AI Assistant Case Study, Single-Agent vs. Multi Agent System and Popular MCP repos
I’ve been busy lately and finally got managed to spend sometime to write this post.
“Can our chatbot answer questions from Discord, GitHub, our website, and S3?”
That was the question our client asked us.
The answer? Yes, with GenAI and a little magic from RAG (Retrieval-Augmented Generation).
We helped them build a smart FAQ assistant that connects all their fragmented knowledge sources, Discord threads, GitHub repos, website content, and S3 files, into a single conversational interface.
No need to manually centralize or rewrite docs.
Just ask a question → get an instant, accurate answer → from wherever that knowledge lives.
- Deployed on AWS
- Powered by GPT
- Integrated into their existing chatbot
This is what real GenAI integration looks like, not just another chatbot, but a context-aware assistant that knows where your stuff is.
If you are keen to learn how it works, check out the case study here.
Single-Agent RAG system vs. Multi-Agent RAG
When building a GenAI solution, a Single-Agent RAG system does everything:
→ Routes queries to a source (vector DB, web, Discord, etc.)
→ Retrieves info, reasons through it, and generates a response.
It works… until it doesn’t.
But there is a problem here, one agent doing everything means limited depth, no specialization, and harder debugging.
That’s where Multi-Agent RAG comes in:
→ A retriever agent finds relevant info
→ A reasoner agent validates and structures it
→ A responder agent crafts the final output
→ All with tool and API access as needed
The result:
✅ Better context = more accurate answers
✅ Modular logic = easier to maintain and extend
✅ Clear roles = less hallucination, more reliability
Think of it like a kitchen with prep cooks, a head chef, and a server.
One person trying to do it all? Chaos. A team working in sync? Michelin-level execution.
𝘪𝘮𝘢𝘨𝘦 𝘤𝘳𝘦𝘥𝘪𝘵𝘴: 𝘸𝘦𝘢𝘷𝘪𝘢𝘵𝘦_𝘪𝘰
Opensource MCP frameworks available across different languages
MCP (Model Context Protocol) is getting more popular as the standard for connecting AI agents to tools, APIs, and enterprise systems.
If you're working on AI agents, here's a list of opensource MCP frameworks available across different languages:
🔹 FastMCP by Jeremiah Lowin: A high-level framework for building MCP servers in Python. Link.
🔹 FastMCP by Frank Fiegel: A high-level framework for building MCP servers in TypeScript. Link.
🔹 LiteMCP A high-level framework for building MCP servers in JavaScript/TypeScript. Link.
🔹 mark3labs/mcp-go: Golang SDK for building MCP Servers and Clients. Link.
🔹 mullerhai/sakura-mcp: Scala MCP Framework for building effective agents with MCP servers and MCP clients shade from modelcontextprotocol.io. Link.
🔹 salty-flower/ModelContextProtocol.NET: A C# SDK for building MCP servers on .NET 9 with NativeAOT compatibility Link
🔹 quarkiverse/quarkus-mcp-server: Java SDK for building MCP servers using Quarkus. Link.
Whether you're working in Python, Java, C#, or even building on Solana, there’s an MCP tool for you.
If you know any other frameworks, please share in the comment.