Sift — deterministic website indexing for grep-first LLM agents
Project description
sift crawls a site into local markdown + structured facts your agent greps, reads, and cites — files on disk, not vectors, read over MCP. Every page is content-hashed and dated, so answers trace back to the exact source and snapshot. Re-run anytime; only changed pages are refetched. Self-hosted.
❌ Without sift
Your agent's built-in web-fetch gives you:
- Stale answers — the provider's crawler cached that page weeks ago
- Partial answers — just the one page it happened to land on
- Unprovable answers — no record of what the source said, or when
✅ With sift
- Always fresh — you control the crawl; conditional GETs refetch only what changed, and
changed_sincelets your agent pull just the delta instead of re-reading the corpus - Provable — content-hash + date on every page; cite the source, hash, and snapshot, read any past snapshot (
as_of), or emit a self-containedproveinclusion proof a third party verifies offline (python -m sift.verify_proof) without trusting the server — optionally anchored by an RFC-3161 timestamp so the date is witnessed by a third party, not self-asserted - Complete & grep-native — the whole site on disk, not a handful of retrieved snippets
- Self-hosted — any
http(s)site you can reach, public or internal; your data stays yours
Get started
Requires Python 3.11+.
🤖 Let your coding agent set it up — recommended
Paste one prompt into Claude Code, Cursor, Codex, or any MCP-aware agent. It explains sift, asks which site to index, then installs the engine, builds an index, wires up MCP, and shows you how to query it — end to end.
📋 Copy the one-paste setup prompt
Set up **sift** in this project — a deterministic, content-hashed website indexer that
serves a verifiable markdown corpus to AI agents over MCP (https://github.com/dvlshah/sift).
Do the steps in order. Needs Python 3.11+.
0 — EXPLAIN, THEN ASK ME (do this BEFORE installing anything)
First, explain sift to me in 4–6 plain-English lines — assume I've never heard of it:
• WHAT it is: it crawls a whole website/docs site into local markdown files (plus structured
facts) that you, the agent, can grep / read / cite — files on disk, not a vector database.
• CORE FEATURES: (1) complete — the full site, not the few pages a live web-fetch happens to
land on; (2) verifiable — every page is content-hashed + dated, so any answer can be proved
back to the exact source and snapshot; (3) always-current — re-running the crawl refetches
only what changed; (4) read over MCP — you query it with provenance, read-only by default.
• WHY IT'S NEEDED: a one-off scrape gives you 3 pages with no proof of what they said or when.
sift keeps an agent correct about an evolving body of docs AND able to prove what it cited.
(If someone only needs one page once, sift is the wrong tool — a plain fetch is fine.)
Then ASK ME: "Which website or docs site do you want to index locally?" — wait for my answer
and use it as TARGET_SITE everywhere below. Do not run any commands until I answer.
1 — INSTALL THE ENGINE
```bash
pip install sift-engine # adds the `sift`, `sift-mcp`, `sift-evals` commands
sift --version && which sift-mcp # confirm it's on PATH
```
2 — INSTALL THE SIFT SKILL INTO THIS REPO
Download the skill so you (the agent) know how to build, operate, and query an index.
Read .claude/skills/sift/SKILL.md after downloading — it is the source of truth for the rest.
```bash
SKILL=https://raw.githubusercontent.com/dvlshah/sift/v0.3.0/.claude/skills/sift
mkdir -p .claude/skills/sift/reference
curl -fsSL $SKILL/SKILL.md -o .claude/skills/sift/SKILL.md
for f in cli config mcp-tools; do curl -fsSL $SKILL/reference/$f.md -o .claude/skills/sift/reference/$f.md; done
```
3 — BUILD A SMALL STARTER INDEX (using the TARGET_SITE I gave you in step 0)
Write a sift.toml (generic profile + host allow-list = the host of TARGET_SITE), then build a
capped, publishable smoke-test index:
```bash
cat > sift.toml <<'TOML'
[site]
profile = "sift.sites.generic:GenericProfile"
[seed]
host_allow = ["HOST_OF_TARGET_SITE"] # e.g. docs.example.com — derive from TARGET_SITE
TOML
sift init --root ./sift-index
sift seed --root ./sift-index --config sift.toml --from-domain TARGET_SITE
sift run --root ./sift-index --config sift.toml --limit 25 --coverage-base planned
sift verify --root ./sift-index --skip-signature
```
Drop `--limit 25 --coverage-base planned` for a full crawl once extraction looks good.
