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.
Rust #AI #SystemsProgramming #OpenSource #DeveloperTools #LocalAI #Tokio #Ollama
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