macha-autonomous/EXAMPLES.md

Macha Autonomous System - Configuration Examples

Basic Configurations

Conservative (Recommended for Start)

services.macha-autonomous = {
  enable = true;
  autonomyLevel = "suggest";  # Require approval for all actions
  checkInterval = 300;        # Check every 5 minutes
  model = "llama3.1:70b";     # Most capable model
};

Moderate Autonomy

services.macha-autonomous = {
  enable = true;
  autonomyLevel = "auto-safe";  # Auto-fix safe issues
  checkInterval = 180;          # Check every 3 minutes
  model = "llama3.1:70b";
};

High Autonomy (Experimental)

services.macha-autonomous = {
  enable = true;
  autonomyLevel = "auto-full";  # Full autonomy
  checkInterval = 300;
  model = "llama3.1:70b";
};

Monitoring Only

services.macha-autonomous = {
  enable = true;
  autonomyLevel = "observe";  # No actions, just watch
  checkInterval = 60;         # Check every minute
  model = "qwen3:8b-fp16";    # Lighter model is fine for observation
};

Advanced Scenarios

Using a Smaller Model (Faster, Less Capable)

services.macha-autonomous = {
  enable = true;
  autonomyLevel = "auto-safe";
  checkInterval = 120;
  model = "qwen3:8b-fp16";  # Faster inference, less reasoning depth
  # or
  # model = "llama3.1:8b";  # Also good for simple tasks
};

High-Frequency Monitoring

services.macha-autonomous = {
  enable = true;
  autonomyLevel = "auto-safe";
  checkInterval = 60;  # Check every minute
  model = "qwen3:4b-instruct-2507-fp16";  # Lightweight model
};

Remote Ollama (if running Ollama elsewhere)

services.macha-autonomous = {
  enable = true;
  autonomyLevel = "suggest";
  checkInterval = 300;
  ollamaHost = "http://192.168.1.100:11434";  # Remote Ollama instance
  model = "llama3.1:70b";
};

Manual Testing Workflow

1. Test with a one-shot run:

# Run once in observe mode
macha-check

# Review what it detected
cat /var/lib/macha-autonomous/decisions.jsonl | tail -1 | jq .

2. Enable in suggest mode:

services.macha-autonomous = {
  enable = true;
  autonomyLevel = "suggest";
  checkInterval = 300;
  model = "llama3.1:70b";
};

3. Rebuild and start:

sudo nixos-rebuild switch --flake .#macha
sudo systemctl status macha-autonomous

4. Monitor for a while:

# Watch the logs
journalctl -u macha-autonomous -f

# Or use the helper
macha-logs service

5. Review proposed actions:

macha-approve list

6. Graduate to auto-safe when comfortable:

services.macha-autonomous.autonomyLevel = "auto-safe";

Scenario-Based Examples

Media Server (Let it auto-restart services)

services.macha-autonomous = {
  enable = true;
  autonomyLevel = "auto-safe";  # Auto-restart failed arr apps
  checkInterval = 180;
  model = "llama3.1:70b";
};

Development Machine (Observe only, you want control)

services.macha-autonomous = {
  enable = true;
  autonomyLevel = "observe";
  checkInterval = 600;  # Check less frequently
  model = "llama3.1:8b";  # Lighter model
};

Critical Production (Suggest only, manual approval)

services.macha-autonomous = {
  enable = true;
  autonomyLevel = "suggest";
  checkInterval = 120;  # More frequent monitoring
  model = "llama3.1:70b";  # Best reasoning
};

Experimental/Learning (Full autonomy)

services.macha-autonomous = {
  enable = true;
  autonomyLevel = "auto-full";
  checkInterval = 300;
  model = "llama3.1:70b";
};

Customizing Behavior

The config file lives at:

/etc/macha-autonomous/config.json (auto-generated from NixOS config)

To modify the AI prompts:

Edit the Python files in systems/macha-configs/autonomous/:

• agent.py - AI analysis and decision prompts
• monitor.py - What data to collect
• executor.py - Safety rules and action execution
• orchestrator.py - Main control flow

After editing, rebuild:

sudo nixos-rebuild switch --flake .#macha
sudo systemctl restart macha-autonomous

Integration with Other Services

Example: Auto-restart specific services

The system will automatically detect and propose restarting failed services.

Example: Disk cleanup when space is low

Monitor will detect low disk space, AI will propose cleanup, executor will run nix-collect-garbage.

Example: Log analysis

AI analyzes recent error logs and can propose fixes based on error patterns.

Debugging

See what the monitor sees:

sudo -u macha-autonomous python3 /nix/store/.../monitor.py

Test the AI agent:

sudo -u macha-autonomous python3 /nix/store/.../agent.py test

View all snapshots:

ls -lh /var/lib/macha-autonomous/snapshot_*.json
cat /var/lib/macha-autonomous/snapshot_$(ls -t /var/lib/macha-autonomous/snapshot_*.json | head -1) | jq .

Check approval queue:

cat /var/lib/macha-autonomous/approval_queue.json | jq .

Performance Tuning

Model Choice Impact:

Model	Speed	Capability	RAM Usage	Best For
llama3.1:70b	Slow (~30s)	Excellent	~40GB	Complex reasoning
llama3.1:8b	Fast (~3s)	Good	~5GB	General use
qwen3:8b-fp16	Fast (~2s)	Good	~16GB	General use
qwen3:4b	Very Fast (~1s)	Moderate	~8GB	Simple tasks

Check Interval Impact:

• 60s: High responsiveness, more resource usage
• 300s (default): Good balance
• 600s: Low overhead, slower detection

Memory Usage:

• Monitor: ~50MB
• Agent (per query): Depends on model (see above)
• Executor: ~30MB
• Orchestrator: ~20MB

Total continuous overhead: ~100MB + model inference when running

Security Considerations

The autonomous user has sudo access to:

• systemctl restart/status - Restart services
• journalctl - Read logs
• nix-collect-garbage - Clean up Nix store

It CANNOT:

• Modify arbitrary files
• Access user home directories (ProtectHome=true)
• Disable protected services (SSH, networking)
• Make changes without logging

Audit trail:

All actions are logged in /var/lib/macha-autonomous/actions.jsonl

To revoke access:

Set enable = false and rebuild, or stop the service.

Future: MCP Integration

You already have MCP servers installed:

• mcp-nixos - NixOS-specific tools
• gitea-mcp-server - Git integration
• emcee - General MCP orchestration

Future versions could integrate these for:

• Better NixOS config manipulation
• Git-based config versioning
• More sophisticated tooling

Stay tuned!