AI Research Stack

Two open-source tools, built together — from crawling the open web to publishing governed, auditable research.

ai-web-research

MIT

A deterministic, robots-aware AI web crawler & research agent

Crawls and monitors the web with legal and ethical guardrails built in from the start — reads and honors robots.txt, respects each site's Crawl-delay and rate limits, and blocks SSRF targets before every fetch. On top of that foundation: LLM-based semantic extraction with source-quote verification, a Divergence–Resonance–Compression research loop, and an MCP server exposing every capability to compatible agents.

Python 3.11+uvCrawl4AIhttpxSQLiteMCP Server
git clone https://github.com/kakon77777-commits/ai-web-research
cd ai-web-research
uv venv && uv pip install -e .
View on GitHub

Veritaxa Workbench

MIT

The first stable CHSA reference runtime for governed research

Takes evidence — from a crawler like ai-web-research, or any authorized source — through a governed pipeline: normalized Evidence IR, stable Evidence Spans, candidate Claims, governed Claim Review, evidence invalidation with dependency propagation, and deterministic, portable Research Bundles. Four boundaries hold throughout: Extraction ≠ Evidence, Evidence ≠ Verified Claim, Approved Claim ≠ Universal Truth, Recompilation ≠ Automatic Republication.

Python 3.11+SQLiteXLSX Governance WorkbenchCLIOptional MCP Gateway
git clone https://github.com/kakon77777-commits/Veritaxa
cd Veritaxa
python release/v1.0/materialize.py --output-dir build --extract --clean
cd build/source/veritaxa_workbench_v1_0 && pip install -e . --no-build-isolation
View on GitHub

How they connect

ai-web-research is the Source Adapter — legally-scoped, rate-limited fetching and extraction. Veritaxa is the governance layer on top of it — turning fetched evidence into reviewed, versioned, publishable research. Both speak the same Model Context Protocol, so they connect without custom glue code.