Why Zero-API-Key Web Search (Jev-Powered)
An agent that searches the raw web pays for everything it reads: context tokens, latency, and attention spread over SEO noise. Zero-API-Key Web Search puts a sieve between search and the agent:
- Search the web with zero API keys (DuckDuckGo by default, self-hosted SearXNG for cross-validation, optional Bright Data for production SERP + Web Unlocker).
- Sieve the results through Laya — an open-weight (Apache-2.0), non-autoregressive decision model that scores every result for relevance in a single forward pass (~35 ms on GPU, 100–330 questions/sec batched) with calibrated probabilities. It never generates text, so there is nothing to parse and nothing to hallucinate.
- Verify claims with probabilistic stance classification (support / conflict / neutral per source) instead of pure keyword matching, then emit citation-ready evidence reports.
Every stage degrades gracefully: no Laya installed? The pipeline falls back to the lexical heuristic verifier and still works — Laya is an accelerant, not a dependency.
query ──► providers ──► cross-validate ──► ┌─────────┐ ──► LLM context pack
│ LAYA │ (citations only)
│ SIEVE │
noise ───────────────────►└─────────┘ ──► verify_claim
(dropped pre-context) (probabilistic stance)
30-Second Setup
pip install zero-api-key-web-search # free search, no API key, no model download
zero-search "Python 3.13 release" --json
zero-verify "Python 3.13 is the latest stable release" --deep --json
zero-report "Python 3.13 stable release" --claim "Python 3.13 is the latest stable release" --json
Add the Sieve (Optional, ~800 MB, Fully Local)
pip install "zero-api-key-web-search[laya]" # pulls laya + torch
# Relevance-filter every search result before it reaches your agent
zero-search "react state management" --laya --laya-threshold 0.6 --json
# Probabilistic stance verification (laya-stance-v1 model)
zero-verify "Python 3.13 is the latest stable release" --laya --json
# Both, in the flagship evidence report
zero-report "gpt-5 release" --claim "GPT-5 is released" --laya --deep --json
On Apple Silicon it runs on MPS; on NVIDIA GPUs on CUDA; anywhere on CPU (slower but works).
Behind a network that can't reach Hugging Face? export HF_ENDPOINT=https://hf-mirror.com.
What Laya does inside the pipeline
The verification blend becomes 0.55 × neural stance + 0.35 × source quality + 0.10 × freshness (vs. the lexical model's 0.45 × keyword overlap + …). The result payload keeps the full lexical sidecar so you can compare both classifiers on your own data.
Checkpoints
zero-verify "..." --laya --laya-subfolder multilingual # non-English claims
MCP Server (8 tools)
{
"mcpServers": {
"zero_api_key_web_search": {
"command": "zero-mcp",
"args": []
}
}
}
search_web, llm_context, browse_page, verify_claim, evidence_report, list_providers, clear_cache, setup_providers — the first four accept "laya": true and "laya_threshold" so a Claude/Cursor/any-MCP agent can opt into the sieve per call.
Provider Paths: Free to Production
Path 1 — Free (zero configuration). DuckDuckGo, no account:
zero-search "Python 3.13 release" --json
Path 2 — Free cross-validated. Self-hosted SearXNG for dual-provider corroboration (see docs/searxng-self-hosted.md):
./scripts/start-searxng.sh
export ZERO_SEARCH_SEARXNG_URL="http://127.0.0.1:8080"
zero-search "AI regulation" --profile free-verified --json
Path 3 — Production SERP. Bright Data for 7 engines (Google, Bing, DuckDuckGo, Yandex, Baidu, Yahoo, Naver), geo-targeting, structured results:
zero-setup # interactive wizard
zero-search "news" --provider brightdata --engine google --type news --region us-en --json
Path 4 — Production + Web Unlocker. 403/429/CAPTCHA/geo-blocked pages auto-retried through the Web Unlocker on zero-browse.
Engineering
- Circuit breaker per provider (3 consecutive failures → 60 s cooldown)
- Response cache for searches and pages,
clear_cacheincluded - Async + threaded provider fan-out; goggles-style reranking presets (
docs-first,research,news-balanced) - Claim decomposition for compound statements, sub-claim verdicts
- Page-aware verification (
--deep): fetch top pages and re-score - Graceful degradation everywhere: Laya missing → lexical verifier; provider down → next provider; page blocked → unlocker; unlocker off → snippet-only
- 111 tests passing (
python -m pytest tests/ -q)
Honest Limits
Read docs/trust-model.md before trusting any verdict. The short version:
- The lexical verifier (
evidence-aware-heuristic-v3) is pattern-matching, not entailment — it is transparent and fast, and blind to paraphrase, negation scope, and mismatched numbers. Laya mode (laya-stance-v1) fixes the class of failure, not the possibility of failure. - Laya ships over-confident. Upstream measured mean ECE 0.466 → 0.081 only after per-domain temperature refitting. Our defaults (threshold 0.5) are reasonable, not calibrated — sweep
--laya-thresholdagainst a small labeled set from your domain before production use. - Laya's base checkpoints are a fast base to specialise, not a zero-shot oracle (upstream's own typed-decisions benchmark: 0.362 zero-shot vs 0.766 fine-tuned; XNLI-style tasks are much stronger). For high-stakes domains, fine-tune the open checkpoint on your workflow and drop it in via
--laya-model/--laya-subfolder. - High-cardinality option sets (>20 options per question) are Laya's weak spot; Zero-API-Key Web Search only asks 1–2-option yes/no questions per item, which stays in its comfort zone.
- Verification quality still depends on what search returns: no web footprint →
insufficient_evidence, by design.
Attribution
The Jev-powered sieve integration uses Laya by Convai Innovations (Apache-2.0, open weights) — an open Jev-class, non-autoregressive decision model. Search providers, the verification pipeline, the MCP server and the CLI are original to this project (MIT).
License
MIT — see LICENSE.
