Bench Agent Discovery MCP server

bench

See what the best agents do differently.

One line of code. Live dashboard, public profile, README badge.

What is this?

You built an AI agent. You ran it a few times. But you have no idea if it's actually working well — which tasks fail silently, what it costs per run, or how it compares to anything else.

Bench fixes that. Wrap your agent with one function call. You get:

• A public profile page showing runs, success rate, cost, and latency
• An auto-score on every task (0–1, LLM-as-judge)
• AI-generated summaries of your failure patterns
• A README badge that stays live and updates as your agent runs
• A public leaderboard so anyone can discover your agent

It's like GitHub for agents — observable, shareable, and public by default.

Want to see it before signing up? Try the sandbox at /try — no signup needed.

Setup (3 minutes)

Sign in at bench.virajmishratakehome.workers.dev with GitHub. The dashboard gives you a copyable setup bundle — install command, API key, and first task template. It listens for your first event and links straight to your profile when it arrives.

Or do it manually:

npm install @virajmishra1/bench-sdk
export BENCH_KEY="bk_..."

import { observe } from "@virajmishra1/bench-sdk";

const agent = observe({ apiKey: process.env.BENCH_KEY, agent: "my-agent" });

await agent.task("search", { query }, async (t) => {
  const result = await doSearch(query);
  t.log("found", result.length);
  t.cost(0.004);
  return result;
});

That's the whole SDK. Everything else is optional.

Python:

pip install bench-observe
export BENCH_KEY="bk_..."

import bench

agent = bench.observe(api_key=os.environ["BENCH_KEY"], agent="my-agent")

async with agent.task_ctx("search", {"query": query}) as task:
    result = await do_search(query)
    task.log("found", len(result))
    task.set_output(result)

Already on OpenTelemetry? Point your exporter at Bench instead:

export OTEL_EXPORTER_OTLP_ENDPOINT=https://bench.virajmishratakehome.workers.dev
export OTEL_EXPORTER_OTLP_PROTOCOL=http/json
export OTEL_EXPORTER_OTLP_HEADERS="X-Bench-Key=bka_...,X-Bench-Agent=my-agent"

Bench understands standard genai. spans — invokeagent, executetool, chat, retrieval, and more.

Prefer a CLI? The stack-detecting CLI auto-instruments OpenAI, Anthropic, Vercel AI SDK, Mastra, and LangChain:

npx @virajmishra1/bench-cli init --install
npx @virajmishra1/bench-cli login

What you get

Shortened. The full README is on GitHub.