Agent Laboratory: Using LLM Agents as Research Assistants
What is the project about?
Agent Laboratory is an end-to-end autonomous research workflow system designed to assist human researchers in implementing their research ideas. It uses specialized agents powered by large language models (LLMs) to support the entire research process.
What problem does it solve?
The project aims to accelerate scientific discovery and optimize research productivity by automating repetitive and time-intensive tasks such as literature reviews, coding, experimentation, and report writing. It allows researchers to focus on ideation and critical thinking.
What are the features of the project?
- Autonomous Research Workflow: Guides the research process through three phases: Literature Review, Experimentation, and Report Writing.
- Specialized Agents: Employs different LLM-powered agents for specific tasks within each phase.
- External Tool Integration: Integrates tools like arXiv, Hugging Face, Python, and LaTeX.
- Customizable: Accommodates varying levels of computational resources and human involvement (co-pilot mode).
- Model Support: Currently supports OpenAI (o1, o1-preview, o1-mini, gpt-4o) and DeepSeek (deepseek-chat) models.
- Checkpointing: Allows loading previous saves to recover from interruptions.
- Multilingual Support: Can operate in languages other than English.
What are the technologies used in the project?
- Large Language Models (LLMs): OpenAI models, DeepSeek models.
- Python
- External APIs: arXiv, Hugging Face.
- LaTeX
What are the benefits of the project?
- Increased Research Productivity: Automates time-consuming tasks.
- Accelerated Scientific Discovery: Enables faster research cycles.
- Focus on High-Level Thinking: Frees researchers to concentrate on core research ideas.
- Flexibility: Adaptable to different resource constraints and levels of human intervention.
What are the use cases of the project?
- Assisting researchers in conducting literature reviews.
- Automating the process of designing and running experiments.
- Generating comprehensive research reports.
- Supporting researchers with varying levels of technical expertise.
- Speeding up the overall research workflow.
