Antares Python Client

Antares Python Client

A high-level Python library for integrating Antares simulations into real-time AI, control, and monitoring systems.

Antares Python Client

🧠 What is the Antares Python Client?

The Antares Python Client is a high-level, developer-friendly library that allows you to interact with the Antares simulation engine directly from Python. Designed for modern simulation workflows, it provides a seamless interface to control simulations, add dynamic entities (like ships), and stream simulation events in real time.

Whether you’re prototyping autonomous decision-making systems, testing radar algorithms, or integrating simulations into command dashboards, antares-python makes it possible - and remarkably simple.

⚙️ Why Python?

Python is the language of choice for:

  • AI agents (e.g., LLM-based decision-making)
  • Operational dashboards (e.g., with Dash, Streamlit, or Flask)
  • System integration (e.g., connecting simulations to ROS, Kafka, or MQTT)
  • Algorithm prototyping (e.g., sensor fusion, trajectory prediction)

With antares-python, you can embed Rust-powered realistic, high-frequency maritime simulations directly into those environments — enabling closed-loop testing, ML training, or control logic validation at scale.

📦 Python Integration in 30 Seconds

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from antares.client import AntaresClient
from antares.models.ship import LineShip

client = AntaresClient()

client.add_ship(LineShip(
    initial_position=(30.1, -77.3),
    angle=1.2,
    speed=6.0
))

async for track in client.subscribe():
    threat = tactical_ai.evaluate(track)
    if threat.level > 0.8:
        system.launch_countermeasure(track.position)

➡️ Spawn a ship and react to its telemetry in real time. Combine with tactical logic or AI systems for autonomous decisions.

🛰️ Use Cases in Autonomous & Military Systems

Antares Python Client acts as a simulation substrate for intelligent systems. Key applications include:

  • ✅ Closed-loop testing Simulate dynamic threats and assess how guidance, targeting or evasion algorithms respond under realistic conditions.

  • ✅ Live inputs for AI systems Stream real-time data into LLM-based copilots or tactical assistants to enhance battlefield decision support.

  • ✅ Dashboard integration Feed positional and behavioral data into command UIs for situational awareness, mission rehearsal, or red team analysis.

  • ✅ Synthetic data generation Generate diverse and labeled simulation data at scale to train models for classification, tracking, or anomaly detection.

  • ✅ System validation under uncertainty Test robustness of autonomous systems against unpredictable scenarios, sensor degradation, or adversarial tactics.

🌍 Designed for Integration

You can easily:

  • Start simulations programmatically
  • Add agents or environmental entities dynamically
  • Receive live data via TCP (compatible with asyncio, threading, or any event loop)
  • Configure everything via .env or .toml

Antares Python Client has zero external dependencies beyond its own models and tools, ensuring it’s lightweight and portable — ideal for secure or isolated environments.

🛡️ Robust. Modular. Battle-Ready.

Whether you’re building mission rehearsal tools, tactical planning apps, or AI-controlled defense simulations — antares-python offers a powerful, production-ready foundation for integration, testing, and innovation.

🔗 Learn More

Full technical details, API reference, and source code are available at the GitHub repository.