WHSearch

AI-native research search engine built as a lightweight modular monolith.

Current phase

All phases implemented: foundation, multi-provider web discovery (DuckDuckGo + Wikipedia fan-out), robots-gated reader, passage retrieval, adaptive research planning with stopping conditions, claim-level evidence with source-independence and contradiction checks, MCP search agent (search, read_page, search_and_read, research), bounded SQLite/FTS5 local index, and explicit research budgets.

Design rules

• Evidence first: retrieve sources and passages before generating research conclusions.
• Online first: use external discovery initially; keep persistent storage bounded.
• Respect robots.txt, access policies, and conservative per-domain rate limits.
• Domain models and protocols must not depend on HTTP clients, providers, MCP, or extractors.
• MCP is an adapter layer; research/search logic stays in application modules.
• Optional integrations must not be required for importing the core domain.
• Resource limits are explicit so the system remains usable on low-memory machines.

Planned phases

1. Foundation: contracts, configuration, logging, testing, architecture checks. Done.
2. Web discovery: provider abstraction and DuckDuckGo discovery. Done (+Wikipedia).
3. Web reader: robots policy, fetching, extraction, metadata, passages. Done.
4. Retrieval: passage ranking and deduplication. Done.
5. Research: adaptive query planning and stopping conditions. Done.
6. Evidence: claims, source independence, contradictions, verification. Done.
7. Search agent: orchestration through the MCP tools. Done.
8. Local index: SQLite/FTS5 bounded cache and reusable evidence. Done.
9. Autonomous research budgets and larger-scale discovery. Done (budgets + fan-out).

Development

Use the repository virtual environment when available:

.venv/bin/python -m pytest
.venv/bin/python -m compileall -q src tests
.venv/bin/ruff check src tests

The quality gate must pass before moving to the next phase.

Install as an MCP server

Requires Python >= 3.12. After the whsearch package is published to PyPI, no manual install is needed — uvx fetches and runs it on first use:

{
  "mcpServers": {
    "whsearch": { "command": "uvx", "args": ["--from", "whsearch[mcp]", "whsearch"] }
  }
}

Alternatives:

uv tool install "whsearch[mcp]" && whsearch   # persistent install via uv
pipx install "whsearch[mcp]" && whsearch      # persistent install via pipx
pip install -e ".[mcp]" && whsearch           # from source

WHSEARCH_INDEX_PATH enables the persistent local index.

License

GPL-3.0-or-later, see LICENSE. Copyright (C) 2026 WHSearch contributors. Per-file copyright holder names were intentionally left generic; update them to your name before publishing if you are the sole author.