wigolo

---

wigolo runs on your machine as an MCP server and gives an AI coding agent one durable surface for everything web-related — search, fetch, crawl, extract, cache, find-similar, research, and autonomous gather loops. The core tools need no API keys, nothing it touches leaves ~/.wigolo/, and there's no bill that grows with how much your agent thinks.

Quickstart

Requires Node ≥ 20 and ~1.5 GB of free disk. macOS, Linux, and Windows.

One command installs the local engine (search, browser, on-device models), auto-wires it into your agent, and sets up the MCP connection:

npx wigolo init --non-interactive --agents=<your-agent>

• <your-agent> — one or more of claude-code · cursor · codex · gemini-cli · vscode · windsurf · zed · antigravity (comma-separated). wigolo writes the MCP config and instructions for you — nothing else to set up.
• Any other MCP-capable agent? Omit --agents — the engine still installs headlessly, and you point your agent at wigolo's MCP server (npx wigolo mcp) yourself.

That's the whole setup — search, fetch, crawl, extract, cache, and find-similar work with no API key. Check it's healthy:

npx wigolo doctor

Not for you? npx wigolo config --uninstall --yes removes everything, cleanly.

Manual MCP setup (any other agent)

The --agents flag has a built-in installer for each agent listed above — but it can't cover every agent in the world. For anything else — your own custom or in-house agent, or any MCP-capable client we don't wire automatically yet — set wigolo up by hand: it's just another MCP server. Install the engine once, then register it:

npx wigolo init --non-interactive        # engine only: models, browser, cache — no agent wiring

Most clients use an mcpServers block in a JSON config file:

{
  "mcpServers": {
    "wigolo": {
      "command": "npx",
      "args": ["-y", "wigolo"]
    }
  }
}

wigolo with no subcommand starts the MCP stdio server (that is the default). If you installed it globally, use "command": "wigolo", "args": [] instead.

The file location — and the exact key — vary by client:

Agent	Config file	Servers key
Cursor	~/.cursor/mcp.json	mcpServers
Windsurf	~/.codeium/windsurf/mcp_config.json	mcpServers
Gemini CLI	~/.gemini/settings.json	mcpServers
Antigravity	~/.antigravity/mcp.json	mcpServers
VS Code	user mcp.json (Command Palette → MCP: Open User Configuration)	servers
Zed	~/.config/zed/settings.json	context_servers
Claude Code	(no file) run claude mcp add wigolo --scope user -- npx -y wigolo (--scope user = global; drop it for project-only)	—
Codex	~/.codex/config.toml (TOML, not JSON)	[mcp_servers.wigolo]
Any other	wherever it registers MCP servers	its MCP-servers key

Codex uses TOML instead of JSON:

[mcp_servers.wigolo]
command = "npx"
args = ["-y", "wigolo"]

To enable answer synthesis (below) for a hand-wired agent, add the provider and key to the server's env:

{
  "mcpServers": {
    "wigolo": {
      "command": "npx",
      "args": ["-y", "wigolo"],
      "env": { "WIGOLO_LLM_PROVIDER": "gemini", "GEMINI_API_KEY": "<your-free-key>" }
    }
  }
}

Let an AI set it up for you

Setup is simple enough to hand off to an AI. Ask your coding agent (Claude Code, Cursor, …) — or any chat assistant (ChatGPT, Claude, Gemini) — to do it, and it can follow the steps above. Paste a prompt like:

Set up the wigolo MCP server for my agent. wigolo is a local-first MCP server installed with npx wigolo init --non-interactive (engine only — no API keys). Then register it in my agent's MCP config as an mcpServers entry { "command": "npx", "args": ["-y", "wigolo"] }. Note the per-client differences: VS Code uses the servers key with "type": "stdio"; Zed uses context_servers; Codex uses TOML [mcp_servers.wigolo]; Claude Code uses the CLI claude mcp add wigolo --scope user -- npx -y wigolo. My agent is and its MCP config is at <path, or "wherever it registers MCP servers">.

