Description
SciFlow is a comprehensive AI assistant designed to streamline the entire research process, from literature review to submission. It operates as a local-first, single-machine deployment, ensuring that all research assets remain on your device. The tool utilizes a multi-agent pipeline orchestrated by LangGraph, which automates tasks such as literature review, writing, quality control, and submission assistance. SciFlow supports various citation styles and offers features like Markdown editing, KaTeX formula rendering, and multi-version snapshots.
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The platform is built on a React and Vite frontend, connected to a NestJS backend, with data stored in SQLite. It is designed for single-user local deployment, with no multi-tenant or third-party cloud uploads. SciFlow can integrate with OpenAI-compatible models, allowing users to input their own API keys and model configurations.
SciFlow's quality control includes a seven-dimensional scoring system that evaluates literature, logic, citations, language, innovation, charts, and formatting. The tool also offers structured review feedback and submission tracking, including journal matching and cover letter generation. Additionally, it features an experimental sandbox for executing Python code locally, with resource limits and timeout configurations.
The platform is open-source under the MIT license, allowing users to freely use, modify, and distribute the software while retaining copyright notices. SciFlow is ideal for researchers looking to automate and enhance their workflow without relying on external cloud services.
- CategoryAI Research Assistants
- Pricing ModelOpen Source
- DeploymentLocal
- PlatformsSingle Machine
SciFlow's Core Features
Local-first deployment
Multi-agent pipeline
Markdown editing
Citation style support
Quality control scoring
Submission tracking
OpenAI model integration
Experimental sandbox
How to use SciFlow?
Configure: Set up your research theme and model configurations
Use: Automate literature review, writing, and quality control
Optimize: Track submissions and refine research outputs
Deploy: Run locally with SQLite and no external dependencies
SciFlow's Use Cases
- Literature Review
- Research Writing
- Quality Control
- Submission Tracking
- Python Experimentation
