wd041216-bit/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.
## 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
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.