AkariAsai/OpenScholar

Health v2 · maintenance only

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1584 stars · 167 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

OpenScholar is a retrieval-augmented language model system from the Allen Institute for AI that answers scientific questions by searching the literature and generating responses grounded in cited papers. The repository provides inference code, training code for a Llama 3.1 8B model, and retriever tooling for offline and online retrieval over scientific corpora.

Use cases

• answer scientific questions with citations to real papers
• synthesize findings across millions of scientific articles
• build a retrieval-augmented generation pipeline over a research corpus
• host a retrieval server for online literature search
• fine-tune Llama 3.1 8B on scientific QA data
• literature review assistant for researchers

When to choose

• you need citation-grounded answers to scientific queries rather than generic LLM output
• you want to run or study a full open RAG stack including retriever, inference, and training code
• you are building research tools that must stay current with fast-moving scientific literature

When to avoid

• you need a polished end-user product rather than research code you assemble yourself
• you lack GPU resources or API keys for the underlying language models and retrieval infrastructure
• your domain is not scientific literature and you need a general-purpose RAG framework

Facets

application · maturity active

rag llm-inference llm-training search-engine nlp large-language-models artificial-intelligence data-science python cli scientific-literature retrieval-augmented-generation llama-3.1 citation-grounded-answers research-assistant allenai scholarqa natural-language-processing search web-server gpu docker

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markdown · JSON · MCP: product_card(name="AkariAsai/OpenScholar")