I built and published a Rust AI agent from scratch in my journey to learn Rust.
GitHub: https://github.com/Tejas1Koli/rust-ai-agent
crates.io: https: https://crates.io/crates/rust-ai-agent
What it currently supports: • Local LLM execution with Ollama • DuckDuckGo web search • Async execution with Tokio • Structured logging • Typed configuration + validation • Cross-platform binaries (Linux/macOS/Windows) • Single-binary CLI distribution One thing I found interesting while building this: The entire source code is currently around ~25 KB. An equivalent LangChain-style AI agent would usually end up much larger because of: framework layers → wrappers → middleware → orchestration abstractions Rust forced me to think more directly about: • Runtime flow • Async execution • Tool orchestration • Error handling • Deployment • System design decisions Compared to many Python-based AI agents, Rust gives: • Lower runtime overhead • Better concurrency handling • Stronger type safety • Easier deployment through compiled binaries • Less dependency/runtime bloat Building this made me realize that AI tooling is increasingly becoming a systems engineering problem, not just a prompting problem. Still improving: • Provider abstractions • Streaming • Memory/runtime systems • Better orchestration flow You can try it with: cargo install rust-ai-agent P.S. I used tutorials/documentation while building parts of this, but implementing and shipping it taught me much more than just watching content.