That prompt is self-contained, so even an assistant with no web access can act on it. If the assistant can browse, point it at this README (the Manual MCP setup section above has every client's exact config path) or the project's machine-readable llms.txt — both carry the full procedure, including the optional LLM-synthesis env below.

Optional — enable answer synthesis

research, agent, and search format=answer use an LLM to write the final answer. Turn them on by setting a provider and its key (in your shell, or in your agent's MCP env block). WIGOLO_LLM_PROVIDER names the LLM — set it alongside the key:

export WIGOLO_LLM_PROVIDER=gemini
export GEMINI_API_KEY=<your-key>      # free from https://aistudio.google.com/apikey — the free tier is plenty

Any provider works — use anthropic + ANTHROPIC_API_KEY, openai + OPENAI_API_KEY, or groq + GROQ_API_KEY. To stay fully local and keyless, set WIGOLO_LLM_PROVIDER=ollama (or a local server URL) instead. Gemini is suggested because its free tier is more than enough for wigolo.

Run with Docker

A prebuilt image runs the MCP server without installing Node yourself. It bundles the browser engine and on-device models, and the default command is the stdio MCP server.

docker run -i --rm -v wigolo-data:/data ghcr.io/knockoutez/wigolo

The -i flag keeps stdin open for the MCP protocol, and the volume persists the local cache and models across runs (first run downloads the models). Wire it into Claude Code:

claude mcp add wigolo --scope user -- docker run -i --rm -v wigolo-data:/data ghcr.io/knockoutez/wigolo

Any MCP client works the same way: set command to docker and args to the run flags above. The image is also on Docker Hub as towhid69420/wigolo.

Tools

Tool	What it does
🔎 search	Multi-engine web search (18 direct adapters) with rank fusion, ML cross-encoder reranking, and an explainable per-result score. Pass a query array for parallel breadth.
📄 fetch	Load one URL through a tiered router (HTTP → TLS-impersonation → headless browser) that auto-escalates on anti-bot challenges or SPA shells. Clean markdown + metadata + links.
🕸️ crawl	Multi-page crawl — BFS, DFS, sitemap, or map-only. Per-domain rate limits, robots.txt respect, boilerplate dedup.
🧩 extract	Structured data from a page: tables, metadata, JSON-LD, brand identity, named schemas (Article / Recipe / Product / …), or any custom JSON Schema.
💾 cache	Query everything already seen — keyword (BM25) or hybrid (BM25 + on-device vectors). Plus stats, clear, and change detection.
🧲 find_similar	Pages similar to a URL or a concept, via 3-way fusion of keyword + semantic + live web.
🧠 research	Decompose a question → fan out sub-queries → fetch sources → synthesize a cited report (or a structured brief the host LLM writes from).
🤖 agent	Autonomous gather loop: plan → search → fetch → extract → synthesize, with a step log, time budget, and optional output schema.
🔁 diff + ⏱️ watch	See exactly what changed on a page since last visit; re-check on a schedule and deliver changes to a webhook.

Why it's different

wigolo isn't the free stand-in you settle for until the budget clears — it's built to hold the same line as the paid services in this lane, and it brings receipts. What actually separates it:

• Built for agents, not humans. One MCP call fans out many queries across many engines in parallel — something a serial host tool-loop can't replicate — with transparent per-result scoring and budget-aware output.
• Honest output. Stale cache, failed fetches, degraded backends, and truncation are surfaced in the result, never disguised as empty-but-successful data.
• $0 per query, free to re-query. Default search talks to public engines through direct adapters; the reranker and embeddings run on-device. Every response is cached, so asking again is instant and costs nothing.
• Private by default. Cache, embeddings, models, and config live under ~/.wigolo/. Nothing reaches a third party unless you explicitly opt into an LLM for synthesis.

wigolo is a focused web layer for one agent on one machine — not a hosted SaaS, a vector database other apps query, or a browser-automation framework. Within that lane it goes toe-to-toe with the paid services on result quality — and the meter, the key, and the data-egress simply aren't there.

