grip-search

Agent-grade retrieval on top of grip. A question in, ranked cited passages out — by driving a real browser against real pages.

Distribution grip-search, import gripsearch.

from gripsearch import Retriever, BraveSource

async with Retriever(BraveSource(api_key=...)) as r:
    result = await r.search("how does asyncio.gather handle exceptions")

for p in result.passages:
    print(p.text)
    print(f"  — {p.url} {p.citation}")

for f in result.failures:      # blocked or unreachable sources, never silently dropped
    print("failed:", f)

What it claims

Not "faster than Tavily" — measured, it is 3–5x slower. Real page loads cost real seconds and no amount of engineering removes that.

• Same or lower cost. ~$0.025/query vs ~$0.049 for an equivalent Tavily path. The browser is not where the money goes; the LLM read is, and that is identical either way.
• Better reach. Measured, and it does not hold. Across 33 URLs, static fetch recovered the content on every page either arm could read — 23/23, gap zero, including SPAs, because documentation sites server-render for SEO. See evaluation/. Where static fetch fails it fails on blocking, and blocking defeats a headless browser too.
• Real citations. Every passage carries a URL plus a block id and heading trail, not just a document.

Pipeline

1. source.find(query, limit=8) → candidates
2. Fetch all concurrently through one grip Browser, 15s per page
3. Drop sources whose page_error is set — before spending a token on them
4. page.read() each survivor → citable blocks
5. BM25 rank across blocks, dedup near-duplicates
6. Return passages, with failures attached

Design notes

Discovery is rented, extraction is owned. CandidateSource has one method. BraveSource is the only real implementation — SERP scraping was measured and returns zero extractable results on every engine tried, so no scrape implementation ships. One that returned nothing would get reached for during an outage and fail silently.

Ranking is BM25, not embeddings. Embeddings add a model call and an index to a pipeline already dominated by the LLM read cost, and nothing measured says lexical ranking is the accuracy bottleneck. Revisit when a reach evaluation exists.

Failures are reported, never dropped. A blocked source appears in result.failures. If every source fails, search() raises NoUsableSources with them attached rather than returning an empty result that reads like "nothing found".

Stealth defaults on here, unlike grip. A retrieval layer fetching public pages has no reason to announce itself as automation; a general-purpose SDK does.

Not in v1

Interaction-to-reveal (pagination, "show more", infinite scroll), proxies, answer synthesis, caching, MCP/HTTP surface. See the spec for why.