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Turn any site or docs into a clean, always-fresh feed that agents can actually read.

Point it at a domain and it produces and continuously maintains clean machine-readable representations: llms.txt and llms-full.txt, a hosted MCP resource server, per-page markdown with stable anchors, structured changelog and version metadata, and an embeddings-ready index.

No account. No card. No sales call.

  • Clean feeds generated from any site or docs
  • Re-crawled and diffed when content changes
  • A hosted MCP resource server

Get a working setup in about ten seconds

Pick authentication, region, client and limits. Copy a config and client snippet that run, with the latency they imply. Runs in your browser. No account, no card, no call.

Your service

Generates locally. Copy the output and it runs.

p50 latency
25 ms
p99 latency
80 ms
Worst case
30.3 s
all retries exhausted
my-service.config.yaml yaml
# my-service — generated by AgentFeeder
# Copy to my-service.config.yaml. Every value below is live, not a placeholder.
apiVersion: v1
kind: Service
metadata:
  name: my-service
  region: us-east
spec:
  endpoint: https://my-service.agentfeeder.com/v1
  auth:
    mode: apikey
    credential: ${MY_SERVICE_TOKEN}
  limits:
    requestsPerMinute: 600
    timeoutMs: 10000
    maxRetries: 2
    backoff: exponential
  observability:
    structuredLogs: true
    tracing: w3c
    replayRetentionDays: 7
Try it now bash
curl -sS https://my-service.agentfeeder.com/v1/run \
  -H "X-API-Key: $TOKEN" \
  -H "Content-Type: application/json" \
  --max-time 10.0 \
  -d '{"input": {"task": "hello"}}'
Client node
import { Client } from "@my-service/sdk";

const client = new Client({
  endpoint: "https://my-service.agentfeeder.com/v1",
  auth: { mode: "apikey", token: process.env.TOKEN! },
  timeoutMs: 10000,
  maxRetries: 2,
});

const result = await client.run({ task: "hello" });
console.log(result.data);
Things this configuration will do to you
  • API keys do not expire on their own. Set a rotation reminder now.

Take this with you

No email required. It is your result.

AgentFeeder — config
· my-service · us-east · apikey
· p50 25ms, p99 80ms
· 1,500,000 requests/month at 600 rpm burst

Run it yourself: https://agentfeeder.com

Control how AI represents your product

A feed layer that turns a website into clean, always-fresh representations an assistant or an agent can read without parsing.

llms.txt and llms-full.txt

Point it at a domain and it produces and maintains llms.txt and llms-full.txt for the whole site.

A hosted MCP resource server

Your content is served through a hosted MCP resource server, ready for agents to call.

Per-page markdown

Every page is available as clean markdown with stable anchors, without navigation or cookie banners.

Changelog and version metadata

Structured changelog and version metadata keep pricing, API versions and limits current.

An embeddings-ready index

Content is indexed ready for embeddings, so agent builders can use it as a source straight away.

Re-crawled on change

It re-crawls when your content changes, diffs the result and notifies you.

The problem

An AI assistant asked about a product answers from a stale crawl of a JavaScript-rendered marketing site and gets the pricing, the API version and the limits wrong.

The vendor has no way to correct it.

Meanwhile agent builders burn tokens scraping HTML full of navigation and cookie banners to extract three paragraphs of substance.

Who it is for

  • Developer-tools and SaaS companies

    Vendors with documentation who want AI assistants to get their pricing, API version and limits right.

  • Content businesses

    Publishers who want control over how AI represents what they publish.

  • Agent builders

    Developers who need reliable sources and are tired of scraping HTML for three paragraphs of substance.

How it works

AgentFeeder, from the first step to the result.

  1. 1

    Point it at your domain

    Add the site or documentation you want represented.

  2. 2

    Feeds are generated

    llms.txt, per-page markdown, an MCP resource server and an embeddings-ready index are produced.

  3. 3

    Kept fresh automatically

    Changes trigger a re-crawl, a diff and a notification.

Questions people actually ask

What does AgentFeeder produce?
llms.txt and llms-full.txt, a hosted MCP resource server, per-page markdown with stable anchors, structured changelog and version metadata, and an embeddings-ready index.
How does it stay current?
It re-crawls your site when content changes, diffs the result and notifies you.
Why would a vendor want this?
Assistants often answer from stale crawls of JavaScript-rendered sites and get pricing, API versions and limits wrong. A maintained feed gives them an accurate source you control.
Why would an agent builder want this?
It is a source that does not need parsing, so you are not burning tokens on navigation and cookie banners.
What does it cost?
39 dollars a month per domain for hosted, auto-refreshing feeds with change notifications.
Is there a larger plan?
Yes. 249 dollars a month adds multiple domains, private sources, MCP hosting and analytics on which agents consumed what.
Why should I trust the config generator?
The config is built from the options you pick, and it lists anything in it that will misbehave under load before you copy it.

Pricing

Priced per domain, with a multi-domain plan for private sources and agent analytics.

DomainRecommended

Hosted, auto-refreshing feeds for one domain.

$39
per domain, per month
  • Auto-refreshing feeds
  • Change notifications
Get in touch

Multi-domain

For vendors with several sites or private content.

$249
per month
  • Multiple domains
  • Private sources
  • MCP hosting
  • Analytics on which agents consumed what
Talk to us

Prices in USD.

Get a working setup in about ten seconds

Pick authentication, region, client and limits. Copy a config and client snippet that run, with the latency they imply. Runs in your browser. No account, no card, no call.

Open the free tool

It runs in your browser. AgentFeeder never sees your inputs.