Bench Agent Discovery

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "bench-agent-discovery": {
      "command": "npx",
      "args": [
        "-y",
        "@virajmishra1/bench-cli"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

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:

Terminal

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

server.ts

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:

Terminal

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

server.ts

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:

server.ts

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 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:

Terminal

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

What you get

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

Framework adapters

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

server.ts

// Anthropic — wraps every messages.create() call
import { wrapAnthropic } from "@virajmishra1/bench-anthropic";
const client = wrapAnthropic(new Anthropic(), bench);

// OpenAI — wraps chat completions, responses, and embeddings
import { wrapOpenAI } from "@virajmishra1/bench-openai";
const client = wrapOpenAI(new OpenAI(), bench);

// Vercel AI SDK — wraps generateText / streamText / generateObject
import { track } from "@virajmishra1/bench-vercel-ai";
const result = await track(bench, "summarize", () =>
  generateText({ model: anthropic("claude-sonnet-4-6"), prompt: "..." })
);

// Mastra
import { wrapMastra } from "@virajmishra1/bench-mastra";

Let your AI find agents

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

Terminal

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:

Code

SDK (npm: @virajmishra1/bench-sdk)
        |  batched events, X-Bench-Key
        v
POST /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

Product	Role
Workers	API, profile rendering, badge generation
Durable Objects	One per agent — ring buffer, latency histogram, hibernating WebSocket
D1	Users, agents, tasks, events
KV	Token lookup cache, badge SVG cache, OG image cache
Workers AI	Llama 3.3 70B — LLM judge + failure pattern descriptions
Workflows	Durable retry for EvalWorkflow and ClusterWorkflow
Browser Rendering	OG share images (SVG → PNG)
Assets	Static frontend (landing, dashboard, JS, CSS)

SDK reference

server.ts

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 automatically
await 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 event
agent.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/bench
cd bench && npm install

cd packages/worker
npx wrangler login

# Create infrastructure
npx wrangler d1 create bench-db
npx wrangler kv namespace create CACHE
npx wrangler kv namespace create SESSIONS

# Paste the returned IDs into wrangler.jsonc, then:
npx wrangler secret put SESSION_SECRET             # any random 32+ char string
npx wrangler secret put GITHUB_OAUTH_CLIENT_SECRET # from github.com/settings/developers

# Apply schema and deploy
npm run db:remote
npm run deploy

File layout

Read the full README →View source on GitHub →

Related MCP Servers

View all in Developer Tools View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

npm downloads

Package downloads in the last 30 days.

Last commit

1mo ago

Most recent push to the default branch.

Directory activity

1 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Bench Agent Discovery

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "bench-agent-discovery": { "command": "npx", "args": ["-y","@virajmishra1/bench-cli"] } }

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

Live Dynamic SVG Preview

Bench Agent Discovery AllMCPs Directory Badge

Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/bench-agent-discovery?style=directory)](https://allmcps.com/mcp/bench-agent-discovery)

HTML Embed

<a href="https://allmcps.com/mcp/bench-agent-discovery"><img src="https://allmcps.com/api/badge/bench-agent-discovery?style=directory" alt="Bench Agent Discovery on AllMCPs" /></a>

Technical Specs & Signals

CategoryDeveloper Tools

3/6 checks healthy over the last 45d

40Quality signal: Fair · 40/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools16/30

Adoption & activity4/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 4d ago via OSV.dev · @virajmishra1/bench-cli (npm)

★ Spotlight Slot

Feature Your MCP Server

Get maximum visibility for your server across our directory, search results, and detail pages.

Spotlight Your Server

Own this project?

This directory is pre-filled from public sources. Claim via GitHub README, site badge, or DNS TXT to unlock edit access and the Official badge — proof is checked automatically, then reviewed by our team.

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to Bench Agent Discovery →Install in Claude Desktop Install in Cursor Install in VS Code Setup guides for all 13 MCP clients

Bench Agent Discovery

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "bench-agent-discovery": {
      "command": "npx",
      "args": [
        "-y",
        "@virajmishra1/bench-cli"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

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)

Or do it manually:

Terminal

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

server.ts

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:

Terminal

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

server.ts

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:

server.ts

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 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:

Terminal

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

What you get

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

Framework adapters

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

server.ts

// Anthropic — wraps every messages.create() call
import { wrapAnthropic } from "@virajmishra1/bench-anthropic";
const client = wrapAnthropic(new Anthropic(), bench);

// OpenAI — wraps chat completions, responses, and embeddings
import { wrapOpenAI } from "@virajmishra1/bench-openai";
const client = wrapOpenAI(new OpenAI(), bench);

// Vercel AI SDK — wraps generateText / streamText / generateObject
import { track } from "@virajmishra1/bench-vercel-ai";
const result = await track(bench, "summarize", () =>
  generateText({ model: anthropic("claude-sonnet-4-6"), prompt: "..." })
);

// Mastra
import { wrapMastra } from "@virajmishra1/bench-mastra";

Let your AI find agents

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

Terminal

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:

Code

SDK (npm: @virajmishra1/bench-sdk)
        |  batched events, X-Bench-Key
        v
POST /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)

Cloudflare products used

Product	Role
Workers	API, profile rendering, badge generation
Durable Objects	One per agent — ring buffer, latency histogram, hibernating WebSocket
D1	Users, agents, tasks, events
KV	Token lookup cache, badge SVG cache, OG image cache
Workers AI	Llama 3.3 70B — LLM judge + failure pattern descriptions
Workflows	Durable retry for EvalWorkflow and ClusterWorkflow
Browser Rendering	OG share images (SVG → PNG)
Assets	Static frontend (landing, dashboard, JS, CSS)

SDK reference

server.ts

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 automatically
await 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 event
agent.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/bench
cd bench && npm install

cd packages/worker
npx wrangler login

# Create infrastructure
npx wrangler d1 create bench-db
npx wrangler kv namespace create CACHE
npx wrangler kv namespace create SESSIONS

# Paste the returned IDs into wrangler.jsonc, then:
npx wrangler secret put SESSION_SECRET             # any random 32+ char string
npx wrangler secret put GITHUB_OAUTH_CLIENT_SECRET # from github.com/settings/developers

# Apply schema and deploy
npm run db:remote
npm run deploy

File layout

Read the full README →View source on GitHub →

Related MCP Servers

View all in Developer Tools View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

npm downloads

Package downloads in the last 30 days.

Last commit

1mo ago

Most recent push to the default branch.

Directory activity

1 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Bench Agent Discovery

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "bench-agent-discovery": { "command": "npx", "args": ["-y","@virajmishra1/bench-cli"] } }

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

Live Dynamic SVG Preview

Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/bench-agent-discovery?style=directory)](https://allmcps.com/mcp/bench-agent-discovery)

HTML Embed

<a href="https://allmcps.com/mcp/bench-agent-discovery"><img src="https://allmcps.com/api/badge/bench-agent-discovery?style=directory" alt="Bench Agent Discovery on AllMCPs" /></a>

Technical Specs & Signals

CategoryDeveloper Tools

3/6 checks healthy over the last 45d

40Quality signal: Fair · 40/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools16/30

Adoption & activity4/15

Community engagement0/10

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 4d ago via OSV.dev · @virajmishra1/bench-cli (npm)

★ Spotlight Slot

Feature Your MCP Server

Get maximum visibility for your server across our directory, search results, and detail pages.

Spotlight Your Server

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.