Search the web, extract and crawl pages, map sites and run cited research reports with Tavily, built for AI agents.
Tavily ships in the w6w first-party pack. It declares 7 actions, 2 health checks, and the host runs its code in a sandbox that never sees the credential.
io.w6w.tavilyTavily is a web access layer built for AI agents: a search engine that returns ranked, cleaned snippets (and optionally an LLM-written answer), plus tools to pull the full content out of known URLs, crawl a whole site, map its URL structure, and run multi-step research that ends in a cited report.
This app covers the whole core surface in one connection: Search, Extract, Crawl and Map for fast, credit-metered reads of the live web, and Research, which starts an asynchronous report and lets a workflow poll for it. It also reads the account’s credit usage, so a workflow can check headroom before it starts an expensive crawl or research task.
Every call is metered in Tavily credits, so the app prefills conservative defaults, and health checks track Tavily’s own status page and the plan’s remaining credits.
Three routes to the same 7 actions. The Workflow tab is generated from Tavily's own manifest and carries its real ids, so it is copy-pasteable; the Code and CLI examples are the same call for any action on any app, so every app-specific value in them is a blank you fill in.
extract Extract clean content from up to 20 URLs. Per-URL failures come back in failed_results, not as an error.
research-get Get a research task's status (pending, in_progress, completed, failed) and, once completed, its report and sources.
research-start Queue a multi-step research task that produces a cited report. Poll Get Research Task for the result.
usage-get Get credit usage for this API key and the account plan in the current billing cycle.
A workflow step names the app and the action, and the editor fills in the
connection when you pick one. This is the Step shape from the
workflow spec, carrying Tavily's real ids.
{
"manifestVersion": "2",
"name": "tavily-example",
"steps": [
{
"id": "crawl",
"uses": {
"app": "io.w6w.tavily",
"action": "crawl",
"connection": "conn_YOUR_CONNECTION_ID"
},
"with": {
"url": "<url>"
}
}
]
}crawl extract map research-get research-start +2 more actions available
Every app-specific value here is a blank you have to fill in. An
app action is reached through the connection that authenticates it, so the
address is a connection id, not the app id — and connections belong to your account,
so a public page cannot know yours. Create one for Tavily, then fill in
the three blanks: conn_YOUR_CONNECTION_ID, the action key, and the
parameters that action declares. The call itself is real — the shape is transcribed
from the studio's own snippet builder, which prints the same kind of blanks — but
nothing in it is specific to Tavily. The Workflow tab is where this app's
real ids are.
npm install @w6w/sdkyarn add @w6w/sdkpnpm add @w6w/sdkdeno add npm:@w6w/sdkimport { W6wClient, isActionRun } from "@w6w/sdk";
// Reads W6W_BASE_URL and W6W_TOKEN from the environment when omitted.
const client = new W6wClient();
const envelope = await client.run({
urn: "conn_YOUR_CONNECTION_ID",
action: "crawl",
payload: {
url: "<value>",
// instructions: "<value>",
// maxDepth: "<value>",
// maxBreadth: "<value>",
// limit: "<value>",
// selectPaths: "<value>",
// selectDomains: "<value>",
// excludePaths: "<value>",
// excludeDomains: "<value>",
// allowExternal: "<value>",
// timeout: "<value>",
// chunksPerSource: "<value>",
// extractDepth: "<value>",
// format: "<value>",
// includeImages: "<value>",
},
});
if (isActionRun(envelope)) console.log(envelope.value); npm install -g @w6w/cli w6w run conn_YOUR_CONNECTION_ID --action crawl --payload '{"url":"<value>"}' Give an AI agent Tavily — without giving it Tavily's credentials. One MCP endpoint exposes every app, function and workflow the caller is entitled to, as tools it can discover and run. Access is granted per team while we onboard.
One tool call{
"name": "w6w_invoke",
"arguments": {
"ref": "app:io.w6w.tavily#crawl",
"input": {
"url": "<url>"
}
}
}
Every tool names its target with a single ref. The
app: form above doesn't name a connection at all — the
host resolves which of the caller's Tavily connections to sign
with, and refuses rather than guesses when the answer is ambiguous.
The token is attached host-side, at the moment of the call. It is never a tool argument, never in the model's context, and never in a transcript — so a prompt injection has nothing to exfiltrate.
Tools are derived per end user from what that person has actually connected and is entitled to — not one shared bot identity carrying the union of everyone's access.
Multi-step work runs on the workflow engine and returns a run handle the agent can poll — retries, branching and state survive the conversation that started them.
Tavily's declared health checks are on the surface too, so an agent can tell "the vendor is down" from "your credential expired" before it burns a retry on either.
The MCP surface is part of the hosted platform. Tavily itself is MIT, and the runtime that executes it is source-available (FSL).
Tavily declares its own checks, so its health is a property of the app rather than something the host guesses at.
The `Tavily API Service` component of status.tavily.com.
Plan, pay-as-you-go and per-key credit consumption from GET /usage.