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* docs(cli): document device-code login and config reset guidance; renumber sections * docs(cli): fix duplicate numbering (renumber Login/API Keys/Configuration sections) * docs: Fix webhook documentation to include meta dict in all webhook payloads - Add note explaining that meta objects from kickoff requests are included in all webhook payloads - Update webhook examples to show proper payload structure including meta field - Fix webhook examples to match actual API implementation - Apply changes to English, Korean, and Portuguese documentation Resolves the documentation gap where meta dict passing to webhooks was not documented despite being implemented in the API. * WIP: CrewAI docs theme, changelog, GEO, localization * docs(cli): fix merge markers; ensure mode: "wide"; convert ASCII tables to Markdown (en/pt-BR/ko) * docs: add group icons across locales; split Automation/Integrations; update tools overviews and links
58 lines
2.2 KiB
Plaintext
58 lines
2.2 KiB
Plaintext
---
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title: Ferramenta LangChain
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description: O `LangChainTool` é um wrapper para ferramentas LangChain e mecanismos de consulta.
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icon: link
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mode: "wide"
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---
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## `LangChainTool`
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<Info>
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CrewAI integra-se perfeitamente com a abrangente [lista de ferramentas](https://python.langchain.com/docs/integrations/tools/) do LangChain, todas as quais podem ser usadas com CrewAI.
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</Info>
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```python Code
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import os
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from dotenv import load_dotenv
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from crewai import Agent, Task, Crew
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from crewai.tools import BaseTool
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from pydantic import Field
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from langchain_community.utilities import GoogleSerperAPIWrapper
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# Set up your SERPER_API_KEY key in an .env file, eg:
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# SERPER_API_KEY=<your api key>
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load_dotenv()
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search = GoogleSerperAPIWrapper()
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class SearchTool(BaseTool):
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name: str = "Search"
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description: str = "Useful for search-based queries. Use this to find current information about markets, companies, and trends."
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search: GoogleSerperAPIWrapper = Field(default_factory=GoogleSerperAPIWrapper)
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def _run(self, query: str) -> str:
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"""Execute the search query and return results"""
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try:
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return self.search.run(query)
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except Exception as e:
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return f"Error performing search: {str(e)}"
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# Create Agents
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researcher = Agent(
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role='Research Analyst',
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goal='Gather current market data and trends',
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backstory="""You are an expert research analyst with years of experience in
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gathering market intelligence. You're known for your ability to find
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relevant and up-to-date market information and present it in a clear,
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actionable format.""",
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tools=[SearchTool()],
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verbose=True
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)
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# rest of the code ...
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```
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## Conclusão
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As ferramentas são fundamentais para ampliar as capacidades dos agentes CrewAI, permitindo que realizem uma ampla variedade de tarefas e colaborem de forma eficaz.
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Ao construir soluções com CrewAI, aproveite tanto ferramentas personalizadas quanto existentes para potencializar seus agentes e aprimorar o ecossistema de IA. Considere utilizar tratamento de erros, mecanismos de cache e a flexibilidade dos argumentos das ferramentas para otimizar o desempenho e as capacidades dos seus agentes. |