Here's what one real result looks like, dissected — including the failed engine and the weak result, because those are part of the answer too:

Benchmark

All four tools converged on the same core answer — and only one of them handed back verbatim, byte-pinned evidence while doing it.

One cold query, run live inside a single Claude Fable 5 session and fanned out to four web tools on equal footing — built-in WebSearch, wigolo, Tavily, and Exa — then reported by the agent itself under one rule: judge on the evidence alone, no favoritism. The query: when to choose logical vs streaming replication in Postgres.

The headline is in the report itself: all four tools converged on the same core answer. Same top source as the paid tools, same conclusions — parity demonstrated, not asserted. On top of that, wigolo was the only tool of the four to return verbatim quoted excerpts pinned to byte-offset source spans with citation IDs, an explainable per-result score decomposition (cross-encoder, lexical, engine consensus), and live per-engine telemetry — and when two of its results were weak, its own scorer flagged them as junk on-screen. The cloud tools earn their line too: Exa rendered the official docs' comparison matrix in full. Both edges, stated straight, by the same model that drove all four tools.

One honest query, not a leaderboard — run your own and you'll see the same shape: the keyless local tool standing shoulder to shoulder with the paid services, handing your agent evidence the others don't, at $0 with nothing leaving your machine. Here's the full run:

Same fight, different physics

The paid tools are genuinely good — that's what makes the parity interesting. The differences that remain aren't quality, they're physics:

	wigolo	Firecrawl	Exa	Tavily
Multi-engine web search	✅	✅	✅	✅
Fetch & structured extraction	✅	✅	✅	✅
Whole-site crawl & map	✅	✅	—	✅
Verbatim excerpts pinned to byte-offset source spans	✅	—	—	—
Explainable per-result score decomposition	✅	—	—	—
Persistent local memory — re-query instantly, offline	✅	—	—	—
Query data stays on your machine	✅	—	—	—
API key / account	none	required	required	required
Cost per query	$0	metered	metered	metered

_{Feature standing as of July 2026 — check each vendor's docs for current state.}

That last row is the one that compounds — agents don't ask once, they ask in bursts:

Architecture

A single Node process speaking MCP (JSON-RPC over stdio). Everything heavy is local and lazy-loaded, so a zero-key install pays nothing for the parts it isn't using.

flowchart TD
    A["🤖 AI coding agent<br/>any MCP client"]
    A -->|MCP over stdio| B["<b>wigolo</b><br/>10 tools · dynamic instructions<br/>in-process browser pool + cache + models"]

    B --> C{"Tool layer"}
    C --> T1["search · fetch · crawl · extract"]
    C --> T2["cache · find_similar · research · agent"]

    T1 --> F["⚙️ Fetch router<br/>HTTP → TLS-impersonation → headless browser<br/><i>per-domain learning</i>"]
    T1 --> S["⚙️ Search<br/>18 engines → RRF fusion → cross-encoder rerank<br/><i>explainable evidence score</i>"]
    T2 --> DB[("🗄️ SQLite<br/>url cache · FTS5 keyword · sqlite-vec")]
    T2 --> ML["🧠 On-device ML<br/>BGE-small embeddings (384d)<br/>MiniLM cross-encoder reranker"]

    F -.->|optional| LLM["☁️ Cloud LLM<br/>synthesis only · opt-in"]
    S -.->|optional| SX["🔀 Aggregator backend<br/>opt-in legacy / hybrid"]

    F --> WEB["🌍 Public web"]
    S --> WEB

    style B fill:#7c3aed,stroke:#5b21b6,color:#fff
    style WEB fill:#0ea5e9,stroke:#0369a1,color:#fff
    style DB fill:#1e293b,stroke:#334155,color:#fff
    style LLM stroke-dasharray: 5 5
    style SX stroke-dasharray: 5 5

• Code beats model. Deterministic work — canonicalization, rank fusion, dedup, schema matching, hashing — never touches an LLM. The model is reserved for judgment, opt-in, and capped per request. LLM-filled fields are checked against the source and nulled if absent, so hallucinations don't reach your output.
• Routing on observable signals. The fetch ladder escalates to a real browser on what it sees — SPA markers, challenge bodies, thin content — not domain guesses. It learns per-domain and unlearns when a site stops needing it.
• Transparent, honest results. Every result carries a score breakdown and a query-understanding block; degraded state is always surfaced, never hidden.

