Thank you for checking out VT Code and great questions! Somehow I am only able to get the comment now (4 hours late), sorry!

I will answer your questions

> does ast-grep run first as a structural filter, with ripgrep for content matching, or are they used independently depending on the query type?

ast-grep and ripgrep are used independently depending on the query type, not in a sequential filtering relationship. In VT Code's, the `unified_search` tool provides two distinct search actions/args: `grep` and `structural`. Where, `grep` action uses ripgrep for broad text matching and quick file-content sweeps, which calls system `rg` binary or falls back to default grep (I also use my own perg, which is my cli for grep). The `structural` action uses ast-grep (https://ast-grep.github.io/) for syntax-aware search, read-only project scans, and rule tests, it directly executes ast-grep binary without any ripgrep preprocessing.

The routing is based on your search needs, design based on semantic:

- Plain text search -> `grep` action (ripgrep)

- Syntax-aware search -> `structural` action (ast-grep)

I design VT Code's system prompt to explicitly states to prefer `grep` (rg) for broad text search and prefer `structural` (ast-grep) for syntax-sensitive search.

> How are you handling the schema translation?

VT Code handles schema translation through a unified abstraction layer with provider-specific translation methods, avoiding provider-specific code paths in the main application logic. (https://github.com/vinhnx/vtcode/blob/main/vtcode-core/src/l...) The system uses a unified `LLMProvider` trait with shared request/response types, then handles provider-specific translations at the boundary layer.

Different providers have varying role support, handled through the `MessageRole` enum with provider-specific string conversion methods:

- OpenAI: Supports `system`, `user`, `assistant`, `tool` roles directly. Based on OpenAI Completion/Response API roles definition.

- Anthropic: Converts tool responses to `user` messages, handles system messages separately.

- Gemini: Maps `assistant` to `model`, handles system messages as `systemInstruction`.

Each provider implements the `LLMProvider` trait and handles its own schema conversion internally. For example, the Anthropic provider converts requests in `convert_to_anthropic_format()`.

> ACP support is interesting. I haven't seen many agents implement it yet, mostly MCP. Is your read that ACP is going to gain adoption, or is including both more about hedging?

I support and adopt ACP since it was first announced and introduced from Zed. And the currently VT Code run with almost all ACP-compatible clients (Zed, Minamo Notebook, Jetbrain, Toad...). See: https://github.com/vinhnx/VTCode/blob/main/docs/guides/zed-a.... I'm not sure about ACP adoption metric but ACP integration helps VT Code run on more surfaces (eg: environments).

> The local inference angle (LM Studio, Ollama) matters for use cases where source code can't leave the network. Have you benchmarked which open models hold up reasonably for tool-calling-heavy workflows? In my experience most local models below 70B struggle with multi-turn tool use even when their raw code generation is decent.

Currently, local inference support via LM Studio and Ollama is still early and experiment in VT Code and I haven't run it much and yet to have benchmarks with open models since I don't have enough VRAM. But it definitely in my checklist and could get helps from the comminity if anyone interest.

Thank you!