io.github.VirajMishra1Published by GitHub account VirajMishra1v0.1.0Updated Oct 24, 2026
Discover public AI agents, reusable recipes, and trusted benchmark evidence by task.
A public, read-only MCP server for discovering AI agents, their reusable recipes, and trusted benchmark evidence by task.
Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.
Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.
Read https://sourceweft.com/mcp/io.github.VirajMishra1%2Fbench/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.Add this to your client's mcpServers config.
{
"mcpServers": {
"bench-agent-discovery": {
"type": "http",
"url": "https://bench.virajmishratakehome.workers.dev/mcp"
}
}
}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]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:
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.
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:
That's the whole SDK. Everything else is optional.
Python:
Already on OpenTelemetry? Point your exporter at Bench instead:
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:
Drop-in wrappers that auto-instrument your existing LLM calls:
Bench exposes a public MCP server at /mcp. Connect it to Claude Code:
Or Codex:
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.
Bench runs entirely on Cloudflare. Each product is doing a specific job:
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.
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.
MIT — see LICENSE
Built by @virajm1shra on Cloudflare.
Source: README.md at commit 3d8bc04
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.
sortlimitmodelquerylicensecategoryreusableverifiedframeworkliveCallablemaxP50LatencyMsmaxCostPerRunUsdGet a public agent
Get one public agent's recipe, public capability manifest, coarse invocation status, owner telemetry, and verified benchmark submissions.
handlerequiredList verified benchmark contracts
List public, versioned benchmark contracts and only their trusted-runner-verified submissions.
Takes no parameters.
100% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
npm install @virajmishra1/bench-sdkexport 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;});pip install bench-observeexport 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)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"npx @virajmishra1/bench-cli init --installnpx @virajmishra1/bench-cli login// 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";claude mcp add --transport http bench https://bench.virajmishratakehome.workers.dev/mcpcodex mcp add bench --url https://bench.virajmishratakehome.workers.dev/mcpSDK (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)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();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 deploybench/+-- 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