Bench Agent Discovery

io.github.VirajMishra1由 GitHub 账号 VirajMishra1 发布v0.1.0更新于 Oct 24, 2026

Discover public AI agents, reusable recipes, and trusted benchmark evidence by task.

已验证Streamable HTTP可网页运行知识与记忆AI 与机器学习

概览

AI 生成的概览

一个公开、只读的 MCP 服务器，用于按任务发现 AI 智能体、其可复用配方以及可信的基准评测证据。

可以做什么
按任务、能力、类别、框架、模型、已验证证据或复用配置搜索已列出的公开智能体，所有者遥测与基准证据分开返回。获取单个公开智能体的配方、公开能力清单、粗略调用状态、所有者遥测和已验证的基准提交。列出公开的、带版本的基准合约，以及仅由受信运行器验证的提交。
适用场景
当希望助手为某项任务寻找现成的公开智能体、查看某个智能体的配置方式，或在采用或比较智能体前确认哪些基准结果经过验证时适用。
运行要求
远程 streamable HTTP 端点；未声明需要认证、环境变量或请求头。需要能访问该托管服务的网络。
安装前请注意
仅用于只读发现，不会运行或安装智能体。结果只涵盖公开智能体，所有者自报的遥测与已验证的基准证据分开标注，对自报数据应谨慎看待。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Bench Agent Discovery，将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.VirajMishra1%2Fbench/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "bench-agent-discovery": {
      "type": "http",
      "url": "https://bench.virajmishratakehome.workers.dev/mcp"
    }
  }
}

README

bench

See what the best agents do differently.

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

[Live] [npm] [PyPI] [License] [Built on]

[Bench dashboard — live event stream, task history, eval scores, 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:

bash
npm install @virajmishra1/bench-sdkexport BENCH_KEY="bk_..."
typescript
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:

bash
pip install bench-observeexport BENCH_KEY="bk_..."
python
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:

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

Bench understands standard gen_ai.* spans — invoke_agent, execute_tool, chat, retrieval, and more.

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

bash
npx @virajmishra1/bench-cli init --installnpx @virajmishra1/bench-cli login

What you get

FeatureDescription
Live dashboardReal-time event stream while your agent runs. WebSocket, zero polling.
Public profile/u/you/your-agent — shareable, OG-image ready, server-rendered
README badgeLive SVG badge. Updates automatically. GitHub camo-friendly.
LLM evalEvery task auto-scored 0–1 by a Llama 3.3 70B judge. Score logic is open.
Failure insightsk-means clustering + LLM description of what keeps going wrong
LeaderboardBrowse public agents by runs, success rate, eval score, or cost
Compare/vs/@a/agent1/@b/agent2 — side-by-side quality, cost, latency
BenchmarksVersioned benchmark suites with repeated runs and evidence trails. Separate from self-reported telemetry.
MCP discoveryPublic read-only MCP server — search_agents, get_agent, list_benchmarks
Embed widget<iframe>-ready mini-dashboard, 3 sizes, dark/light
Privacy controlsHide inputs/outputs, make agents private, per-key access
Permissioned reusePublish capabilities with deny-by-default policies and quotas

Framework adapters

Drop-in wrappers that auto-instrument your existing LLM calls:

typescript
// Anthropic — wraps every messages.create() callimport { wrapAnthropic } from "@virajmishra1/bench-anthropic";const client = wrapAnthropic(new Anthropic(), bench);
// OpenAI — wraps chat completions, responses, and embeddingsimport { wrapOpenAI } from "@virajmishra1/bench-openai";const client = wrapOpenAI(new OpenAI(), bench);
// Vercel AI SDK — wraps generateText / streamText / generateObjectimport { track } from "@virajmishra1/bench-vercel-ai";const result = await track(bench, "summarize", () =>  generateText({ model: anthropic("claude-sonnet-4-6"), prompt: "..." }));
// Mastraimport { wrapMastra } from "@virajmishra1/bench-mastra";

Let your AI find agents

Bench exposes a public MCP server at /mcp. Connect it to Claude Code:

bash
claude mcp add --transport http bench https://bench.virajmishratakehome.workers.dev/mcp

Or Codex:

bash
codex mcp add bench --url https://bench.virajmishratakehome.workers.dev/mcp

Tools available: search_agents, get_agent, list_benchmarks. Search returns only public agents. Owner telemetry and benchmark evidence are labeled separately.

See MCP.md for full tool schemas and the privacy model.

