spring-ai-playground

Name: spring-ai-playground
Author: spring-ai-community

Verified

Safe local execution layer for AI agent tools. Build, validate, and publish MCP tools with a no-pass-no-run workflow — cross-platform desktop app powered by Spring AI.

122stars

30forks

Java

Installation

# Add to your Claude Code skills
git clone https://github.com/spring-ai-community/spring-ai-playground

Getting Started

Guides for using ai agents skills like spring-ai-playground.

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Security ReportVerified

Last scanned: 5/30/2026

{
  "issues": [],
  "status": "PASSED",
  "scannedAt": "2026-05-30T16:31:40.551Z",
  "npmAuditRan": true,
  "pipAuditRan": true
}

README.md

Frequently Asked Questions

What is spring-ai-playground?

spring-ai-playground is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by spring-ai-community. Safe local execution layer for AI agent tools. Build, validate, and publish MCP tools with a no-pass-no-run workflow — cross-platform desktop app powered by Spring AI. It has 122 GitHub stars.

Is spring-ai-playground safe to use?

Yes. spring-ai-playground passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install spring-ai-playground?

Clone the repository with "git clone https://github.com/spring-ai-community/spring-ai-playground" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is spring-ai-playground written in?

spring-ai-playground is primarily written in Java. It is open-source under spring-ai-community on GitHub, so you can review or fork the full source.

Are there alternatives to spring-ai-playground?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh spring-ai-playground against similar tools.

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Spring AI Playground

Safe Local Execution Layer for AI Agent Tools

Spring AI Playground is a cross-platform desktop app for building, testing, validating, and executing MCP tools in a controlled local environment. It helps you create reusable MCP tools once and use them across macOS, Windows, and Linux through a self-contained runtime. Unlike platforms that focus primarily on generating agents or authoring tools, Spring AI Playground focuses on making the tools it manages inside the app safer and easier to inspect before reuse.

No pass, no run.

Every tool you build earns a Local Pass — a local test-run with your sample arguments. Only passing tools are added live to the built-in MCP server and become callable from Agentic Chat. A tool that has not passed is never exposed to an agent.

Safe execution does not end at publication. Every chat, tool call, vector lookup, and MCP invocation that runs in the app lands in the built-in Observability dashboards — twelve panels (Overview, Tokens & Cost, AI Models, Tool Studio, MCP Servers, MCP Inspector, Vector Database, Agentic Chat, Host, Web Application, Logs, Traces) backed by a ring buffer with dated disk persistence. Drill from a row into the trace timeline and raw spans, jump to the conversation thread, and deep-link back into Agentic Chat — so the tools you let an agent call are also the tools you can see in detail after the fact.

In Tool Studio, new or updated built-in tools are test-run before they are published to the built-in MCP server. You do not need to know Java, Spring, or JVM internals to use it. If you can install a desktop app and write a small JavaScript function, you can build tools here and connect them to hosts and clients such as Claude Desktop, Claude Code, Cursor, IDEs, and other MCP-compatible environments.

Ships with a bundled catalog of default tools across five source bundles — web fetch, datetime, math, security, encoding, crypto, filesystem, GitHub, Wikipedia, weather, finance, geo, and a Korean-domain bundle (Upbit, Bithumb, Naver, Kakao, KMA, KOFIC, KRX, data.go.kr keychain) — searchable and filterable in the Default Tools directory.

Plus a preset catalog of external MCP servers — Gmail, Notion, Slack, GitHub, Linear, Atlassian, Tavily, Firecrawl, Microsoft-Teams, Sentry, and more — grouped by category with ${ENV_VAR} placeholders so disabled servers can't be activated without setup. Browse the full list in the Default MCP Catalog.

The Problem

AI agents can generate tools quickly, but generated tools are not inherently safe to execute.

It is often unclear what actually runs at execution time
Failures are difficult to predict before real usage
Execution is not easily traceable or inspectable

Most platforms focus on creation.

Very few make verification part of the default workflow for built-in tool publication, and even fewer leave a clear trail of what each tool actually did after it ran. Spring AI Playground treats both as part of safe local execution — Local Pass at the gate, Observability dashboards on the inside.

