Feed real-time web data into autonomous agents and AI workflows to enable smarter decisions, faster reactions, and continuous learning.
Autonomous AI agents are only as effective as the information they receive. CrawlKit provides a reliable way to connect AI agents with fresh, real-world web data—turning the open web into a live input stream for intelligent systems.
AI Agent Input Pipelines are structured flows that deliver external data into autonomous or semi-autonomous AI systems. Instead of operating in isolation, agents receive fresh context from the web, signals from multiple sources, and structured inputs aligned with their tasks. This allows AI workflows to move beyond predefined logic and respond dynamically to changing environments.
CrawlKit enables AI agents to consume a wide range of real-time and near-real-time web signals.
Continuously gather and synthesize information from the web.
Detect changes, anomalies, or opportunities in real time.
Provide up-to-date context for automated or human-in-the-loop decisions.
Identify new content, sources, or trends automatically.
Trigger actions based on changes detected across the web.
Static knowledge quickly becomes obsolete. By feeding AI agents with live web data, teams gain faster response to external changes, reduced blind spots in decision-making, and agents that evolve alongside their environment. This leads to AI systems that are more resilient, accurate, and useful in production.
Traditional automation follows predefined rules. AI agents powered by real-time web data can observe changes, interpret context, and take informed action. CrawlKit helps bridge the gap between autonomous reasoning and the constantly changing web—enabling adaptive intelligence at scale.
Get started in minutes with our simple API.
const response = await fetch('https://api.crawlkit.sh/v1/crawl/raw', {
method: 'POST',
headers: {
'Authorization': 'ApiKey ck_xxx',
'Content-Type': 'application/json'
},
body: JSON.stringify({
url: 'https://news.ycombinator.com/',
waitFor: 2000
})
});
const { data } = await response.json();
// Feed extracted content to AI agent
const agentContext = {
source: data.url,
content: extractMainContent(data.body),
timestamp: new Date().toISOString(),
signals: extractSignals(data.body)
};
// Agent processes real-time web context
await agent.processContext(agentContext);Why teams choose CrawlKit for ai agent input pipelines.
Keep agents informed with the latest public information.
Enable agents to adjust behavior based on real-world signals.
Automate data gathering instead of relying on human updates.
Support multiple agents and tasks using a shared data foundation.
Adapt pipelines as agent capabilities and requirements evolve.
AI Agent Input Pipelines with CrawlKit is commonly used by:
If your AI system depends on understanding real-world web content, this use case provides a strong foundation.
Autonomous agents should not operate on yesterday's information. Connect AI agents with the live web—ensuring they act with context, relevance, and awareness.