run – Tier 0 hit):

=== Tier 0: Fingerprint Cache HIT ===
Intent:     check|email
Confidence: 94.2%
Latency:    0.031ms

The second run resolves in under 0.1 ms because the exact query was cached as a fingerprint hash on the first run. No model inference, no API call.

Step 5: Start the Server

The start command runs the scheduler loop, the Telegram bot (if configured), and the web dashboard (if configured) as background services.

Set a password for the web dashboard:

export NABA_WEB_PASSWORD=your-secure-password

Start the server:

nabaos start

Expected output:

Starting NabaOS...
[start] NABA_TELEGRAM_BOT_TOKEN not set -- Telegram bot disabled.
[start] Starting web dashboard on http://127.0.0.1:8919...

Step 6: Access the Web Dashboard

Open your browser and navigate to:

http://localhost:8919

Log in with the password you set in NABA_WEB_PASSWORD. The dashboard shows:

• Pipeline status – cache hit rates, classification latency, active agents.
• Cost tracker – daily and monthly LLM spend, savings from caching.
• Query history – recent requests, which tier resolved them, and latency.
• Constitution – active rules and enforcement decisions.

What to Do Next