Getting started

How FindAgent works

FindAgent is a cross-LLM marketplace of focused AI agents. Find one, connect it to the AI app you already use, and put it to work — in three steps.

Step 1

Find an agent

Browse the marketplace by industry or discipline, or search for what you need. Each agent has a public page with what it does and who built it. Looking for a remote MCP server specifically? Browse the MCP directory.

Step 2

Connect it to your AI app

Every agent works the same way across clients. Open an agent and follow its steps — for a hosted, doer, or code agent, connect it by adding its MCP server URL; for a local recipe, install it with the FindAgent CLI — to wire it into:

Claude
ChatGPT
Gemini
Cursor
the FindAgent CLI
any MCP client

See the connect guide for per-client setup.

Step 3

Use it inside your app

Once connected, the agent runs where you already work — no new tool to learn. Start a fresh conversation and put it to use.

Two ways to browse

The marketplace is organized on two axes, and an agent can be tagged on both:

Industry — the vertical an agent serves: Healthcare, Finance, Retail, Real Estate, and more.
Discipline — the kind of work it does: Customer Service, HR, Cybersecurity, Legal, Software Development, and more.

Start from the Browse page by Industry or by Discipline, then narrow with the dual-lens header: pick an Industry lens and a Discipline lens and you see their intersection — “Sales agents for Real Estate.” Either lens can stay “Any.” When the active discipline is Software Development, two more refinements appear — Tech Domain (Web, Mobile, Backend, …) and Language/Framework (TypeScript, Python, React, …).

What is an agent here?

Agents on FindAgent come in a few shapes:

Prompt recipe. A packaged system prompt and example flow — instructions your AI app follows.
Tool-using (doer) agents. Agents that call real tools — fetch your analytics, build a spreadsheet, manage a store.
Skills bundles. A Claude, Cursor, Windsurf, or Continue skills-or-rules repo turned into one agent — each skill becomes a prompt your client can call.
Code agents. A creator's real GitHub code, run 1:1 in an isolated sandbox after review. FindAgent reads the repo as-is and generates the agent from it — the creator never writes a config file.
MCP servers. A remote MCP server someone already hosts, listed so you can discover and connect it.
Departments. Two to sixteen agents (up to eight for peer-to-peer) composed into one team that runs as a single MCP server.

Building one yourself? Publish in the submit wizard — or skip the forms and submit straight from your AI client over MCP, where your own assistant reads your repo and writes the listing.

Read the docs

Ready to start?

Browse the marketplace and connect your first agent.

Browse agents

01Getting started

How FindAgent works

FindAgent is a cross-LLM marketplace of focused AI agents. Find one, connect it to the AI app you already use, and put it to work — in three steps.

Browse agents Read the docs

02Three steps

Find it, connect it, use it

Every agent works the same way across clients — no privileged one, no new tool to learn.

Step 1

Find an agent

Step 2

Connect it to your AI app

Open an agent and follow its steps — for a hosted, doer, or code agent, add its MCP server URL; for a local recipe, install it with the FindAgent CLI — to wire it into:

ClaudeChatGPTGeminiCursorthe FindAgent CLIany MCP client

See the connect guide for per-client setup.

Step 3

Use it inside your app

Once connected, the agent runs where you already work — no new tool to learn. Start a fresh conversation and put it to use.

03Two axes

Two ways to browse

The marketplace is organized on two axes, and an agent can be tagged on both.

Industry

The vertical an agent serves: Healthcare, Finance, Retail, Real Estate, and more.

Discipline

The kind of work it does: Customer Service, HR, Cybersecurity, Legal, Software Development, and more.

04Agent kinds

What is an agent here?

Agents on FindAgent come in a few shapes.

Prompt recipe

A packaged system prompt and example flow — instructions your AI app follows.

Tool-using (doer) agents

Agents that call real tools — fetch your analytics, build a spreadsheet, manage a store.

Skills bundles

A Claude, Cursor, Windsurf, or Continue skills-or-rules repo turned into one agent — each skill becomes a prompt your client can call.

Code agents

A creator's real GitHub code, run 1:1 in an isolated sandbox after review. FindAgent reads the repo as-is and generates the agent from it — the creator never writes a config file.

MCP servers

A remote MCP server someone already hosts, listed so you can discover and connect it.

Departments

Two to sixteen agents (up to eight for peer-to-peer) composed into one team that runs as a single MCP server.

Building one yourself? Publish in the submit wizard — or skip the forms and submit straight from your AI client over MCP, where your own assistant reads your repo and writes the listing.

Ready to start?

Browse the marketplace and connect your first agent.

Browse agents

Frequently asked questions

What is an agent on FindAgent?

Agents come in a few shapes: recipe agents (a packaged system prompt and example flow), tool-using "doer" agents that call real tools, skills bundles, code agents (a creator's real code run 1:1 in an isolated sandbox after review), remote MCP servers, and departments (two to sixteen agents — up to eight for peer-to-peer — composed into one team that runs as a single MCP server).

Which AI apps can I use FindAgent agents with?

Agents work across clients — Claude, ChatGPT, Gemini, Cursor, the FindAgent CLI, and any MCP client. It is the same open protocol either way, with no privileged client.

How do I connect an agent to my AI app?

Open an agent and follow its steps: install it locally with the FindAgent CLI, or, for a hosted agent, add its MCP server URL as a connector. See the connect guide for per-client setup.