Who is this for?

Developers building MCP tools who want validation built into the default workflow
Teams running MCP tools in agent host environments (Claude Code, Cursor, IDE plugins, mixed-stack runtimes) who need a stable local execution layer instead of ad-hoc local scripts
Teams who need a clear, local audit trail of token cost, latency, and per-call traces for chat, tool, vector, and MCP invocations
Spring developers looking for a working end-to-end reference implementation of Spring AI's ChatClient, MCP client/server, RAG advisor, vector store, and Observation API
Users of Claude Desktop, Claude Code, Cursor, and other MCP-compatible environments

Quick Start

The fastest path is the desktop app distributed through GitHub Releases.

Spring AI Playground is a standalone desktop app, so you can install it and start building MCP tools without setting up a Java project, Docker environment, or source build first.

1. Download the Desktop App

Choose the installer for your platform from the latest release:

Each badge resolves to the latest published release automatically and opens a confirm dialog with the filename, size, and OS-specific default save path. The downloaded file keeps the version in its name (e.g. spring-ai-playground-<version>-mac-arm64.dmg). Or browse all available assets on the Releases page.

2. Install and Launch

Install the app like a normal desktop application, then launch Spring AI Playground from your applications menu.

The desktop app bundles the backend runtime together with a launcher that provides provider starter templates, YAML override editing, environment-variable based secret handling, and one-click launch.

If you install the app, you can run Spring AI Playground immediately without setting up Docker or running the source manually.

macOS

Gatekeeper may block the install flow in two places:

When you open the downloaded DMG, macOS may show a warning such as “cannot be opened because the developer cannot be verified.” If you trust the release source, go to System Settings > Privacy & Security and click Open Anyway.

After copying the app into Applications, macOS may block the first app launch again. If that happens, open the app once, then return to System Settings > Privacy & Security and click Open Anyway.

If the app still doesn’t open because it remains quarantined, and you trust the app, one practical workaround is:
xattr -dr com.apple.quarantine "/Applications/Spring AI Playground.app"
Windows

The most common warning appears when you run the downloaded installer (.exe).

If Microsoft Defender SmartScreen shows a warning such as “Windows protected your PC” or says the app is unrecognized:

Click More info

Then click Run anyway

Linux

Separate Gatekeeper- or SmartScreen-style reputation warnings are uncommon. When installing the .deb or .rpm package, you usually only need to complete the normal package-install confirmation steps.

For more detailed platform guidance, see the Getting Started guide.

Verify Your Download

Every release ships with a matching .sha256 checksum file and a Sigstore SLSA build provenance attestation. See Verify Your Download in the docs for the exact shasum, Get-FileHash, and gh attestation verify commands.

The desktop launcher handles first-run setup on one screen — provider config, Default MCP Tools curation, and JVM/environment cards — and includes an Ollama model manager to review, search, and download models. See Getting Started and Model Configuration.

Documentation

Detailed installation, configuration, features, and tutorials live in the documentation site:

Documentation site: https://spring-ai-community.github.io/spring-ai-playground/
Getting Started: https://spring-ai-community.github.io/spring-ai-playground/getting-started/
Application Architecture: https://spring-ai-community.github.io/spring-ai-playground/architecture/
AI Agent Tool Safety Architecture: https://spring-ai-community.github.io/spring-ai-playground/safety-architecture/ — defense-in-depth sandbox model, policy resolution, threat model, and per-tool Risk Level reference
Features: https://spring-ai-community.github.io/spring-ai-playground/features/
Tutorials: https://spring-ai-community.github.io/spring-ai-playground/tutorials/

Alternative runtimes are still supported. The same Spring Boot fat JAR drives every channel; switching to a stdio MCP transport is opt-in via the mcp-stdio Spring profile.

For the app/web experience (default — streamable-http MCP server on port 8282, Vaadin UI front and center):

Docker — docker run -p 8282:8282 -v spring-ai-playground:/root ghcr.io/spring-ai-community/spring-ai-playground
Local source run — ./mvnw -Pproduction spring-boot:run

For the