Benzi

Language Agnostic · Model Agnostic · Compiles Locally · BYOK · MCP Compatible

Contents

— What is Benzi —

— Live demos —

— Language support —

— Getting started —

— SWE-bench Verified —

— How it works —

— Toolset —

— Features —

— Benzi vs CC, DSH, OC —

— FAQ and Benzi's map vs LSP/SCIP indexers —

What is Benzi

Most AI coding agents grep and glob raw source code into AI model's context window and hope the model finds what matters. Benzi parses every file first — a real compiler, built on tree-sitter — into a precise, queryable map. Every symbol, every call edge, every reference, every class in its inheritance chain. One pass, done.

Every file parsed, imports resolved, class ancestry built, every identifier traced to its definition — before a single question is answered. Call flow and data flow join at every call site, so a bad value traces to its origin in one tool call. Claude Code greps; Cursor embeds; Aider maps signatures; Benzi resolves — in O(1). Ten languages so far, plus a markup engine for HTML, CSS, and DOM-JS — see Language support. Benzi is free to use during beta.

You can try pasting this repo's link to Benzi in the live demo too!

Benzi on SQLite

Like where this is headed? A ⭐ goes a long way.

Live demos

[StallionSwipe](BENZI_GREENFIELDING_EXAMPLES/horse_tinder/) · Python, HTML, CSS, JS — a dating app for horses, greenfielded by Benzi from scratch in a single chat session. No image is a file: every horse portrait is procedural SVG, generated in code. Match with one and it flirts back through a real model, live. Frontend, backend, and the prompts — all written by Benzi. Try it live.

Or, try any repo of your choice at all here — point Benzi at any public GitHub repo and it builds the index live. varianttech.net/demo.

Language support

Python · JavaScript · TypeScript · Java · C# · C++ · C · Go · Rust · Ruby

One compiler, ten languages — each is a tree-sitter grammar plugin, so the core of the map (symbols, call edges, references, inheritance, data flow) is built the same way everywhere.

Depth is uneven. Python is deepest, and the only one with the runtime tracer. Every language reaches the core of the map, but each has its own constructs, not all modelled yet. For most code, every other language's map comes close to Python's.

Incremental reindexing is also less optimized for C, C++, Rust, and Ruby — it works, just not as fast on a large edit loop.

Wrong or thin answer in your language? Open an issue with the repo, the question, and what it got wrong — that's how the uneven parts get found.

Getting started

Benzi is completely free to use during beta. There are two ways to use it, and they share one login: the same benzi-login config works everywhere.

1. The Benzi agent — Benzi's own agent, running on the compiled index, in VS Code or from the terminal. These features are part of the agent itself, so they come with VS Code and headless and are not available over MCP:

• Runtime tracer — runs your code and records what actually happened: which target each ambiguous call really hit, with the real argument values.
• Static-analysis-gated edits — every write is syntax-checked and checked against a fresh index before the next step; an edit that breaks the code is rolled back instead of built on.
• Blast radius before and after every edit — who calls the symbol, who holds it, and what feeds its parameters, before the change and again once it lands.
• Self-aware upgrade to pro — when a task outgrows the model it's running on, the agent upgrades itself to a larger model, then drops back when the task is done. Set BENZI_ESCALATE_MODEL=deepseek-pro (or another model alias) to turn it on.
• Persistent memory and rollback — per-repo facts that survive restarts, and one-step undo of any edit.

Run it either way:

• VS Code extension — VS Code Marketplace

1. Install Benzi from the VS Code Marketplace.
2. Click the Benzi icon in the left activity bar (circled below) to open the Benzi: Settings panel.
3. Pick your model and paste your API key: an Anthropic key, or a non-Anthropic one (DeepSeek, Groq, Kimi and others).
4. Enter your email and press Send, then type in the one-time code it emails you.
5. Press Save model & keys, then Run Benzi.

• Headless CLI — PyPI

The same steps as the VS Code extension, from your terminal: install, log in, set your model and key, then run.

bash pip install benzi

Python 3.11–3.14 on Windows, macOS and Linux. Installs the benzi-login, benzi-headless and benzi-mcp commands.

1. Log in — emails you a one-time code to type in.

bash benzi-login --login you@example.com

2. Set your model and key

bash benzi-login --model deepseek benzi-login --nonanthropic-key YOUR_KEY # or for Claude models: benzi-login --anthropic-key YOUR_KEY

3. Run it on a repo — from the terminal, for scripts and CI.

bash benzi-headless . "fix the failing test in parser.py"

1. The Benzi compiler as an MCP server — the compiled index, served as tools to the agent you already use: Claude Code, Cursor, or any MCP client. This is the map without Benzi's agent loop, so output quality depends on your agent, and the agent features above aren't included.

The steps are the same as headless: pip install benzi, then log in with benzi-login. You can skip the model and key, because your own agent does the model calls. Then register benzi-mcp with your agent instead of running benzi-headless. For example, in Claude Code, once for all your projects:

   claude mcp add --scope user benzi -- benzi-mcp

benzi-mcp then serves whichever folder you start Claude Code in, so switching projects needs nothing extra. To pin it to one repo no matter where you start, pass that repo's path:

   claude mcp add --scope user benzi -- benzi-mcp /path/to/your/repo

VS Code's settings panel and benzi-login share one config: log in once, and VS Code, headless and MCP all use it.

SWE-bench Verified

The full SWE-bench Verified set — 500 real GitHub issues from twelve Python repositories — run end to end on DeepSeek V4.1-Flash, one attempt per instance, graded by the official swebench.harness.run_evaluation inside its own per-instance Docker images, with network access to GitHub and PyPI blocked inside every container. 413 of 500 resolved (82.6%) for $9.26 in total, 1.85¢ per task, with 97.6% of input tokens served from cache.

Full technical report: swebench-october-run2/SWE_BENCH_REPORT.md (web version). Every instance's cost, tokens and turns: varianttech.net/benchmark_swebench. The cross-harness efficiency comparison below (and the full 24-bug chart): varianttech.net/benchmark.

<img src="https://raw.githubusercont