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The Anthropic SDK doesn’t have lifecycle hooks, so Benchspan wraps your Claude call with a decorator (Python) or wrapCall helper (TypeScript). Every user and tool message is scanned before the request goes out.

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Python

1

Install

pip install benchspan anthropic
2

Decorate the call

claude.py
from benchspan import BenchGuard, InjectionDetectedError
from anthropic import Anthropic

guard = BenchGuard(api_key="ag_live_...", agent="claude-app")
client = Anthropic()

@guard.wrap
def ask_claude(messages):
    return client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        messages=messages,
    )

try:
    response = ask_claude(messages)
except InjectionDetectedError as e:
    print(f"Blocked: {e.result.verdict}, score={e.result.score:.4f}")
Use @guard.wrap_async for an async def function.

​
TypeScript

1

Install

npm install @benchspan/sdk @anthropic-ai/sdk
2

Wrap the call

claude.ts
import { BenchGuard, InjectionDetectedError } from "@benchspan/sdk";
import Anthropic from "@anthropic-ai/sdk";

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

try {
  const response = await guard.wrapCall(messages, () =>
    client.messages.create({
      model: "claude-sonnet-4-6",
      max_tokens: 1024,
      messages,
    }),
  );
} catch (e) {
  if (e instanceof InjectionDetectedError) {
    console.log(`Blocked: score=${e.result.score.toFixed(4)}`);
  }
}

​
Tool use

Claude’s tool-use loop means tool outputs flow back into the messages array as tool_result blocks. Pass those through as role: "tool" messages so Benchspan scans them: 
# After the first Claude call with tools
for block in response.content:
    if block.type == "tool_use":
        tool_output = run_tool(block.name, block.input)
        messages.append({
            "role": "user",
            "content": [{
                "type": "tool_result",
                "tool_use_id": block.id,
                "content": tool_output,
            }],
        })

# Next ask_claude() call will scan the tool_output before sending
response = ask_claude(messages)
For scanning tool-result blocks specifically, the simplest pattern is an explicit scan right after the tool runs: 
tool_output = run_tool(block.name, block.input)
guard.scan(tool_output, role="tool", source=block.name)  # raises on injection
messages.append({...tool_result block...})