Your AI agent is about to pay an invoice. Screwtape checks it first.

Copy curl

curl -s https://screwtape.ai/v1/judge \
  -H "Authorization: Bearer $SCREWTAPE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"task":"State the crew size and launch site.","agent_output":"The crew size was 4. The mission launched from Cape Canaveral.","evidence":["The mission launched on 2024-03-01 from Cape Canaveral.","The crew size was 4."],"criteria":["crew size","Cape Canaveral"]}'

Copy Python

import json
import os
import urllib.request

key = os.environ["SCREWTAPE_API_KEY"]
payload = {
    "task": "State the crew size and launch site.",
    "agent_output": "The crew size was 4. The mission launched from Cape Canaveral.",
}
request = urllib.request.Request(
    "https://screwtape.ai/v1/judge",
    data=json.dumps(payload).encode(),
    headers={
        "Authorization": "Bearer " + key,
        "Content-Type": "application/json",
    },
    method="POST",
)
print(urllib.request.urlopen(request).read().decode())

Copy TypeScript

const key = process.env.SCREWTAPE_API_KEY;
const payload = {
  task: "State the crew size and launch site.",
  agent_output: "The crew size was 4. The mission launched from Cape Canaveral.",
};
const response = await fetch("https://screwtape.ai/v1/judge", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${key}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify(payload),
});
console.log(await response.json());

Copy MCP

{
  "mcpServers": {
    "screwtape": {
      "url": "https://screwtape.ai/mcp",
      "headers": {
        "Authorization": "Bearer ${env:SCREWTAPE_API_KEY}"
      }
    }
  }
}

Copy CrewAI

class ScrewtapeJudgeTool(BaseTool):
    name: str = "screwtape_judge"
    description: str = "Judge an agent output. Returns JSON with verdict, score, and reasons."
    args_schema: type[BaseModel] = JudgeInput

    def _run(self, task: str, agent_output: str) -> str:
        return json.dumps(screwtape_post("/v1/judge", {"task": task, "agent_output": agent_output}))

Copy LangGraph

def judge(state: State) -> dict:
    payload = {"task": state["task"], "agent_output": state["agent_output"]}
    return {"verdict": screwtape_post("/v1/judge", payload)}

graph = StateGraph(State)
graph.add_node("judge", judge)
graph.set_entry_point("judge")
graph.add_edge("judge", END)
app = graph.compile()