Configuration

A clean install works out of the box. A few settings meaningfully raise output quality — set them as environment variables or in your agent's MCP env block.

# 1. Synthesis — the biggest lever. research / agent / search-answer need an LLM
#    to write the final text. Set the provider AND its key (a key alone is ignored).
export WIGOLO_LLM_PROVIDER=gemini                   # names the LLM; free tier is plenty (or anthropic/openai/groq)
export GEMINI_API_KEY=<your-key>                    # that provider's key (ANTHROPIC_API_KEY / OPENAI_API_KEY / …)
#   ...or fully local & keyless:  export WIGOLO_LLM_PROVIDER=ollama   (or a local http URL)

# 2. Wider retrieval funnel
export WIGOLO_SEARCH=hybrid                         # core engines + aggregator fallback
export WIGOLO_GITHUB_TOKEN=...                      # GitHub code search 10 → 30 req/min + org-private

# 3. Land more fetches, stay warm
export WIGOLO_TLS_TIER=auto                         # per-domain TLS-impersonation past Cloudflare/DataDome
export WIGOLO_EAGER_WARMUP=1                        # pay the ~1s model load up front, not on first search

For repeated interactive use, run wigolo serve so the browser pool, embeddings, and reranker stay resident across calls. To stay 100% on-device, a local LLM endpoint + WIGOLO_TLS_TIER=auto is the honest minimal set.

Per-call habits that pay off: query arrays (["a","b","c"]) for parallel breadth · search_depth: "deep" for queries that matter · include_domains as a hard filter for docs lookups.

CLI commands

Command	What it does
wigolo / wigolo mcp	Start the MCP stdio server (the default command).
wigolo init	Set up wigolo: install components, wire into detected agents. --non-interactive --agents=<csv> --provider=<name> --search=<backend> for CI.
wigolo setup mcp	Re-write just the MCP server entries, without the full wizard.
wigolo doctor	Cold-start health check — no network fetches.
wigolo verify	End-to-end smoke test (fetch, crawl, extract, search, rerank, embed).
wigolo serve	HTTP daemon — keeps subsystems warm across multiple clients.
wigolo shell	Interactive REPL (--json for piping).
wigolo config	Settings TUI; or headless --set K=V, --export, --import, --cleanup, --uninstall --yes.
wigolo status	Plain-text status summary.
wigolo health	Ping a running daemon's /health.
wigolo backfill	Embed cached pages that have no vector yet (--batch-size, --dry-run).
wigolo plugin add|list|remove	Manage custom extractor / search-engine plugins.
wigolo uninstall	Remove wigolo from agent configs (keeps your cache).

Environment variables — search & engines

Var	Default	Effect
WIGOLO_SEARCH	core	core (direct engines) / searxng (legacy) / hybrid (core + fallback).
BRAVE_API_KEY	—	When set, Brave joins the engine pool (env-only, never persisted).
WIGOLO_GITHUB_TOKEN	—	Lifts GitHub code search 10 → 30 req/min; enables org-private search (env-only).
SEARXNG_URL	—	External aggregator URL; when set, skips local bootstrap.
SEARXNG_MODE	native	native (Python venv) or docker.
SEARXNG_PORT	8888	Port for the native aggregator.
SEARXNG_QUERY_TIMEOUT_MS	8000	Per-query timeout to the aggregator.
WIGOLO_MULTI_QUERY_CONCURRENCY	5	Max parallel (query × engine) tasks.
WIGOLO_MULTI_QUERY_MAX	10	Max unique queries after normalization.
WIGOLO_QUERY_EXPAND_VARIANTS	5	Heuristic query-expansion variants.
SEARCH_NARROW_RENDER_MAX_CANDIDATES	3	Max candidates for which a domain-scoped (include_domains) search renders result pages in the browser engine during enrichment — recovers real content from JS-heavy documentation sites. Bounded to a few URLs; broad searches never escalate. 0 disables.