Preview exactly which URLs a crawl would touch — and how many — *without fetching a
byte*: `sift discover --from-domain TARGET_SITE` (writes nothing; emits JSON).
No `sitemap.xml` (or an incomplete one)? After a run, `sift seed --from-frontier`
extracts in-scope links from the fetched pages and crawls one hop deeper each pass.
Hardened / bot-blocked host (Cloudflare/Akamai/Imperva) → add `--impersonate-fallback`
(free, TLS-fingerprint impersonation; `pip install 'sift-engine[impersonate]'`).
JS-rendered SPA → `pip install 'sift-engine[browser]' && python -m playwright install chromium`,
then add `[browser]\nenabled = true` (it joins the ladder as a free render tier).
JS-rendered SPA that has an official API (eCFR, CVE.org, FederalRegister …) → a
profile's `api_url(url)` fetches the robots-allowed API instead of the shell —
cleaner and cheaper than a browser, and the citation stays the human page (see
the bundled `sift.sites.cve:CVEProfile`).
Still blocked (JS-challenge edges) → `--firecrawl-fallback` (paid; needs FIRECRAWL_API_KEY).
These compose into one escalation ladder: native → impersonate → browser → Firecrawl.
sift respects `robots.txt` `Disallow` at seed by default; set `[crawl]` →
`respect_robots = false` only for sources you have permission to index.
4 — WIRE THE READ-ONLY MCP SERVER
Use the ABSOLUTE path to ./sift-index. Add this to the project's .mcp.json (Claude Code / Cursor / Codex):
```json
{ "mcpServers": { "sift": { "command": "sift-mcp", "args": ["--root", "ABSOLUTE/PATH/TO/sift-index"] } } }
```
Claude Code shortcut: `claude mcp add sift -- sift-mcp --root "$(pwd)/sift-index"`
Then restart the MCP client so the `sift` tools load.
5 — SHOW ME THE QUERY LOOP
Call the `snapshot_status` tool to confirm the index is published, then explain the loop:
snapshot_status first → grep_corpus to locate → read_md / read_facts to drill in →
cite source_url + content_hash + fetched_at. Mention that re-running `sift run` refreshes the
index, and that the /sift skill covers building, operating, and querying in depth.
(TARGET_SITE = the site I name when you ask in step 0, e.g. https://docs.example.com)
🧑 Or do it yourself
pip install sift-engine
# the ATO sitemap below uses a bundled profile, so this runs with zero config
sift init --root ./index
sift seed --root ./index --from-sitemap https://www.ato.gov.au/sitemap.xml
sift run --root ./index --limit 25 --coverage-base planned # smoke-test first
sift verify --root ./index --skip-signature
sift-mcp --root ./index # serve the index to your agent
Then point your agent at it (use an absolute path):
{
"mcpServers": {
"sift": { "command": "sift-mcp", "args": ["--root", "/abs/path/to/index"] }
}
}
Indexing your own site? Add a sift.toml (generic profile + host allow-list) — see Configuration.
How it works
seed → plan → fetch → extract → commit → publish
Five idempotent phases. publish runs 5 verification gates, then atomically swaps the current/ snapshot and writes a Merkle root over every page hash. Deterministic: same input → same content_hash → same Merkle root — so any reader re-verifies a page in O(1), or the whole snapshot end-to-end with sift verify.
Your agent reads the published snapshot read-only over MCP: snapshot_status → grep_corpus → read_md / read_facts → cite source + hash + date.
Docs
- CLI reference — every command and flag
- Configuration & site profiles —
sift.tomland theSiteProfilecontract - MCP tools — parameters, output caps, multi-index mode
- Corpus format & integrity contract — on-disk layout and what each read tool returns
- Contributing · Security
Open core, Apache-2.0. This repo is the full open-source engine (pipeline + MCP server) and runs standalone. A hosted platform built on it is in development.
Status — v0.3.0; tests green on Python 3.11–3.13. Adds proof-carrying answers (prove / verify-proof + an RFC-3161 timestamp anchor), time-travel reads (changed_since, diff_md, as_of over retained snapshots), API acquisition/content transports (api_url source + json extract), robots + SSRF + bot-challenge hardening, digital-PDF tables, a recursive link frontier, and sift discover (read-only pre-crawl preview). Known limits: no run-dir GC yet, stdout-only logging, stdio-only MCP transport. Issues & roadmap → GitHub Issues.
License
Apache-2.0 — © 2026 Deval Shah.
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