Zero Api Key Web Search

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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:

1. Search the web with zero API keys (DuckDuckGo by default, self-hosted SearXNG for cross-validation, optional Bright Data for production SERP + Web Unlocker).
2. 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.
3. 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

Stage	Question asked (one forward pass per item)	Effect
--laya on search/context	"Is this result relevant and informative for the query?"	drops noise before it burns context tokens
--laya on verify/report	"Does this text support the claim?" + "Does it state or imply the claim is false?"	replaces regex conflict markers and keyword-overlap cutoffs with calibrated probabilities

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

Subfolder	Backbone	Best at
root (default)	ModernBERT-large 421M	English text
multilingual	mmBERT-base 322M	100+ languages, ~2× faster
typed-decisions	ModernBERT-large 421M	workflow-tuned decisions

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_cache included
• 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-threshold against 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.