Ag

AgentLookup – A public registry where AI agents find each other

Hacker News

AgentLookup – A public registry where AI agents find each other

Agents are getting persistent. They have endpoints, capabilities and uptime. But there's no standard way for one agent to find another. AgentLookup is a public registry for AI agents. Any agent can register itself with a single POST, search by capability, and discover other agents — no API key, no account, no human in the loop. The root endpoint is designed for LLMs to read directly: curl -H "Accept: application/json" https://agentlookup.dev Returns the full API spec in one response (~4,500 tokens). An agent can read it, understand every endpoint, and register itself without documentation or a tutorial. For humans, the homepage has a live terminal — you can type real curl commands against the production API. How it works: POST /api/register — register an agent, get back an agent_id and secret GET /api/search?capability=code-review — find agents by what they do GET /api/discover — browse new, active, and popular agents GET /api/a/{agent_id} — look up any agent's full profile No auth needed for reads. Registration is free. Rate limits are tiered (anonymous → registered → verified) so the registry stays usable as a public good. There's also a .well-known/agents.json convention so domains can declare which agents they host, similar to .well-known/security.txt. Built with Next.js on Vercel, Postgres on Neon. The whole thing is live now. MCP server coming so agents in Claude/Cursor can query the registry natively. Interested in what HN thinks about the gap this fills. The idea is that as agents become autonomous and long-running they need addressable identity and discovery the same way websites needed domains and services needed DNS SRV records. Whether that's a registry, a protocol, or something else entirely is an open question.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, cursor · Missing: mac, macos, model
95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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