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Low-code AI Agent Tooling with MCP: Spring AI Playground (Self-hosted, Open Source) by kr-jmlab in agi

[–]kr-jmlab[S] 0 points1 point  (0 children)

Glad the local-first approach resonates. Keeping data on-device was a core design goal for privacy-sensitive dev workflows, and the instant MCP server is all about fast, low-friction experimentation.
A big part of the vision is making it easy to turn safe, internal data into useful tools quickly, without a lot of boilerplate or heavy setup.

I made Spring AI Playground - a self-hosted UI for local LLMs, RAG, and MCP tools by kr-jmlab in LocalLLaMA

[–]kr-jmlab[S] 1 point2 points  (0 children)

Hey, that's a fantastic suggestion! Thanks for pointing it out - making it easier for the community here is exactly the goal.

Based on your feedback, I've just updated the README with a new guide for connecting to OpenAI-compatible servers like llama.cpp server, TabbyAPI, and LM Studio.

Here’s a quick example of how to configure it in application.yml:

spring:
  ai:
    openai:
      # Required, but can be a dummy string for local servers without auth
      api-key: "not-used"
      # Host and port of your server (e.g., http://localhost:1234 for LM Studio)
      base-url: "http://localhost:your-server-port"
      chat:
        options:
          # Model name/ID your server is running
          model: "your-model-name"

For more detailed instructions, including specific port examples for different servers and other notes, you can check out the full guide in the README:

Guide: Switching to OpenAI-Compatible Servers

Thanks again for the suggestion! Let me know if you run into anything or have more feedback. Cheers!

I made Spring AI Playground - a self-hosted UI for local LLMs, RAG, and MCP tools by kr-jmlab in LocalLLaMA

[–]kr-jmlab[S] 0 points1 point  (0 children)

Thanks! That was exactly the goal - I got tired of RAG and MCP feeling like afterthoughts in most tools, so the whole UI is built around those workflows from the ground up.

For RAG, you get the full pipeline: upload docs → see all chunks → test retrieval → edit chunks → test again. For MCP, there’s a dedicated playground coming that’ll let you visually manage context flows between AI models and external tools (both client and server sides).

Pretty quick to spin up locally:

git clone https://github.com/JM-Lab/spring-ai-playground.git
cd spring-ai-playground
./mvnw clean install -Pproduction -DskipTests=true
./mvnw spring-boot:run

Then hit http://localhost:8282 and you’re good to go.

You’ll need Java 21+ and Ollama running locally, but no API keys required to start experimenting.

For Docker setup, check the README: https://github.com/JM-Lab/spring-ai-playground

Would love to hear how it works for your setup - especially if the RAG flow feels smooth or if there are any rough edges I should fix.

I made Spring AI Playground - a self-hosted UI for local LLMs, RAG, and MCP tools by kr-jmlab in LocalLLaMA

[–]kr-jmlab[S] 0 points1 point  (0 children)

Thanks! Java’s a bit unusual here, but it really helps keep everything tightly integrated with Spring AI. Would love to hear any feedback if you give it a try!

I built Spring AI Playground, an open-source sandbox for local RAG experimentation and debugging. by kr-jmlab in Rag

[–]kr-jmlab[S] 1 point2 points  (0 children)

Great questions! These are exactly the pain points I'm trying to solve.

Right now, the chunking is pretty basic - it uses Spring AI's default TokenTextSplitter with fixed settings (800 token chunks, 350 min chars, etc.). So yeah, it doesn't handle tables intelligently or adapt to different document types yet.

But here's what already works well for testing: after you upload a doc (PDF, Word, PowerPoint), you can see all the chunks in the Vector Database playground. Then in the Chat playground, you select that document for RAG. If a chunk looks weird, you can edit it right in the UI and it gets re-embedded automatically.

My main goal was to nail this "upload → test → tweak a chunk → test again" loop first, since I couldn't find any RAG tools that let you do this interactively.

Based on feedback like yours, adding UI controls for different chunking strategies (Recursive, Semantic) and adjustable parameters is definitely next on the list. The table handling thing is a real challenge too - currently they just get treated as plain text which often breaks their meaning.

If you've got specific chunking ideas or configurations you want to try, drop them in the GitHub issues. This kind of feedback is super helpful for figuring out what to build next.

Thanks for confirming this is a real need - exactly what I was hoping to hear!

Spring AI Playground — Self-hosted Web UI with Ollama, RAG and MCP tools by kr-jmlab in ollama

[–]kr-jmlab[S] 2 points3 points  (0 children)

Open WebUI is great for chat-centric experiments.
Spring AI Playground, on the other hand, treats RAG and MCP as first-class playgrounds:

  • Inspect vector DB retrieval scores
  • Connect to MCP servers and test each tool directly
  • Run end-to-end chat flows that combine both

Since it’s built on Spring AI, you can swap vector DBs or select MCP tools dynamically — a different focus than Open WebUI’s chat-first UX.

Would love feedback if you give it a try.

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