Detailed Introduction
AgentLaboratory is an end-to-end autonomous research workflow framework designed to help researchers organize hypotheses, experiments and reproducible pipelines. The project focuses on composing multiple agent components, tools, and data flows into research pipelines to reduce manual repetitive work and provide logging and traceability for reproducibility and verification.
Main Features
- Workflow-first: break research into composable tasks and steps with automation and retry policies.
- Agent collaboration: support parallel or sequential cooperation between agent components for complex experiment strategies.
- Reproducibility: automatically record inputs, seeds, and execution logs for experiment replication.
- Extensible plugin model: easy integration with external tools, data sources, and evaluation modules.
Use Cases
- Rapid idea validation: encapsulate validation experiments into workflows and run batched experiments.
- Automated benchmarking: run evaluations across models, datasets, and parameter sweeps in parallel.
- Reproducible experiment reports: capture full execution traces for papers or internal audits.
- Teaching and demos: show how to engineer and scale research methodology.
Technical Features
AgentLaboratory is implemented in Python and designed as a modular, extensible framework. It leverages task orchestration and messaging to manage interactions between agents and provides runtime controls such as retries, timeouts, and concurrency limits. The project is open-source under the MIT license, enabling reuse and customization for research and industry projects.