Mantis Configure

Pipeline Configurator (/mantis-configure)

System Goal

Environment and Model Configurator. Configures workflow.json with appropriate sandbox execution mechanisms, AI model providers, API endpoints, and credential bindings. Provides instantaneous preflight verification to guarantee that LLM credentials and sandbox isolation requirements are fully operational before launching security review campaigns.

Command Definition

• Command: /mantis-configure
• Description: Configures Mantis pipeline settings (sandboxes, models, credentials, preflight validation) in workflow.json.
• Execution Command:

  # From reference/ directory:
  python3 scripts/configure.py [flags...]
  
  # From repository root:
  python3 reference/scripts/configure.py [flags...]
  

• CLI Options:
  • --sandbox / -s: Sandbox mechanism (static-only, gvisor, microsandbox, gce).
  • --model / -m: Default LLM model (e.g. gemini-3.7-flash, vertex_ai/claude-opus-5, vertex_ai/zai_org/glm-5.2-maas, openai/{MODEL_ID}).
  • --api-base: Custom endpoint URL for OpenAI-compatible LLM servers (e.g. http://localhost:8000/v1).
  • --reasoning-effort: Reasoning effort level (low, medium, high).
  • --timeout: LLM request timeout in seconds.
  • --project / -p: GCP Project ID (for GCE sandbox or Vertex AI routing).
  • --zone / -z: GCP Zone (e.g. us-central1-b).
  • --image / -i: Sandbox image name (e.g. mantis-sandbox-image or mantis-sandbox:latest).
  • --subnet: GCE Subnet name (e.g. mantis-isolated-subnet).
  • --workdir: Sandbox guest workdir (default: /workspace).
  • --workflow / -w: Path to workflow.json (defaults to auto-discovery).
  • --db / -d: Path to SQLite knowledge database (default: knowledge.db).
  • --auto: Auto-detects host capabilities and configures optimal settings automatically.
  • --save: Explicitly saves configuration changes to workflow.local.json.
  • --save-tracked / --global: Saves configuration changes directly to base workflow.json.
  • --interactive: Interactive step-by-step terminal wizard.
  • --test / --preflight: Executes fast (1-2s) validation tests verifying LLM reachability and sandbox readiness.
  • --show: Displays current configuration and diagnostic status.
  • --dry-run: Simulates configuration changes without modifying files.
  • --update-nodes: Updates all agent nodes in workflow.json to use the specified default model.
  • --json: Outputs configuration status and preflight diagnostics in JSON.

Supported Sandbox Mechanisms

Supported Model Providers

1. Gemini Models (Google / Vertex AI):
   • gemini-3.7-flash, gemini-3.5-flash-lite
   • vertex_ai/gemini-3.7-flash, vertex_ai/gemini-3.5-flash-lite
2. Claude Models (Vertex AI Model Garden):
   • vertex_ai/claude-opus-5
3. MaaS & Open Source Models (Vertex Model Garden):
   • vertex_ai/zai_org/glm-5.2-maas
4. Custom OpenAI-Compatible Endpoints:
   • openai/{MODEL_ID} or vertex_ai/openai/{MODEL_ID}
   • Supports custom --api-base (e.g. vLLM, Ollama, LiteLLM proxy), --reasoning-effort, and --timeout.

Common CLI Workflows

1. Fast Preflight Verification (~1s)

Check if LLM credentials and sandbox requirements are operational:

python3 reference/scripts/configure.py --test

2. Auto-Detect and Configure

Automatically inspect host capabilities (/dev/kvm, docker/runsc, gcloud) and select the best available sandbox:

python3 reference/scripts/configure.py --auto

3. Switch to Static Analysis (Zero Dependencies)

python3 reference/scripts/configure.py --sandbox static-only

4. Configure gVisor Container Sandbox

python3 reference/scripts/configure.py --sandbox gvisor --image mantis-sandbox:latest

5. Configure GCE Ephemeral Cloud Sandbox

python3 reference/scripts/configure.py --sandbox gce --project my-gcp-project --zone us-central1-b

6. Switch AI Model to Claude or Custom Endpoint

# Vertex AI Claude
python3 reference/scripts/configure.py --model vertex_ai/claude-opus-5

# Custom Local vLLM / OpenAI server
python3 reference/scripts/configure.py --model openai/my-model --api-base http://localhost:8000/v1

7. Interactive Configuration Wizard

python3 reference/scripts/configure.py --interactive

Python API Reference

When invoked programmatically from Python:

from scripts.configure import (
    detect_capabilities,
    ensure_configured,
    ensure_configured_async,
    is_default_or_unconfigured,
    run_preflight_checks,
    run_preflight_checks_async,
    update_workflow_config,
)

# 1. Check if configuration contains default placeholders
is_unconf, issues = is_default_or_unconfigured(config)

# 2. Run fast preflight checks (sync or async)
ok, messages = run_preflight_checks(config, test_llm=True, test_sandbox=True)
# or: ok, messages = await run_preflight_checks_async(config)

# 3. Ensure configured (auto-resolves defaults if unconfigured)
valid_config = ensure_configured(auto=True)
# or: valid_config = await ensure_configured_async(auto=True)

Input/Output Contract

• Reads:
  • workflow.json and optional workflow.local.json overlay
  • Host environment (virtualization devices, container engines, cloud CLI credentials)
• Writes:
  • workflow.local.json (or workflow.json when --save-tracked is set)