Transform Scientific Software into AI Assistants — 3 Steps to Intelligent Transformation
The Bohrium platform introduces the bohr-agent-sdk Scientific Agent Development Kit, enabling AI systems to truly execute professional scientific tasks and helping developers quickly build their own specialized research agents. Through a three-step process — Invoking MCP Tools, Orchestrating Agent Workflows, and Deploying Services — any scientific software can be rapidly transformed into an AI assistant.
With a decorator pattern, just a few annotations can quickly transform scientific computing programs into MCP standard services. Built-in application templates turn scattered research code into standardized, reusable intelligent components.
Supports mainstream Agent open frameworks including Google ADK, Langraph, and Camel, providing flexible choices for developers familiar with different technology stacks.
Dual-mode architecture supports seamless transition between development and production. Local environments enable rapid iteration and feature validation, while Bohrium's cloud GPU clusters handle production-grade computing tasks. The SDK automatically manages the complete workflow of task scheduling, status monitoring, and result collection, with built-in file transfer mechanisms for handling large-scale data uploads and downloads. Developers focus on core algorithm implementation while infrastructure management is fully automated.
Based on the modern React framework, deploy fully-featured web applications with one click. Built-in 3D molecular visualization engine supports multiple structure formats and rendering modes for interactive molecular structure display. Real-time data synchronization ensures instant computing status updates, while multi-session management supports parallel task processing. Integrated with enterprise-grade features including file management, project switching, and permission control. Transform command-line tools into professional visual applications, significantly enhancing user experience and tool usability.
Powerful scientific computing task management and monitoring platform
Modern web application interface providing intuitive user experience
pip install bohr-agent-sdk -i https://pypi.org/simple --upgrade# Get calculation project template
dp-agent fetch scaffolding --type=calculation
# Get device control project template
dp-agent fetch scaffolding --type=device
# Get configuration file
dp-agent fetch configLab Mode Development Example
from typing import Dict, TypedDict
from dp.agent.device.device import Device, action, BaseParams, SuccessResult
class TakePictureParams(BaseParams):
"""Picture taking parameters"""
horizontal_width: str # Image horizontal width
class PictureData(TypedDict):
"""Picture data structure"""
image_id: str
class PictureResult(SuccessResult):
"""Picture taking result"""
data: PictureData
class MyDevice(Device):
"""Custom device class"""
device_name = "my_device"
@action("take_picture")
def take_picture(self, params: TakePictureParams) -> PictureResult:
"""
Execute picture taking action
Through the @action decorator, automatically register this method as an MCP standard service
"""
hw = params.get("horizontal_width", "default")
# Execute actual device control logic
return PictureResult(
message=f"Picture taken with {self.device_name}",
data={"image_id": "image_123"}
)Cloud Mode Development Example
"""
MCP protocol-based cloud device control example
"""
import signal
import sys
from dp.agent.cloud import mcp, get_mqtt_cloud_instance
from dp.agent.device.device import TescanDevice, register_mcp_tools
def signal_handler(sig, frame):
"""Graceful shutdown handling"""
print("Shutting down...")
get_mqtt_cloud_instance().stop()
sys.exit(0)
def main():
"""Start cloud services"""
print("Starting Tescan Device Twin Cloud Services...")
# Register signal handler
signal.signal(signal.SIGINT, signal_handler)
# Create device instance
device = TescanDevice(mcp, device)
# Automatically register device tools to MCP server
# register_mcp_tools implements automatic registration through Python introspection
register_mcp_tools(device)
# Start MCP server
print("Starting MCP server...")
mcp.run(transport="sse")
if __name__ == "__main__":
main()# Local lab environment
dp-agent run tool device
# Cloud computing environment
dp-agent run tool cloud
# Scientific calculation mode
dp-agent run tool calculation
# Start agent (with Web UI)
dp-agent run agent --config
# Debug mode
dp-agent run debugAfter running dp-agent fetch scaffolding, you'll get a standardized project structure:
your-project/
├── lab/ # Lab mode
│ ├── __init__.py
│ └── tescan_device.py # Device control implementation
├── cloud/ # Cloud mode
│ ├── __init__.py
│ └── mcp_server.py # MCP service implementation
├── calculation/ # Calculation mode
│ └── __init__.py
├── .env # Environment configuration
└── main.py # Main program entry
Configure necessary environment variables in the .env file:
# MQTT connection configuration
MQTT_INSTANCE_ID=your_instance_id
MQTT_ENDPOINT=your_endpoint
MQTT_DEVICE_ID=your_device_id
MQTT_GROUP_ID=your_group_id
MQTT_AK=your_access_key
MQTT_SK=your_secret_key
# Computing resource configuration
BOHRIUM_USERNAME=your_username
BOHRIUM_PASSWORD=your_passwordNote: The dp-agent fetch config command automatically downloads configuration files and replaces dynamic variables (such as MQTT_DEVICE_ID). For security reasons, this feature is only available in internal network environments.
- Materials Science Computing: Molecular dynamics simulation, first-principles calculations
- Bioinformatics Analysis: Gene sequence analysis, protein structure prediction
- Laboratory Equipment Control: Intelligent control of research equipment such as electron microscopes and X-ray diffractometers
- Data Processing Workflows: Automated data cleaning, analysis, and visualization
- Machine Learning Training: Model training, hyperparameter optimization, result evaluation
# Upload files to cloud
dp-agent artifact upload <path>
# Download cloud files
dp-agent artifact download <artifact_id>The SDK provides real-time task status monitoring, supporting:
- Task queue management
- Computing resource scheduling
- Automatic result collection
- Exception handling and retry mechanisms