Prototyping AI Agents Still Takes Too Long

In recent months, everyone seems to be building their own AI agent, and for good reason. The potential is huge.

But here’s the catch: even testing a simple idea often takes hours of setup. You need to choose a framework, spin up a backend, write boilerplate for tool use, manage memory, handle chat history. And that’s before even validating if the idea is worth it.

I kept running into the same wall:

I just want to see if this agent idea works , why is it so much effort?

That’s why I built something different.

Introducing Agent Playground

Agent Playground is a lightweight tool to help you prototype fully functional AI agents in 2 minutes. It’s built on top of LangGraph and LangChain, but doesn’t require any of the usual setup or boilerplate. You just need to fill out a short form with your agent’s details, select the tools you want to use, and you’re ready to go.

Here’s why it changes the prototyping game:

• Built-in memory, chat history using Postgresql

• Connect to 1,000+ real-world tools via MCP (Model Context Protocol), powered by Smithery.ai

• Bring your own API key and use any model from OpenAI or Google — the Playground abstracts away provider differences

• Instant API access so you can use your agent in your app or frontend that you’re building

• Built specifically for prototyping MVPs, validating ideas, or building personal AI assistants

Here is a quick look at the form you fill out to create an agent:

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Create Agent Form

All you need is your own API key , and a Smithery account to use MCP tools, then you are good to start prototyping with Agent Playground.

Agents architecture

Each agent in Agent Playground is built on the ReAct architecture, a proven pattern that enables multi-step reasoning and dynamic tool use.

ReAct blends reasoning and action, allowing agents to think through tasks step by step.

All of this is handled behind the scenes, so you can focus on testing ideas — not building the plumbing.

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ReAct Architecture (Source: IBM)

Key Features

Built-in Memory, Tool Calling & Chat History

Every agent you create comes with memory and a chat history context, allowing for more coherent conversations and multi-step logic.

Tool calls are handled automatically based on the model’s output, before executing any tool, the model asks for confirmation, so you can review and modify the input if needed.

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Validate Tool Calls

Tool Integrations via MCP

Connect your agent to real-world apps like GitHub, Slack, Notion, Web search, and more — without writing any integration code. The Playground is powered by Smithery.ai, giving you access to 1,000+ MCP servers, each offering one or more tools.

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MCP Servers in Agent Playground

Just select the tools you need, the Playground handles the rest. (You’ll need a Smithery account to access MCP tools.)

Model-Agnostic: OpenAI & Google Supported

Bring your own API key and use any model from OpenAI or Google — the Playground abstracts away the provider logic.

Full API Access — Build on Top Instantly

Every agent you create comes with a full set of REST APIs so you can plug it into your frontend, backend, or product.

You can:

• Send chat messages

• Retrieve conversation history

• Chain responses into multi-step flows

• Combine multiple agents into a custom orchestration layer

Whether you’re building a small tool or prototyping a full multi-agent system , you don’t need to rebuild from scratch. Just call the APIs of your existing prototypes.

Example 1: Chat with Your Agent

POST /v1/agent/chat/prototype/:prototypeId
Content-Type: application/json
Authorization: Bearer YOUR_AGENTAILOR_API_KEY

{
"threadId": "string",
  "content": "string",
  "resume": {
    "action": "continue",
    "data": {
      "key": "value"
    }
  }
}

The response includes the agent’s reply, updated state, and any tool calls made during processing.

Example 2: Retrieve Agent Conversation History

GET /v1/agent/history/:threadId
Authorization: Bearer YOUR_AGENTAILOR_API_KEY

You’ll get the full conversation history , useful for rendering transcripts or debugging.

[
  {
    "message": {
      "id": "message-12345",
      "type": "human",
      "content": "Hello !"
    },
    "interrupts": []
  },
  {
    "message": {
      "id": "message-67890",
      "type": "ai",
      "content": "Hello there! How can I help you today?",
      "tokenUsage": {
        "promptTokens": 35,
        "completionTokens": 10,
        "totalTokens": 76
      }
    },
    "interrupts": []
  }
]

You can find out more about the API endpoints in the Agent Playground API documentation.

Build Whatever You Imagine

Want to test multiple agents with different roles?
Want to chain them into a lightweight orchestrator?
Want to plug them into a frontend or SaaS demo?

You can do all of that , and fast.

Agent Playground handles the hard stuff (memory, tools, chat logic) so you can focus on building and validating.

Ready to try it yourself before watching the demo? Launch Playground, it takes less than 2 minutes.

See It in Action

Here’s a real example of what you can build with Agent Playground , in under 2 minutes.

What’s happening in the video:

I created an agent called “Agent Researcher”, designed to help users find information, analyze data, and synthesize insights.

Using the Playground, I gave it access to two tools:

• The Exa MCP server for web search

• My personal Notion workspace via the Notion MCP server

The agent:

1. Searched the web for information on reasoning vs non-reasoning AI models

2. Synthesized the findings

3. Wrote a short summary directly into Notion

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Message with Agent Researcher

All of this happened within a few messages, with the agent using the web search tool to gather information and then writing a summary into Notion.

Launch Your Agent in 2 Minutes — Free and Dev-Ready

If you’re building with AI or experimenting with agent workflows, you shouldn’t need to spend hours setting up infrastructure just to test an idea. All you need is an API Key and your imagination. It’s completely free , and ready when you are. Launch Agent Playground

Who is It For?

Agent Playground is built for developers, indie hackers, and technical founders who want to experiment with AI agents , without getting stuck in setup hell.

It’s especially useful if:

• You’re validating a new product idea or workflow

• You want to build an internal tool that uses LLMs + real tools

• You’re testing multi-agent coordination

• Or you’re tired of rebuilding the same agent boilerplate again and again

If you’ve ever thought “I just want to see if this works before I commit” , this is for you.

Feedback, Bugs, and Ideas Welcome

The Agent Playground is still in beta, and I’m actively building based on what people need.

If you try it and hit a bug, have a feature request, or just want to brainstorm ideas , I’d genuinely love to hear from you. You can also join us on the Agentailor Discord to share your thoughts and connect with other builders.

Let’s make prototyping AI agents as easy as spinning up a web app. Launch Agent Playground