Zero-API-Key Web Search

⚡ Jev-Powered ⚡ — free neural search & evidence verification for AI agents.

Zero API keys. MCP-ready. Batch-filter, rerank and verify evidence locally at 100+ decisions/sec with an open Jev-class decision model.

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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](https://huggingface.co/convaiinnovations/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 ```bash 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) ```bash 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 | ```bash zero-verify "..." --laya --laya-subfolder multilingual # non-English claims ``` ## MCP Server (8 tools) ```json { "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: ```bash 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](docs/searxng-self-hosted.md)): ```bash ./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](https://get.brightdata.com/h21j9xz4uxgd) for 7 engines (Google, Bing, DuckDuckGo, Yandex, Baidu, Yahoo, Naver), geo-targeting, structured results: ```bash 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](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](https://huggingface.co/convaiinnovations/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](LICENSE).