9 real-world MCP projects for AI engineers covering: - RAG - Memory - MCP client - Voice Agent - Agentic RAG - and much more! Find them in the GitHub repo below.

Oct 26, 2025 · 6:31 AM UTC

27
221
5
1,383
GitHub repo: github.com/patchy631/ai-engi… (don't forget to star it ⭐ )
1
3
18
If you found it insightful, reshare it with your network. Find me → @_avichawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.
2
Replying to @_avichawla
Great share! + The repo shared here has more than 90 hands on project on AI engineering!
1
2
🚀
1
Replying to @_avichawla
Someone should build a pitchbook MCP. Now that would be valuable
1
Would a guidebook work 😅?
Avi Chawla
 @_avichawla
Jul 25
The ultimate MCP illustrated guidebook (free)! 75+ pages that cover: - The MCP fundamentals (explained visually). - 11 hands-on projects for AI engineers (covered with code). 100% hands-on.
1
more replies
Replying to @_avichawla
How can I understand MCP, do we need it really or we can use some alternatives?
1
Avi Chawla
 @_avichawla
Jul 25
The ultimate MCP illustrated guidebook (free)! 75+ pages that cover: - The MCP fundamentals (explained visually). - 11 hands-on projects for AI engineers (covered with code). 100% hands-on.
Replying to @_avichawla
How can we contribute to this pipeline? Our Conversational AI Engine handles the transport and orchestration from voice capture ("speech input") and Audio Response ("speech output"). Pick your STT, LLM, and TTS providers. You can even bring your own LLM.
2
Replying to @_avichawla
Make sure to dive deep into one project first before jumping between them. Helps solidify skills and understanding rather than spreading too thin.
Replying to @_avichawla
A few months back I got hands on knowledge about MCP through building MCP for Obsidian. Easy to understand and informative. Do star the GitHub repo if you find it useful - github.com/pradyumnac26/Obsi…
5
Replying to @_avichawla
The Voice-to-Voice AI Agent workflows would be much better if you use @AgoraIO’s Conversational AI. The agents never miss a word, in any real world environment.
1
Replying to @_avichawla
Thanks Avi, These MCP projects are awesome I love the voice agent idea, Thanks for sharing the repo.
1
Replying to @_avichawla
exploring RAG right now, any tips?
1
Replying to @_avichawla
Thanks a lot for this.
Replying to @_avichawla
Love seeing MCP projects in action! Exciting to watch the ecosystem grow with real-world use cases like these. Let’s keep pushing what’s possible with open standards.
Replying to @_avichawla
CORE MCP provides the memory infra for AI agents github.com/RedPlanetHQ/core

GitHub - RedPlanetHQ/core: Build your personal memory system to power your AI apps.

Build your personal memory system to power your AI apps. - RedPlanetHQ/core

github.com
Replying to @_avichawla
Replying to @_avichawla
Nice! Thanks for the share!
Replying to @_avichawla
Great list!
Replying to @_avichawla
Oh, there is MCI missing, can someone add it? usemci.dev/

Model Context Interface - MCI Documentation

usemci.dev
Replying to @_avichawla
These MCP projects look really useful for practical AI implementation, especially the agentic RAG example
Replying to @_avichawla
RAG/Memory/Voice are component stacks. The true value is beyond the stack: the autonomous execution layer and persistent environments for reliable, multi-step agentic systems. Focus on state persistence, not just steps.
Replying to @_avichawla
Essential MCP implementations for serious AI engineers! Hands-on systems that move beyond theory and into production-grade intelligence.
Replying to @_avichawla
Incredible list 🔥 Curious to know — which of these projects do you think will have the biggest real-world impact on how we learn with AI? 🤖📚
Replying to @_avichawla
Great share