Human-in-the-loop approval for every command your AI agents execute. Policy engine. Audit trail. Zero trust by default.
Every command flows through a secure approval pipeline before touching your system.
┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ AI Agent │────▶│ /plan API │────▶│ Pending │ │ (AutoGen, │ │ (FastAPI) │ │ Queue │ │ Claude, │ └─────────────┘ └──────┬──────┘ │ Custom) │ │ └─────────────┘ ▼ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ Audit │◀────│ Policy │◀────│ Human UI │ │ Log │ │ Engine │ │ (Approve) │ └─────────────┘ └─────────────┘ └─────────────┘
Every layer designed to keep humans in control of AI agent actions.
Every command queued for human review before execution. No autonomous access to your system — ever.
26 deny rules block dangerous operations. Allow patterns match command intent. Deny-first, category-based filtering.
Every command logged with timestamp, result, duration, and exit code. JSON-line audit log for compliance and forensics.
Python SDK and PowerShell modules. Native AutoGen integration. Build custom agents with one function call.
Server-Sent Events for live run status. Watch commands queue, execute, and complete in real time.
Working directory restrictions and path validation. File API jailed to project root. No escape to system directories.
Integrate Provara into any Python agent with a single import.
from provara import plan
# Queue a command for human approval
result = plan(
command="Get-Process python",
note="Checking running Python processes",
timeout_s=30
)
print(f"Queued: {result['pending_id']}")
# → Queued: 20250213_143022_a1b2c3d4
# Command waits for human approval before executing Open source. MIT licensed. Deploy in minutes.