Agent Laboratory: automated code and study writing assistant for researchers

General Introduction

Agent Laboratory is an end-to-end autonomous research workflow designed to help researchers realize their research ideas. The system consists of specialized agents driven by large-scale language models that support the entire research workflow-from conducting literature reviews and developing plans to executing experiments and writing synthesis reports.Agent Laboratory is not designed to replace researchers' creativity, but rather to complement researchers' work by automating repetitive and time-consuming tasks such as coding Rather, Agent Laboratory complements the work of researchers by automating repetitive and time-consuming tasks such as coding and documentation, allowing them to focus on creativity and critical thinking. The system adapts to different computing resources and human inputs and is designed to accelerate scientific discovery and optimize research productivity.

An intelligent research assistant based on LLM: Agent Laboratory, which can assist in literature research, code writing, paper writing, automate repetitive work, so that you can focus on creativity and critical thinking. Two core capabilities, code assistant, help you to turn your research ideas into actual code, which will be automatically improved and optimized; writing assistant, automatic generation of academic paper format, and integration of experimental results to generate professional research reports. Writer's Assistant, which automatically generates academic paper formats, integrates experimental results, and generates professional research reports.

Function List

• Literature Review: Automatically collect and analyze relevant research literature to generate a review report.
• Experimental design: based on the research objectives to automatically generate the experimental plan, to ensure the scientific and rigorous research process.
• Experiment execution: Execute experiments with automated tools to collect and analyze data in real time.
• Report writing: automatically generate a detailed research report, including experimental methods, results and conclusions.
• Data management: automatic organization and storage of experimental data for subsequent review and analysis.
• Cost-benefit analysis: Provides cost and time analysis of experiments to help optimize the use of research resources.
• User Interface: Simple and intuitive user interface for easy operation and management by researchers.

Using Help

Agent Laboratory offers a full suite of automated research tools that are easy to use, and the following is a detailed guide to using them.

Installation of dependencies

1. Go to the project catalog:cd AgentLaboratory
2. Install the required Python dependencies:pip install -r requirements.txt

Configuration environment

1. Configure environment variables (if needed) according to the project requirements, refer to the project's README.md Documentation.
2. Start the local server:python manage.py runserver

Using the main functions

Literature review

1. Select the "Literature Review" function in the user interface.
2. Enter the research topic or keywords and the system will automatically collect relevant literature and generate a review report.

Experimental design

1. Enter the study objectives and parameters in the Design of Experiment screen.
2. The system generates a detailed experimental plan, including experimental steps, required materials and expected results.

Experimental execution

1. Upload the data required for the experiment and the system will automatically execute the experiment.
2. Data is collected and analyzed in real time during the experiment, and the results are presented in the form of graphs and data reports.

Report Writing

1. When the experiment is completed, select the "Report Writing" function.
2. The system will automatically generate a research report based on the experimental data, containing experimental methods, results and conclusions.

data management

1. All experimental data is automatically organized and stored in the cloud.
2. Users can access and download this data at any time for easy subsequent analysis and sharing.

Cost-benefit analysis

1. The system automatically records the time spent and cost of each experiment.
2. Users can view detailed analysis reports in the Cost-Benefit Analysis screen to optimize the use of research resources.