fortunto2 - Overview

Hi, I'm Rustam

I build AI agents in Rust. Not because it's trendy, but because agents that run 104 tasks in parallel need zero-GC, and tools that process video/audio on device need real performance. Python for glue and ML pipelines, Rust for everything that ships.

Founder of SuperDuperAI | Blog: rustman.org

Why Rust for agents

Most agent frameworks are Python wrappers around API calls. That works until you need parallel tool execution, sub-second response times, or WASM deployment. I build the infrastructure layer in Rust: the LLM client, the agent loop, the tools. Then concrete agents inherit the stack and add domain logic.

The crate tree

openai-oxide            LLM client (caching, WebSockets, structured outputs)
  └─ sgr-agent          Agent framework (structured CoT, function calling, providers)
       ├─ sgr-agent-core     Tool trait, FileBackend trait, AgentContext
       ├─ sgr-agent-tools    14 reusable tools (read, write, search, eval, apply_patch...)
       ├─ sgr-agent-ml       ONNX embeddings, centroid classifier, adaptive k-NN
       └─ agents built on top:
            ├─ agent-bit      Competition agent (PAC1 benchmark, 74/104)
            ├─ rust-code      Terminal coding agent (TUI, MCP, skills)
            └─ supervox-agent Voice agent (live translate, post-call analysis)

openai-oxide is the foundation. Persistent WebSockets, SIMD JSON, hedged requests. Published on crates.io, npm, PyPI.

sgr-agent sits on top. Two-phase function calling (reasoning then action), provider routing, parallel tool execution. The FileBackend trait means the same tools work over RPC, local filesystem, or in-memory mocks.

sgr-agent-tools is the reusable toolkit: smart search (fuzzy + Levenshtein), batch read, JS eval via Boa engine, Codex-compatible diffs.

sgr-agent-ml handles on-device ML: ONNX bi-encoder embeddings, cosine-similarity classifier, adaptive k-NN with persistence. Powers agent-bit's security detection (MiniLM + DeBERTa NLI) without API calls.

Agents built on this

Agent	What	Score
agent-bit	PAC1 competition: CRM workspace, security detection, 15 skills, ONNX classifiers	74/104 (GPT-5.4)
rust-code	Terminal coding agent: TUI, tmux tasks, MCP, fuzzy search	daily driver

Both share the same stack. agent-bit adds ONNX security classifiers and a pipeline state machine. rust-code adds TUI and filesystem integration. The framework handles the common 80%.

More about the architecture: How I spent $250+ on an AI agent competition

Other Rust tools

Project	What	Install
airq	Air quality CLI. Sensor + model merge, WASM core	`brew install fortunto2/tap/airq`
visa-photo	Biometric visa photos. AI background removal, Dioxus desktop	`brew install fortunto2/tap/visa-photo`
supervox	Voice toolkit. STT, VAD, TTS, mic capture	`cargo add voxkit`
OpenWok	Open-source food delivery. Dioxus fullstack, QR payments, privacy-first

Python: agent infra & tools

Project	What
solo-factory	Claude Code plugin. 27 skills, 3 agents, full startup pipeline
solograph	Code intelligence MCP server. FalkorDB + tree-sitter, KB search, session history
seo-cli	SEO CLI. Google Search Console, Bing, Yandex, IndexNow
invoice-pdf-crm	File-based CRM. PDF invoices, letters, company cards

Stack

Rust (agents, tools, WASM) | Python (ML, MCP servers, CLI) | TypeScript (web) | Swift (iOS) | Kotlin (Android)