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Building a Conversation Loop with XAI

For complex tasks, Grok may need to call multiple tools across several turns. A conversation loop handles this automatically.

Why Use a Conversation Loop?

Some queries require multiple tool calls. For example, "Summarize my emails and add important ones to my task list" might require:

  1. Fetching emails
  2. Reading email contents
  3. Creating tasks

A loop handles these multi-step interactions automatically.

Implementing a Conversation Loop

Here's a robust conversation loop that handles multiple tool call rounds:

await metorial.withProviderSession(
  metorialXai,
  { serverDeployments: ['your-server-deployment-id'] },
  async session => {
    let messages = [
      {
        role: 'user',
        content: 'Summarize the README.md file of the metorial/websocket-explorer repository on GitHub.'
      }
    ];

    // Deduplicate tools by name to avoid conflicts
    let uniqueTools = Array.from(
      new Map(session.tools.map(t => [t.function.name, t])).values()
    );

    // Allow up to 10 iterations
    for (let i = 0; i < 10; i++) {
      let response = await xai.chat.completions.create({
        model: 'grok-beta',
        messages,
        tools: uniqueTools
      });

      let choice = response.choices[0];
      let toolCalls = choice.message.tool_calls;

      // If no more tools needed, we have our final answer
      if (!toolCalls || toolCalls.length === 0) {
        console.log(choice.message.content);
        return;
      }

      // Execute the tools and add results to history
      let toolResponses = await session.callTools(toolCalls);
      messages.push(
        { role: 'assistant', tool_calls: toolCalls },
        ...toolResponses
      );
    }

    throw new Error('No final response received after 10 iterations');
  }
);

Best Practices

  • Set a maximum iteration limit to prevent infinite loops
  • Deduplicate tools if you encounter naming conflicts
  • Handle the final response when no tool calls are returned
  • Log iterations during development to understand the flow

Error Handling

Always wrap your loop in try-catch to handle errors gracefully:

try {
  // ... conversation loop ...
} catch (error) {
  console.error('Conversation failed:', error);
}

XAI on Metorial

Integrate XAI's powerful language models with over 600 tools and services using Metorial's streamlined, MCP-powered platform. Our open-source Python and TypeScript SDKs make it simple to connect Grok and other XAI models to the integrations your AI agents need—from productivity suites and CRMs to databases and communication platforms. With Metorial, adding sophisticated integration capabilities to your XAI applications takes just a couple of lines of code, dramatically reducing time to market and eliminating the maintenance burden of custom integration code. Whether you're building conversational AI, data analysis tools, or intelligent automation with XAI's cutting-edge models, Metorial provides the infrastructure to connect seamlessly with real-world tools and workflows. Our platform handles all the complexity of authentication, API calls, rate limiting, and error handling automatically, so you can focus on crafting unique AI experiences rather than wrestling with integration challenges. Join developers who are shipping faster by leveraging Metorial's battle-tested integration layer—let us manage the connections while you concentrate on innovation and creating value for your users with XAI's advanced language models.

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About Metorial

Metorial provides developers with instant access to 600+ MCP servers for building AI agents that can interact with real-world tools and services. Built on MCP, Metorial simplifies agent tool integration by offering pre-configured connections to popular platforms like Google Drive, Slack, GitHub, Notion, and hundreds of other APIs. Our platform supports all major AI agent frameworks—including LangChain, AutoGen, CrewAI, and LangGraph—enabling developers to add tool calling capabilities to their agents in just a few lines of code. By eliminating the need for custom integration code, Metorial helps AI developers move from prototype to production faster while maintaining security and reliability. Whether you're building autonomous research agents, customer service bots, or workflow automation tools, Metorial's MCP server library provides the integrations you need to connect your agents to the real world.

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