OpenScholar
Best for scientific research
Open-source model
Retrieval-augmented AI
Academic-focused
Free to use
Synthesize scientific literature with an open, retrieval-augmented language model
Assistant
Research
OPEN_SOURCE
ADVANCED Complexity
HYBRID
Last updated: Apr 15, 2026
openscilm.allen.ai
Quick Answer
OpenScholar is a fully open, retrieval-augmented language model from Ai2 and the University of Washington, designed to synthesize scientific literature. It can answer scientific questions, summarize papers, and find prior work by leveraging 108M+ abstracts and 12M+ full-text papers. Best for researchers and academics. Free and open source.
Decision Intelligence
Best For
Synthesizing scientific literature for researchers
Answering scientific questions for academics
Summarizing research papers for students
Exploring algorithms for computer scientists
Not Ideal For
General knowledge queries outside of scientific domains
Users without technical skills to deploy models locally
Real-time conversational AI for non-research tasks
Generating creative content or marketing copy
Fit Score
Complexity
ADVANCED
Company Size
SOLO
SMALL
MEDIUM
ENTERPRISE
Team Size
INDIVIDUAL
SMALL_TEAM
LARGE_TEAM
Skill Level
INTERMEDIATE
EXPERT
About OpenScholar
OpenScholar is a fully open, retrieval-augmented language model developed by Ai2 and the University of Washington. It synthesizes over 108M abstracts and 12M full-text papers to answer scientific questions. Users can download the model weights, training data, and retrieval index for free.
What Makes OpenScholar Unique
OpenScholar is a fully open, retrieval-augmented language model with publicly available model weights, training data, and retrieval index, allowing for complete transparency and customizability in scientific literature synthesis.
Capabilities
Synthesize scientific literature
Answer scientific questions
Retrieve information from 108M+ abstracts
Retrieve information from 12M+ full-text papers
Summarize research papers
Identify prior work on a topic
Downloadable model weights and training data
Retrieval-augmented language model
Task Categories
Pros
- +Fully open-source model, weights, and data for transparency
- +Retrieval-augmented for accurate scientific synthesis
- +Access to a vast collection of 108M+ abstracts and 12M+ full-text papers
- +Developed by a reputable research institution (Ai2)
Limitations
- -Requires technical knowledge to download and utilize the full collection
- -No clear web application for direct interaction beyond basic queries
- -Focuses solely on scientific literature, not general knowledge
- -Limited direct human-in-the-loop features for refinement
Frequently Asked Questions
Your Actions
Dimension Scores
Ease of Use3/10
Output Quality8/10
Value for Money10/10
Customization7/10
Support5/10
Integration1/10
Task Scores
Knowledge Synthesis
9
Literature Search
8
Paper Summarization
8
Data Extraction
7
Cross Reference
7
Hypothesis Generation
6
Methodology Guidance
5
Peer Review Support
4
Trust Score
35/100
Limited Data
Based on 4 verified signals
Trust Signals
Verified CompanyAllen Institute for AI
Fundingsignificant funding, including $152 million from Nvidia and the National Science Foundation
Team Size201-500
Data Residency
Switching & Migration
Migration Difficulty
Moderate Effort
Data portable as:
EXCEL
Feedback form
Quick Stats
Agent TypeAssistant
Memorystateless
Planningbasic
CategoryResearch
PlatformsWEB, API, CLI
Views1
Reviews0
LaunchedNov 2024
Setup Time1 hour+
Status
active