Environment variables — fetch, network & TLS

Var	Default	Effect
USER_AGENT	rotating Chrome UAs	Override the User-Agent header.
FETCH_TIMEOUT_MS	10000	HTTP request timeout.
FETCH_MAX_RETRIES	2	Retry budget for 429 / 502 / 503 / network errors.
MAX_REDIRECTS	5	Manual-mode redirect cap.
PLAYWRIGHT_LOAD_TIMEOUT_MS	15000	Browser page.load wait.
PLAYWRIGHT_NAV_TIMEOUT_MS	30000	Browser navigation timeout.
SEARCH_FETCH_TIMEOUT_MS	15000	Per-result hydration fetch in search.
SEARCH_TOTAL_TIMEOUT_MS	30000	Aggregate search budget.
USE_PROXY / PROXY_URL	false / —	Route fetch through a proxy.
WIGOLO_TLS_TIER	off	off / auto (per-domain learned) / on (always try TLS first).
WIGOLO_TLS_BROWSER	chrome_142	TLS fingerprint profile (<browser>_<version>).
WIGOLO_TLS_SUCCESS_THRESHOLD	3	Successes before a domain flips to TLS-first.

Environment variables — browser pool & auth

Var	Default	Effect
MAX_BROWSERS	3	Max concurrent contexts per browser type.
BROWSER_IDLE_TIMEOUT	60000	Idle context eviction (ms).
BROWSER_FALLBACK_THRESHOLD	3	HTTP failures on a domain before forcing the browser.
WIGOLO_BROWSER_TYPES	auto (all 3)	CSV of browsers to use (chromium, firefox, webkit).
WIGOLO_CDP_URL	—	Chrome DevTools endpoint for a remote / logged-in browser.
WIGOLO_AUTH_STATE_PATH	—	Playwright storageState.json (cookies / localStorage).
WIGOLO_CHROME_PROFILE_PATH	—	Full Chrome User Data dir (copied to temp per use).

Environment variables — cache & crawl

Var	Default	Effect
CACHE_TTL_SEARCH	86400	Search result cache TTL (s).
CACHE_TTL_CONTENT	604800	Page content cache TTL (7 days).
WIGOLO_FAST_STALE_MAX_HOURS	24	In cache mode, accept entries up to this age.
WIGOLO_FAST_TIMEOUT_MS	800	Tight timeout for cache-mode fallback fetches.
CRAWL_CONCURRENCY	2	Per-public-domain concurrent fetches.
CRAWL_DELAY_MS	500	Per-public-domain inter-request delay.
CRAWL_PRIVATE_CONCURRENCY	10	Per-private-domain concurrency (localhost / RFC1918).
CRAWL_PRIVATE_DELAY_MS	0	Per-private-domain delay.
RESPECT_ROBOTS_TXT	true	When false, robots.txt is not fetched.
VALIDATE_LINKS	true	When false, broken-link probe is skipped.
WIGOLO_CRAWL_INDEX	—	1 → crawled pages enqueued for embedding.
WIGOLO_WAIT_FOR_INDEX	—	1 → embedding queue runs synchronously per page.

Environment variables — reranker, embedding & relevance

Var	Default	Effect
WIGOLO_RERANKER	onnx	onnx (cross-encoder) / none (consensus + authority + recency boosts only).
WIGOLO_RERANKER_MODEL	Xenova/ms-marco-MiniLM-L-6-v2	Cross-encoder model ID.
WIGOLO_RERANKER_IDLE_TIMEOUT_MS	300000	Hold the model warm 5 min after last use.
WIGOLO_EMBEDDING_MODEL	BAAI/bge-small-en-v1.5	Embedding model (384-dim).
WIGOLO_EMBEDDING_IDLE_TIMEOUT	1800000	Idle unload (30 min).
WIGOLO_EMBEDDING_MAX_TEXT_LENGTH	8000	Truncation before embedding.
WIGOLO_RELEVANCE_THRESHOLD	0	Min relevance for the agent's post-fetch filter.
WIGOLO_FIND_SIMILAR_COLD_START_THRESHOLD	0.02	Fused score below which find_similar emits cold_start.

