René's URL Explorer Experiment

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        "name": "Can I use CrewAI with non-Gemini models?",
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          "text": "\u003cp\u003eYes, including OpenAI, Anthropic\u0026rsquo;s Claude, Mistral, Groq, AWS Bedrock, and Ollama for local models. Set the \u003ccode\u003ellm\u003c/code\u003e parameter on any agent. For example, use \u003ccode\u003ellm=LLM(model=\"gpt-4.1-mini\")\u003c/code\u003e for OpenAI or \u003ccode\u003ellm=LLM(model=\"ollama/llama3.1\")\u003c/code\u003e for a local Ollama model. You\u0026rsquo;ll need the appropriate API key set as an environment variable for cloud providers.\u003c/p\u003e"
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          "text": "\u003cp\u003eCost depends on the number of agents, tasks, and which LLM you use. Each agent makes separate API calls, so a three-agent crew costs roughly three times what a single-agent call would cost. The Gemini free tier provides generous limits for experimentation.\u003c/p\u003e\n\u003cp\u003eAfter running \u003ccode\u003ecrew.kickoff()\u003c/code\u003e, check \u003ccode\u003ecrew.usage_metrics\u003c/code\u003e to see exact token counts. You can reduce costs by using a cheaper model for tool-calling operations via the \u003ccode\u003efunction_calling_llm\u003c/code\u003e agent parameter.\u003c/p\u003e"
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          "text": "\u003cp\u003eCrewAI is a standalone multi-agent orchestration framework built entirely from scratch, independent of \u003ca href=\"https://realpython.com/build-llm-rag-chatbot-with-langchain/\"\u003eLangChain\u003c/a\u003e. LangChain is a general-purpose framework for building LLM applications with chains, retrieval, memory, and more. If you need to use existing LangChain tools with CrewAI, you can wrap them manually using \u003ccode\u003eBaseTool\u003c/code\u003e from \u003ccode\u003ecrewai.tools\u003c/code\u003e. Many developers use both frameworks for different purposes in the same project.\u003c/p\u003e"
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        "name": "Can agents in a crew run in parallel?",
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          "text": "\u003cp\u003eCrewAI doesn\u0026rsquo;t have a \u003ccode\u003eProcess.parallel\u003c/code\u003e option, but you can achieve parallel execution by setting \u003ccode\u003easync_execution=True\u003c/code\u003e on individual tasks. Tasks marked for async execution run concurrently, and downstream tasks that list them in their \u003ccode\u003econtext\u003c/code\u003e parameter will wait for all of them to complete before starting.\u003c/p\u003e"
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        "name": "When should I use hierarchical team coordination?",
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          "@type": "Answer",
          "text": "\u003cp\u003eUse \u003ccode\u003eProcess.hierarchical\u003c/code\u003e when you want a manager agent to dynamically delegate tasks to the most appropriate team member rather than following a fixed order. This is useful for complex workflows where task assignment depends on intermediate results. You\u0026rsquo;ll need to provide either a \u003ccode\u003emanager_llm\u003c/code\u003e or a custom \u003ccode\u003emanager_agent\u003c/code\u003e when using this mode.\u003c/p\u003e"
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        "name": "How do agents in a crew communicate with each other?",
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          "@type": "Answer",
          "text": "\u003cp\u003eAgents communicate through task outputs rather than direct conversation. In a sequential process, the output of each task automatically flows as context to the next task. You can also explicitly define dependencies using the \u003ccode\u003econtext\u003c/code\u003e parameter on a \u003ccode\u003eTask\u003c/code\u003e. This task-based approach provides predictable data flow but doesn\u0026rsquo;t support the back-and-forth exchanges you\u0026rsquo;d find in AG2 or similar conversational frameworks.\u003c/p\u003e"
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