This Langflow flow implements a multi-step pipeline for generating research briefings from scientific papers retrieved via arXiv.

The workflow starts with a chat input or webhook that accepts a research topic, keyword set, or arXiv-style query string. A URL or API Request component calls the arXiv API (or compatible feeds) to search for the most relevant papers, returning metadata such as title, authors, abstract, publication date, and links to full-text PDFs.

The system then downloads or fetches the full text for selected papers using document loader components and parsing bundles that convert PDFs into clean, chunked text. Split Text components break long articles into semantically coherent sections, while Data Operations components normalize metadata so that each chunk is associated with its source paper, section, and citation details.

Next, an Embedding Model component generates vector representations for the chunks, and a vector store such as Chroma, Qdrant, or pgvector indexes them for similarity search. A Retrieval component surfaces the most relevant sections based on the user’s research question or desired angle for the briefing.

A Prompt Template and Language Model component then synthesize the retrieved content into a structured briefing. The instructions guide the model to produce sections such as Introduction, Background, Methods, Key Findings, Limitations, and Future Work, while preserving reference links and inline citations to original papers. A Structured Output component can enforce a JSON schema that separates each section and lists the underlying paper IDs used in the synthesis.

Finally, a Chat Output or API response node delivers the briefing back to the user or downstream systems. Optional formatting nodes can convert the structured output into Markdown, HTML, or PDF-ready text for direct inclusion in reports, wikis, or slide decks.

The flow can be extended using additional Langflow components to support richer research automation scenarios. You can add multiple source connectors (e.g., CrossRef, Semantic Scholar, PubMed, or institutional repositories) via API Request nodes and Smart Router components that adapt retrieval based on discipline. Scheduler or webhook integrations can run recurring briefings on topics of interest and post results directly to Slack, Notion, or email. Vector store bundles enable long-term storage of papers and embeddings so recurring queries get faster and more comprehensive over time, while Structured Output and Data Operations nodes can generate citation-ready bibliographies, comparison tables, and KPI dashboards for research leaders.