Environment variables — LLM integration (all optional)

Var	Default	Effect
WIGOLO_LLM_PROVIDER	—	anthropic / openai / gemini / groq / custom URL (Ollama, vLLM, LM Studio).
WIGOLO_LLM_MODEL	—	Universal model override.
WIGOLO_LLM_MODEL_{ANTHROPIC|OPENAI|GEMINI|GROQ}	—	Per-provider model override (highest precedence).
WIGOLO_LLM_MAX_CALLS_PER_REQUEST	1	Hard ceiling on LLM calls per tool invocation.
WIGOLO_LLM_CACHE_TTL_DAYS	7	LLM response cache TTL.
WIGOLO_LOCAL_LLM	off	Opt-in keyless local language model tier: off (default) / auto (auto-detect a local model server) / an explicit http(s):// endpoint. Off keeps the keyless path unchanged.
WIGOLO_LOCAL_LLM_MODEL	—	Preferred model name for the local tier; unset auto-picks an installed model.
WIGOLO_LOCAL_LLM_BASE_URL	http://localhost:11434	Endpoint probed when WIGOLO_LOCAL_LLM=auto (falls back to WIGOLO_LLM_BASE_URL, then the default local server).
ANTHROPIC_API_KEY / OPENAI_API_KEY	—	Read on every call; never persisted.
GEMINI_API_KEY / GOOGLE_API_KEY	—	Gemini provider key (either name; read on every call, never persisted).
GROQ_API_KEY	—	Same.
WIGOLO_LLM_API_KEY	—	Generic key for whichever provider WIGOLO_LLM_PROVIDER names. The provider-specific var wins; ignored during auto-detect.

Keys can also live in the OS keychain or an AES-encrypted file (wigolo init / wigolo config) — never in config.json.

Environment variables — daemon, warmup, paths, logging & misc

Var	Default	Effect
WIGOLO_DATA_DIR	~/.wigolo	Root for cache, models, keys, plugins, aggregator venv.
WIGOLO_CONFIG_PATH	${DATA_DIR}/config.json	Persisted config path.
WIGOLO_DAEMON_PORT	3333	Listen port for wigolo serve.
WIGOLO_DAEMON_HOST	127.0.0.1	Bind address.
WIGOLO_EAGER_WARMUP	—	1 → pre-warm embed + rerank on startup (fire-and-forget).
WIGOLO_BOOTSTRAP_MAX_ATTEMPTS	3	Aggregator bootstrap retry limit.
WIGOLO_HEALTH_PROBE_INTERVAL_MS	30000	Background backend-health probe period.
WIGOLO_PLUGINS_DIR	${DATA_DIR}/plugins	Plugin discovery root.
LOG_LEVEL	info	debug / info / warn / error.
LOG_FORMAT	json	json or human-friendly text.
WIGOLO_TELEMETRY	—	1 → local NDJSON event log (off by default, no PII).
WIGOLO_TELEMETRY_ENDPOINT	—	Also POST events fire-and-forget to this URL.
WIGOLO_TUI_REDUCED_MOTION	—	1 → disable TUI spinners / animations.

