antraft 1.2.0

Policy-enforced runtime for autonomous AI agents

Antraft

The Immune System for Agentic AI.

Antraft is an enterprise-grade runtime governance framework for AI agents. It serves as the Governance Layer that makes autonomous agents safe for production deployment.

Antraft acts as a middleware between your agent's "Brain" (LLM) and its "Hands" (Tools), enforcing specific policies, logging actions, and ensuring compliance. It is designed to be framework-agnostic, working seamlessly with LangChain, AutoGen, CrewAI, and custom implementations.

Antraft is the reference implementation of the MI9 Agent Intelligence Protocol.

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Key Capabilities

1. Robust Governance

Secure your agents with deterministic rules.

• Policy Enforcement: Approve, deny, or pause every action before execution.
• Stateful Control: Enforce prerequisites (e.g., "Must authenticate before reading data").
• Rich Logic Rules: define complex constraints like amount > 1000 and not user_verified.
• Path Confinement: Restrict file access to specific directories.

2. Deep Observability

Complete visibility into agent behavior.

• Session Recording: Capture full execution traces (Inputs, Thoughts, Actions, Outputs) for replay debugging.
• PII Redaction: Automatically scrub sensitive data (Emails, SSNs) from logs.
• Cognitive Telemetry: Log the agent's reasoning process, not just its actions.
• Drift Detection: Identify anomalous behavior patterns (loops, spikes) in real-time.

4. Distributed Control

Manage fleets of agents at scale.

• Swarm Protocol: Control thousands of agents across different servers from a central API.
• Human-in-the-Loop: Pause high-risk actions and wait for human approval via API.
• Dynamic Policies: Update agent permissions on-the-fly without restarting.

5. Universal Compatibility (MCP)

Antraft runs as a Model Context Protocol (MCP) server, making it instantly compatible with:

• Claude Desktop
• Cursor / Windsurf IDEs
• Any MCP-compliant client

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Installation

pip install antraft

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Quick Start ("Fluent" SDK)

The Antraft SDK provides a fluent interface to secure ANY python agent in seconds.

import asyncio
from antraft import Antraft

# 1. Define your Agent & Tools
agent = MyAgent()
tools = {
    "search": google_search_tool,
    "shell": run_shell_tool
}

async def main():
    # 2. Wrap & Secure
    await Antraft.guard(agent, tools) \
        .allow(["search"]) \
        .deny(["shell"]) \
        .record_session("logs/session_01.jsonl", redact_pii=True) \
        .run()

if __name__ == "__main__":
    asyncio.run(main())

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Advanced Usage

Defining Complex Policies (YAML)

For enterprise use cases, load policies from file.

# policy.yaml
allow:
  - read_file
rules:
  - trigger: "action:transfer_money"
    checks:
      - "amount > 10000"
    enforce: "pause"
    message: "High value transfer requires approval."

await Antraft.guard(agent, tools) \
    .load_policy("policy.yaml") \
    .run()

Swarm Mode (Remote Control)

Connect an agent to a central Control Plane.

1. Start the Server:

python -m antraft.cli.main serve

2. connect Agents:

await Antraft.guard(agent, tools) \
    .connect_swarm("http://localhost:8000") \
    .run()

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Documentation

• User Guide: Comprehensive "How-To".
• Architecture: System design and components.
• Threat Model: Security analysis.
• Syntax Guide: Configuration reference.

Example Project

• python examples/refund_agent/app.py: realistic support-refund workflow with HITL approval and session recording.

Development

python -m pytest -q

Contributing

We welcome contributions! Please see CONTRIBUTING.md for details.

License

MIT License