Agent Cookbook

This cookbook provides ready-to-use snippets for integrating SecuriX into your agentic loops using popular frameworks.

LangChain (Node.js)

When using LangChain, you can wrap your tool calls in a handler that catches SecuriX's llmError and returns it as a tool result.

import { DynamicTool } from "@langchain/core/tools";
import { Securix } from "@securix/core";

const gmailSearchTool = new DynamicTool({
  name: "gmail_search",
  description: "Search for emails in the user's Gmail account.",
  func: async (input) => {
    try {
      const response = await fetch(
        "https://{provider}.api.securix.app/v1/users/me/messages",
        {
          headers: {
            "securix-api-key": process.env.SECURIX_API_KEY,
            "securix-entity-id": "user_123",
            "securix-agent-id": "gemini_vscode_1",
          },
        },
      );

      const data = await response.json();

      if (response.status === 401 && data.llmError) {
        return data.llmError; // The LLM will follow these instructions
      }

      return JSON.stringify(data);
    } catch (e) {
      return "Error fetching emails. Please try again later.";
    }
  },
});

OpenAI Function Calling

If you are using the raw OpenAI SDK, you can handle the SecuriX response in your execution loop.

import OpenAI from "openai";

const openai = new OpenAI();

async function runAgent(prompt: string) {
  const runner = openai.beta.chat.completions
    .runTools({
      model: "gpt-4-turbo",
      messages: [{ role: "user", content: prompt }],
      tools: [
        {
          type: "function",
          function: {
            name: "get_emails",
            description: "Get recent emails",
            parameters: { type: "object", properties: {} },
          },
        },
      ],
    })
    .on("functionCall", async (call) => {
       // Logic to call SecuriX Proxy
       const res = await fetch("https://{provider}.api.securix.app/...", { ... });
       const data = await res.json();

       if (data.llmError) {
         // Return the LLM error as the function result
         return data.llmError;
       }

       return data;
    });

  const finalContent = await runner.finalContent();
  console.log(finalContent);
}

Vercel AI SDK

Using the tool helper from the Vercel AI SDK:

import { tool } from "ai";
import { z } from "zod";

export const gmailTool = tool({
  description: "Search messages in Gmail",
  parameters: z.object({
    query: z.string().describe("The search query"),
  }),
  execute: async ({ query }) => {
    const response = await fetch(
      `https://{provider}.api.securix.app/v1/users/me/messages?q=${query}`,
      {
        headers: {
          "securix-api-key": process.env.SECURIX_API_KEY,
          "securix-entity-id": "user_123",
          "securix-agent-id": "gemini_vscode_1",
        },
      },
    );

    const data = await response.json();

    if (response.status === 401 && data.llmError) {
      // The model will see this text and explain to the user how to fix it
      return {
        message: data.llmError,
        requiresAction: true,
      };
    }

    return data;
  },
});

Best Practices

Don't Swallow 401s: Always check for llmError on 401 responses. This is what enables the self-healing loop.
Entity Isolation: Always pass the correct securix-entity-id for the user currently interacting with the agent.
Graceful Degradation: If the SecuriX Gateway is unreachable, ensure your agent has a fallback or a clear error message.

Agent Cookbook

Agent Cookbook

LangChain (Node.js)

OpenAI Function Calling

Vercel AI SDK

Best Practices

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