Common per-call options (tool arguments)

Option	Tools	Notes
mode	fetch, search, crawl, extract, find_similar	cache (fast, stale-OK) / default (smart routing) / stealth (full browser, no cache).
search_depth	search	ultra-fast (cache only) / fast / balanced (default) / deep (evidence + rerank highlights).
query	search	string or string[] — arrays fan out in parallel.
include_domains / exclude_domains	search, find_similar, research	Hard whitelist / blacklist (host-suffix match).
format	search	answer / stream_answer — triggers LLM synthesis with citations.
citation_format	search, crawl, research, agent	numbered / json / anthropic_tags.
time_range / from_date / to_date	search	Recency bounds.
render_js	fetch	auto / always / never.
use_auth	fetch, crawl	Route through configured auth (CDP > Chrome profile > storage state).
actions	fetch	Sequential browser actions (click, type, wait, wait_for, scroll, screenshot).
section	fetch	Extract a markdown subtree at a heading.
strategy	crawl	bfs / dfs / sitemap / auto / map.
mode (extract)	extract	selector / tables / metadata / schema / structured / brand.
named_schema	extract	Article / Recipe / Product / CodeSnippet / Paper / EventListing.
depth	research	quick / standard / comprehensive.
max_pages / max_time_ms	agent	Per-invocation page cap (default 3) and wall-clock budget.
max_tokens_out	most	Aggregate output-token budget (default 4000).
include_full_markdown	fetch, crawl, research, agent	false → evidence excerpts instead of full bodies.

Beta & feedback

wigolo is in public beta. Everything documented here works and is held to a 6,000-test suite — beta is about the polish bar, not stability. It stays beta until enough people have used it, kicked it, and starred it that calling it v1 means something.

That makes your feedback the whole game right now. Every report is read, usually the same day:

• 🐛 Report a bug — broke, misbehaved, surprised you
• 💡 Request a feature — something it should do
• 💬 Ask anything — questions, setups, show & tell

And if wigolo earns a place in your setup, the ways to keep it alive: a ⭐ star (it's how open source gets found), a ☕ coffee (there's no paid tier and never will be), or just an email — it goes straight to the one developer who wrote the code.

FAQ

Free? What's the catch?

No catch by design. The expensive parts — ranking, embeddings, the browser engine — run on your hardware, so there's no per-query cost to recover and no reason for a meter. Sustained by donations; the AGPL license legally prevents a bait-and-switch into a closed hosted product.

Is the quality really on par with the paid services?

Run one query and judge — the benchmark section above is a live 4-way run, not a chart. Everyday agent queries land at parity; the paid tools still win some deep-extraction edge cases, and crawling is where wigolo is strongest. Every result shows its scoring, so you don't have to take anyone's word for it.

Won't public search engines block or rot?

It's engineered for exactly that: 18 engines fused with rank fusion (any one failing barely moves results), a tiered fetch ladder with per-domain learning, and an optional aggregator fallback. Degraded backends are reported in the output, never hidden — and the local cache means everything already seen keeps working regardless.

Is this kind of scraping OK?

wigolo reads the public web the way a browser does — robots.txt respected by default, per-domain rate limits, research-grade volumes for one agent on one machine. It's deliberately the polite end of the spectrum, not a harvesting platform.

AGPL — can I use this at work?

Yes, freely, company-wide. The license only bites if you modify wigolo and run it as a network service — then you must publish those modifications. Using it as a local dev tool carries zero obligation. Commercial-licensing questions: reach out.

Why 1.5 GB of disk?

That's the on-device brain: a full browser engine plus the ranking and embedding models the cloud services run on their side and bill you for. Disk is cheap; meters aren't.

Available on

Grab wigolo wherever you manage packages or MCP servers:

• npm — wigolo
• Docker — ghcr.io/knockoutez/wigolo · towhid69420/wigolo
• Official MCP Registry — io.github.KnockOutEZ/wigolo
• Directories — Glama · Smithery · mcp.so · LobeHub

Contributing

Bug reports, feature requests, and PRs are all welcome — see CONTRIBUTING.md. Keep tool handlers thin (business logic lives in the domain modules), add tests, and run the suite before opening a PR. wigolo also has a plugin system for custom extractors and search engines: wigolo plugin add <git-url>.

License

GNU AGPL-3.0-only. Free to use, modify, and self-host — including inside a company. The one obligation: if you run a modified version as a network service, you must publish your modified source under the same license. That keeps wigolo open while preventing a closed, hosted fork. See SECURITY.md to report a vulnerability and TRADEMARK.md for use of the name. For commercial-licensing questions, reach out.