Comment by vinhnx

Comment by vinhnx 10 hours ago

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I'm currently building my own coding agent, VT Code. VT Code is a Rust-based terminal coding agent with semantic code intelligence via Tree-sitter (parsers for Rust, Python, JavaScript/TypeScript, Go, Java) and ast-grep (structural pattern matching and refactoring).

It supports multiple LLM providers: OpenAI, Anthropic, xAI, DeepSeek, Gemini, OpenRouter, Z.AI, Moonshot AI, all with automatic failover, prompt caching, and token-efficient context management. Configuration occurs entirely through vtcode.toml, sourcing constants from vtcode-core/src/config/constants.rs and model IDs from docs/models.json to ensure reproducibility and avoid hardcoding. [0], [1], [2]

Recently I've added Agent Client Protocol (ACP) integration. VT Code is now a fully compatible ACP agent, works with any ACP-clients: Zed (first-class support), Neovim, marimo notebook. [3]

[0] https://github.com/vinhnx/vtcode

[1] https://crates.io/crates/vtcode

[2] https://docs.rs/vtcode

[3] https://agentclientprotocol.com/overview/agents

Thank you!

OldOneEye 10 hours ago

This looks very exciting! I'm following it and I'll give it a go. Not that I'm unsatisfied with Claude Code for my amateur level, but it's clear incentives are not exactly aligned when using a tool from the token provider xD

I love that you've made it open source and that it's in Rust, thanks a lot for the work!

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  • vinhnx 10 hours ago

    Thank you for your kind words. This is my own research into how coding agent works in practice, I love to explore the underlying technologies of how Claude Code, and Codex and coding agent works in general.

    I choose Rust since I have some familiarity and experience with it, VT Code is of course, AI-assisted, I mainly use Codex to help me build it. Thank you again for checking it out, have a great day! : )

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kordlessagain 4 hours ago

Great job! Keep it up - we need more of these and open to boot.

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  • vinhnx 3 hours ago

    Thank you! I'm glad that you find this project useful. VT Code my current passion on how agent coding works and how far I can push myself to build one (with AI-assisted). I will keep developing and improve it. Currently I'm planning to run Terminal-bench to see how VT Code performs.

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sim04ful 9 hours ago

You’ve done an incredible amount of work — I’m definitely going to try out the Zed integration.

I’m curious though, how significant do you think it is for the agent to have semantic access through Tree-sitter?

Also what model have you had the most success with ?

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  • vinhnx 8 hours ago

    Thank you for your very kind-words. I love building and agentic coding is my current curiosity.

    > I’m curious though, how significant do you think it is for the agent to have semantic access through Tree-sitter?

    For this, I'm really not sure, but since the start of building VT Code. I just had this idea to use tree-sitter to assist the agent to have more (or faster/more precise) semantic understanding of the coding, instead of relying them to figure out themself. For me, naively I think this could help agent to have better language-specific and accurately decision about the workspace (context) that they are working. If not having tree-sitter, I think the agent could eventually figure out itself. For this aspect, I should be research more on this topic. In VT Code, I included 6 language: Go, Python, Rust, TypeScript, Swift... ) via rust-binding crates, mostly when you launch the vtcode agent on any workspace, It will show the main languages in the workspace right way.

    > Also what model have you had the most success with ?

    I'm having mainly limited-budget so I can only use OpenRouter and utilize its vast amount models support. So that I can prototype quickly, for different use-cases. For VT Code agent, I'm using mainly x-ai/grok-code-fast-1, in my experience, it most suit for building VT Code agent it self because of speeds, and versatile in function calling and have good instruction following. I also have good successes with x-ai/grok-4-fast. I have not tried claude-4.5-sonnet and gpt-5/gpt-5-codex though. I really love to run benchmarks for VT Code to see how it perform in real world coding task, I'm aiming for Aider polygot bench, terminal-bench and swe-bench-lite, it is in my plan for now in my GitHub issues.

    For VT Code itself, I instruct it to strictly follow system-prompt, in which I take various inspiration from Anthropic, OpenAI and Devin guide/blogs on how to build coding agent. But, for a model-agnostic agent, the capability to support multi providers and multi models is a challenge. For this I think I need help. I'm fortunately to have support from open-source community suggesting me to use zig, I have had good success with it so far, for implement LLM calls and implement the /model picker.

    Overall in my experience building VT, the most important aspect of effective coding agent is context engineering, like all big-lab has research. A good system prompt is also very important, but not context is everything. https://github.com/vinhnx/vtcode/blob/main/prompts/system.md

    // Sorry, English is not my main language, so pardon the typo and grammar. Thank you!

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