Reading
A multi-agent web crawler system built on a workflow of 5 AI Agents (Architect, Crawler, Indexer, Search, UI). It uses the Token Bucket algorithm for rate limiting, SQLite WAL mode to support real-time search, and provides a real-time SPA dashboard.
Section 01
A multi-agent web crawler system built on a workflow of 5 AI Agents (Architect, Crawler, Indexer, Search, UI). It uses the Token Bucket algorithm for rate limiting, SQLite WAL mode to support real-time search, and provides a real-time SPA dashboard.
Section 02
Traditional web crawlers usually adopt a single-process, linear execution mode and face several common challenges: lack of intelligent page parsing, inability to perform real-time searches, difficulty in handling rate limits gracefully, and state persistence issues. These problems are particularly prominent in scenarios requiring large-scale, sustainable crawlers.
The Multi-Agent Web Crawler adopts a new architectural approach—breaking down the crawler system into 5 specialized Agents, each responsible for specific duties, collaborating to complete complex crawling and search tasks.
Section 03
The core innovation of the system lies in breaking down the crawler workflow into 5 specialized Agents:
Section 04
Responsible for the overall system architecture design and coordination, defining interfaces and data flows between Agents.
Section 05
Performs actual web crawling tasks, manages URL queues and crawling strategies.
Section 06
Parses, tokenizes, and indexes the crawled content to build searchable data structures.
Section 07
Handles search queries, performs TF-IDF scoring, and sorts results.
Section 08
Provides a real-time SPA dashboard to display crawling status and search results.
This multi-agent architecture gives the system better modularity and scalability—each Agent can be optimized independently, collaborating to complete complex tasks.
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