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Documentation Index

Fetch the complete documentation index at: https://docs.benchspan.com/llms.txt

Use this file to discover all available pages before exploring further.

The Vercel AI SDK supports language-model middleware. Benchspan ships an asMiddleware() helper that scans the prompt before the model is invoked.
TypeScript only. The Vercel AI SDK has no official Python equivalent.

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Install

npm install @benchspan/sdk ai @ai-sdk/openai

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Usage

route.ts
import { BenchGuard, InjectionDetectedError } from "@benchspan/sdk";
import { wrapLanguageModel, generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const guard = new BenchGuard({ apiKey: "ag_live_...", agent: "vercel-app" });

const model = wrapLanguageModel({
  model: openai("gpt-5"),
  middleware: guard.asMiddleware(),
});

try {
  const { text } = await generateText({
    model,
    prompt: userInput,
  });
  return Response.json({ text });
} catch (e) {
  if (e instanceof InjectionDetectedError) {
    return Response.json(
      { error: "Suspicious content detected" },
      { status: 400 },
    );
  }
  throw e;
}

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With streaming

Works identically with streamText: 
import { streamText } from "ai";

const result = streamText({
  model,   // wrapped model from above
  prompt: userInput,
});

return result.toDataStreamResponse();
If an injection is detected, the middleware throws before the stream is created, so your error handler fires normally.

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With tool calls

When tools are involved, the middleware scans tool outputs as they flow back into the prompt: 
const result = await generateText({
  model,
  prompt: "Summarize my latest email",
  tools: {
    read_email: tool({
      description: "Read an email by ID",
      parameters: z.object({ id: z.string() }),
      execute: async ({ id }) => mailClient.get(id).body,
    }),
  },
  maxSteps: 5,
});
Each time read_email returns and its output is added to the prompt, BenchGuard scans it. An injection in the email body aborts the run before the next model call.

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What gets scanned

Every user and tool message in params.prompt before the model call. system and assistant messages are skipped.