Academy

Modular and extensible middleware for deploying autonomous agents across federated research ecosystems, including HPC systems, experimental facilities, and data repositories.

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Key Features

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Asynchronous Execution

Built for high-performance scientific computing with support for asynchronous task execution and non-blocking operations across distributed resources.

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Heterogeneous Resources

Seamlessly integrates diverse computing resources including HPC clusters, experimental facilities, and cloud infrastructure.

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High-Throughput Data

Optimized for handling massive scientific datasets with efficient data flow management and streaming capabilities.

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Stateful Agents

Advanced abstractions for expressing intelligent agents with persistent state and memory across workflow executions.

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Inter-Agent Coordination

Sophisticated communication and coordination mechanisms enabling complex multi-agent scientific workflows.

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Experimental Integration

Direct integration with experimental control systems and scientific instruments for automated research workflows.

Use Cases

๐Ÿงช Materials Discovery

Deploy agents across multiple HPC systems to accelerate materials research through automated synthesis, characterization, and analysis workflows that adapt based on experimental results.

๐Ÿง  Decentralized Learning

Coordinate federated machine learning across distributed research institutions while maintaining data privacy and enabling collaborative model development.

๐Ÿ“š Information Extraction

Orchestrate intelligent agents to extract, process, and synthesize information from diverse scientific databases and literature repositories in real-time.

๐Ÿ”ฌ Autonomous Experimentation

Enable self-driving laboratories with agents that can design experiments, control instruments, analyze results, and iterate hypotheses automatically.

๐Ÿ“Š Scientific Workflow Automation

Automate complex scientific pipelines spanning multiple institutions, from data collection through analysis to publication-ready results.

๐ŸŒ Multi-Site Coordination

Coordinate research activities across multiple facilities, enabling large-scale collaborative science with intelligent resource allocation.

Getting Started

Deploy your first federated agentic workflow in minutes

# Install Academy
pip install academy-py
1

Define Your Agents

Create intelligent agents with custom behaviors using Academy's intuitive Python API and stateful agent abstractions.

2

Configure Resources

Set up connections to your HPC systems, data repositories, and experimental facilities using Academy's resource management layer.

3

Deploy & Scale

Launch your federated workflow across distributed resources with built-in coordination, monitoring, and fault tolerance.

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