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bytedance/pasa

PaSa -- an advanced paper search agent powered by large language models. It can autonomously make a series of decisions, including invoking search tools, reading papers, and selecting relevant references, to ultimately obtain comprehensive and accurate results for complex scholarly queries. observed · 2026-08-28

github.com/bytedance/pasa · Python · Apache-2.0 (permissive) observed · 2026-08-28

Health v2 · maintenance only

31/100

  • Activity 23
  • Release rhythm 35
  • Longevity 44

Flags: no_releases

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 618
  • days_rel: n/a
  • days_push: 463
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

1649 stars · 122 forks observed · 2026-08-28

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

PaSa is an LLM-powered paper search agent from ByteDance that autonomously invokes search tools, reads papers, and selects relevant references to answer complex scholarly queries. It consists of two 7B agents (Crawler and Selector) trained with reinforcement learning on the synthetic AutoScholarQuery dataset, and is also available as a hosted demo.

Use cases

  • find papers for a detailed academic research query
  • comprehensive literature search beyond Google Scholar
  • search for papers cited within related work sections
  • filter collected papers for relevance to my research question
  • run an autonomous research assistant for scholarly queries
  • evaluate LLM agents on academic search benchmarks

When to choose

  • you need comprehensive recall on complex or fine-grained scholarly queries
  • you want an open-source, self-hostable alternative to Google Scholar or GPT-based search
  • you need a research artifact with trained models and datasets available on Hugging Face

When to avoid

  • you need general web search rather than academic paper search
  • you lack GPU resources to run 7B models locally and don't want to use the hosted demo
  • you need a production-grade search service with SLAs

Facets

application · maturity active

agent-framework search-engine llm-inference rag machine-learning artificial-intelligence large-language-models python self-hosted academic-paper-search llm-agent reinforcement-learning scholarly-search crawler-agent acl-2025 ai-agents search natural-language-processing linux docker

1 source

Member repositories

RepositoryRoleHealth v2
bytedance/pasamain31

For agents

markdown · JSON · MCP: product_card(name="bytedance/pasa")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem