Building a B2B Prospecting Agent with CrewAI and Web Metadata Extractor

Описание и применение

Руководство по созданию агента для поиска B2B-лидов с использованием CrewAI и API извлечения метаданных. Решение разделяет сбор данных и принятие решений на двух агентов для повышения точности и масштабируемости.

Задача

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

Результат

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

Рабочий процесс

• 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.

Доказательства

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

Источник

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

Источник