Architecture

Bench runs entirely on Cloudflare. Each product is doing a specific job:

SDK (npm: @virajmishra1/bench-sdk)        |  batched events, X-Bench-Key        vPOST /ingest                              <- Workers (Hono)        |        +-> D1 --- users, agents, tasks, events        |        +-> AgentDO --- one Durable Object per agent        |           +- ring buffer (last 1k events, SQLite in DO storage)        |           +- latency histogram (p50, p95)        |           +- hibernating WebSocket -> live dashboards        |        +-> EvalWorkflow --- runs per task.end        |           +- Workers AI (Llama 3.3 70B) -> score 0-1 + reasoning        |              -> writes back to D1.tasks        |              -> updates agents.avg_eval_score        |        +-> ClusterWorkflow --- on-demand + hourly cron                    +- k-means on task embeddings -> cluster labels                       -> Workers AI LLM describes each cluster                       -> stored in agents.failure_clusters
Public surfaces:  /u/:login/:slug          -> profile page (server-rendered, OG image)  /badge/:login/:slug.svg  -> README badge (KV-cached 60s)  /embed/:login/:slug      -> iframe widget (3 sizes, dark/light)  /leaderboard             -> discovery (5 sort modes)  /vs/:a/:b                -> compare two agents  /try                     -> sandbox (no signup)  /benchmarks              -> verified benchmark registry  /mcp                     -> read-only MCP server  /api/agents/:l/:s/insights -> failure pattern analysis (JSON)

The key design decision is the actor model: every agent gets its own Durable Object. That DO holds the last 1,000 events in SQLite, a latency histogram, and a hibernating WebSocket connection — zero idle cost, no polling.

Cloudflare products used

ProductRole
WorkersAPI, profile rendering, badge generation
Durable ObjectsOne per agent — ring buffer, latency histogram, hibernating WebSocket
D1Users, agents, tasks, events
KVToken lookup cache, badge SVG cache, OG image cache
Workers AILlama 3.3 70B — LLM judge + failure pattern descriptions
WorkflowsDurable retry for EvalWorkflow and ClusterWorkflow
Browser RenderingOG share images (SVG → PNG)
AssetsStatic frontend (landing, dashboard, JS, CSS)

SDK reference

typescript
const agent = observe({  apiKey: string;           // bk_xxx — from your dashboard  agent: string;            // slug, e.g. "my-agent"  displayName?: string;  endpoint?: string;        // default: bench.virajmishratakehome.workers.dev  flushIntervalMs?: number; // default: 2000  maxBatchSize?: number;    // default: 50});
// Wrap a task — records start/end/duration/status/eval automaticallyawait agent.task("name", input, async (task) => {  task.log("label", value);    // attach a log event  task.cost(0.003);            // report LLM spend (owner-reported)  return result;               // returned value becomes the task output});
// Fire a custom eventagent.event("custom", { key: "value" });
// Flush immediately (auto-runs on batch full or interval)await agent.flush();

task.cost() calls are labeled "owner-reported" in the UI. Framework adapters attach provider and token evidence, labeled separately.

Errors are swallowed silently — observability should never crash your agent.

Self-host

bash
git clone https://github.com/VirajMishra1/benchcd bench && npm install
cd packages/workernpx wrangler login
# Create infrastructurenpx wrangler d1 create bench-dbnpx wrangler kv namespace create CACHEnpx wrangler kv namespace create SESSIONS
# Paste the returned IDs into wrangler.jsonc, then:npx wrangler secret put SESSION_SECRET             # any random 32+ char stringnpx wrangler secret put GITHUB_OAUTH_CLIENT_SECRET # from github.com/settings/developers
# Apply schema and deploynpm run db:remotenpm run deploy

File layout

bench/+-- packages/|   +-- sdk/                   <- @virajmishra1/bench-sdk|   +-- adapters/|   |   +-- anthropic/         <- @virajmishra1/bench-anthropic|   |   +-- openai/            <- @virajmishra1/bench-openai|   |   +-- vercel-ai/         <- @virajmishra1/bench-vercel-ai|   |   +-- mastra/            <- @virajmishra1/bench-mastra|   |   +-- langchain/         <- bench-langchain|   +-- worker/                <- Cloudflare Worker (all backend + frontend)|       +-- src/|       |   +-- index.ts       <- Hono routes|       |   +-- ingest.ts      <- POST /ingest|       |   +-- profile.ts     <- public profile page|       |   +-- badge.ts       <- SVG README badge|       |   +-- embed.ts       <- iframe widget|       |   +-- leaderboard.ts <- discovery page|       |   +-- compare.ts     <- /vs/:a/:b|       |   +-- do/agent.ts    <- AgentDO (actor per agent)|       |   +-- workflows/|       |       +-- eval.ts    <- LLM judge per task|       |       +-- cluster.ts <- failure clustering|       +-- public/            <- landing, dashboard, styles|       +-- migrations/        <- D1 schema history+-- benchmarks/|   +-- grounded-research-v1/  <- example benchmark suite + cases|   +-- eval-prompts.md        <- open-source judge prompts+-- examples/                  <- runnable example agents

License

MIT — see LICENSE

Built by @virajm1shra on Cloudflare.

来源：README.md，提交 3d8bc04

工具

3

search_agents

Search public AI agents

Find listed public agents by task, capability, category, framework, model, verified evidence, or reuse configuration. Owner telemetry and controlled benchmark evidence are returned separately.

输入参数sortlimitmodelquerylicensecategoryreusableverifiedframeworkliveCallablemaxP50LatencyMsmaxCostPerRunUsd

get_agent

Get a public agent

Get one public agent's recipe, public capability manifest, coarse invocation status, owner telemetry, and verified benchmark submissions.

输入参数handle必填

list_benchmarks

List verified benchmark contracts

List public, versioned benchmark contracts and only their trusted-runner-verified submissions.

无需输入参数。

版本历史

1
  1. v0.1.0最新Sep 16, 2026

评分

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