Building a B2B Prospecting Agent with CrewAI and Web Metadata Extractor

说明与用途

使用 CrewAI 和网络元数据提取 API 构建 B2B 潜在客户开发代理的指南。该解决方案将数据收集与决策制定分离为两个代理，以提高准确性和可扩展性。

问题

Manual B2B prospecting does not scale, and custom scrapers require handling SSRF safety, HTML parsing, and rate limits before implementing agent logic.

结果

A functional two-agent system that separates data extraction from judgment, producing a ranked list of B2B leads with specific technical justifications.

工作流

• Install crewai and crewai-webmetadata-extractor via pip.
• Obtain a RapidAPI key for the Web Metadata & Contact Extractor API.
• Set the WEBMETADATA_API_KEY environment variable.
• Define a 'Researcher' agent equipped with WebContactsTool and WebMetadataExtractTool to gather raw signals.
• Define a 'Qualifier' agent to analyze the researcher's output and rank companies based on fit.
• Create a sequential Crew process linking the research task to the qualification task.
• Run the crew kickoff to generate a ranked list with justifications.

证据

This walks through building a two-agent CrewAI crew that takes a list of company URLs and produces a prioritized, reasoned shortlist

来源

The researcher's tool calls return structured JSON, not free text — which is what lets the qualifier reason about specific fields instead of vibes.

来源