Colandr vs Moara

Colandr is a free web platform for evidence reviews and other document reviews and syntheses. Machine learning ranks citations by relevance to help screen titles and abstracts, while natural language processing classifies included documents into user-defined categories and assists full-text data extraction. Reviewers retain the final inclusion, exclusion, and classification decisions, and teams can collaborate and cross-check work. Citation files can be imported in TXT, RIS, or BIB formats, with a 40 MB limit per import, and screening decisions and extracted data can be exported as CSV. Colandr does not connect directly to citation databases; users export citations elsewhere, although the platform can help create Boolean search strings. Full-text papers must be found and imported individually, with no mass-upload feature. Relevance rankings update after every 10 screened citations, and metadata suggestions begin after 50 hand-coded extractions. Colandr describes itself as open-source and open-access.

Moara is a web-based AI research platform for literature reviews, supporting work from discovery through evidence synthesis. Researchers can search, organize, screen, and extract evidence in one workspace, then use its Agent to summarize papers, identify themes and gaps, compare findings, and answer research requests. It supports narrative and systematic reviews, including workflows aligned with PRISMA and Cochrane guidelines. Moara says its database contains more than 200 million scholarly papers across disciplines. Users can import PDFs, RIS, NBIB, TXT, or ZIP files and connect Zotero and LibKey. Teams can assign papers, share annotations, and follow review progress in real time. The Free plan allows up to 50 papers per review library. Core is $15/mo billed annually; Pro is $40/mo billed annually. Moara states that stored data is encrypted with AES-256 and data in transit with TLS 1.2 or higher, with internal access controls and role-based permissions. It says it does not sell personal data or use personal information for advertising.