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Freephdlabor

Freephdlabor is an open-source Python multi-agent framework that coordinates scientific research tasks, including hypothesis generation, experimentation, figures, citations, review and preparation of publication-ready manuscript drafts.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Freephdlabor — OpenViST project profile in Research Agents.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a scientific project needs a coordinated sequence of ideation, experimentation, review and writing rather than a single conversational response.

Who it is for

  • Researchers automating computational research workflows
  • Developers building domain-specific autonomous research labs
  • Teams creating research assistants that combine experiments with manuscript preparation

Documented uses

  • Generate and refine scientific research ideas
  • Coordinate specialized agents across a research workflow
  • Run documented computational experiments through agent tools
  • Prepare figures, citations and scientific manuscript drafts
  • Adapt the workflow with domain-specific tools and agent configurations
Before you choose

Requirements & limits

The repository is public and described as open source under the MIT license. The documented setup requires Python 3.11+, Conda and API keys for selected LLM providers; a CUDA-compatible GPU is recommended for experiments that require computation.

  • The framework is customizable through its agents, prompts, tools and model configuration.
  • Its documented agents cover management, ideation, experimentation, writing, reviewing and proofreading.
  • Domain adaptation requires defining suitable domain-specific tools or finding compatible tools from other repositories.
  • Computational experiments may require GPU resources in addition to model-provider access.
  • The output is a research workflow and manuscript draft, so scientific results still require human assessment.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up Freephdlabor for a research task

Install the documented Python framework and prepare its model configuration for a supplied research direction.

Before you start

  • Python 3.11 or later
  • Conda environment manager
  • API keys for selected LLM providers
  • CUDA-compatible GPU recommended for computational experiments

Steps

  1. Clone the repository and change into its directory.

  2. Create and activate the Conda environment from environment.yml.

  3. Add the required provider keys to the .env file.

  4. Optionally adjust model selection in .llm_config.yaml.

  5. Start the documented entry point with a research task using the --task parameter.

Expected outcome

A configured Freephdlabor installation receives a research idea or direction through its multi-agent entry point.

Guide limitations

  • The documentation identifies GPU use as recommended for experiments that require computation.
  • The setup requires API keys for the chosen LLM providers.
  • This recipe covers the documented setup and launch stages, not independent validation of research outputs.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
4 October 2026
Original website title
ltjed/freephdlabor · Official homepage title · retained in its source language
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