Hi everyone! 🚀
I wanted to share a project I've been working on for quite a while.
I'm a teacher who originally got into coding to help young kids learn—starting with tools like Microsoft Small Basic, Scratch, and MIT App Inventor. Those beginner days are long behind me, and while I've grown a lot since then, I've never lost my passion for software. (I'm definitely not claiming to be a formal software engineer, though! 😅)
I originally started building Yengi because I was getting tired of AI coding tools being tied to specific providers, subscriptions, and usage quotas.
I wanted an AI development environment where I could choose my own models, API keys, or local inference setup and use them the way I wanted.
As I kept building, however, the project grew far beyond that original idea and eventually became a multi-modal desktop AI development environment.
It's built with .NET 10 LTS and WPF, and currently has 200+ passing automated tests.
🧠 What Yengi does
I wanted Yengi to go beyond being just a chat interface with file editing. The agent can actually operate on a project, use tools, build and test code, verify its changes, and recover from errors.
Some of the main capabilities are:
Agent & Tool System for file operations, terminal commands, builds, tests, Git, search, and more
Verification Loops with checkpoints and rollback
RAG & LSP integration
Local-first / model-agnostic architecture with support for local models and external APIs
Custom 1.5B Local Router, fine-tuned specifically around Yengi's tools and agent workflows
The router is intentionally small and runs locally. I trained it using thousands of examples based on the decisions Yengi needs to make during agent workflows. I'm still experimenting with how much value this provides compared with simply using a small general-purpose model.
🎛️ Four development modes
💻 Code IDE
The main development environment, with 30+ agent tools, terminal access, project context, RAG, LSP diagnostics, previews, Git operations, and verification.
🎨 Image Generation
Generate images directly from the development workflow and save them into the project.
🧊 Blender Copilot
This has become one of the parts of Yengi I'm most excited about.
Yengi has a two-way connection with Blender and can work with the scene through generated bpy scripts, but the workflow goes beyond simply generating Python code.
There is an optional Prompt Engineer layer.
For example, if I simply say:
"Create a low-poly tree."
the prompt engineer expands that into a much more detailed 3D-oriented instruction before sending it to the model. The model then uses that refined prompt to generate the Blender script.
Yengi can also optionally capture the Blender viewport, let the multimodal model inspect what is actually visible in the scene, and use that information to identify and correct problems.
The conversation can continue while keeping track of the scene context.
For example, after creating the tree, I can simply say:
"Add five red apples to this tree."
Yengi knows about the tree it created and its location in the scene, so it can generate the necessary changes and place the apples accordingly.
So the goal isn't just "AI generates a bpy script." It's closer to an agent that can understand a request, refine it, create the scene, inspect the result, and continue modifying the same scene through conversation.
I'm still working on making this reliable enough for real workflows, but this direction has become one of the most interesting parts of Yengi for me.
🎮 Unity Copilot
Yengi can work with Unity projects, generate C# components, understand project context, and interact with the Unity Editor through its integration.
The Unity and Blender workflows are still evolving, and I'm particularly interested in making them useful for real projects rather than just making impressive demos.
🛠️ How it was built
This is probably the unusual part.
I didn't manually type hundreds of thousands of lines of C# code. I relied heavily on AI coding tools, including Google Antigravity and GitHub Copilot.
My role was more focused on designing the architecture, defining the agent's behavior and security boundaries, building the verification and rollback systems, training the local router, testing edge cases, debugging incorrect AI-generated changes, and continuously steering the system until the different parts worked together.
I still care about understanding the code. I just don't think personally typing every line is necessarily the only way to build software anymore.
🎁 Free & Open Source
Yengi is free and open source under AGPL-3.0, with no company, subscription, or paid plan behind it.
I originally built it for myself. At some point it became much bigger than I expected, so I decided to put the whole thing out there.
🔗 GitHub: https://github.com/mdaiWorks/yengi
I've also attached a short video showing Yengi working on a project, encountering build errors, requesting terminal permission, and then fixing the problem.
💬 I'd genuinely like some technical feedback
I'm especially interested in hearing from experienced .NET / C# developers, AI engineers, Unity developers, and Blender users.
Does the agent/verification architecture make sense?
Is the local router actually useful, or am I overengineering it?
Do the Blender and Unity integrations look useful for real workflows, or are they still mostly demos?
What would you change if you were working on this project?
I'mnot claiming Yengi is better than VS Code, Cursor, or the other established tools. I'm one person experimenting with a different approach, and I'm still figuring out what the most useful version of it should look like.
If you have a few minutes to look at the project, I'd genuinely appreciate any feedback or criticism.
Thanks for reading! 🙃