Building a Library-First AI Agent SDK in Rust

说明与用途

构建了一个 Rust AI 代理 SDK，可嵌入服务，降低内存占用，并通过稳定的 C ABI 支持插件扩展。

问题

Need an embeddable, performant Rust agent runtime instead of a TypeScript/Node CLI that hits memory limits on low‑spec servers.

结果

The Rust SDK enables embedding an AI agent into services, reduces memory usage, and allows plugin extensions via stable C ABI.

工作流

• 1. Design library‑first architecture with crates: rpi‑ai, rpi‑agent, rpi‑tools, rpi‑harness, rpi‑cli.
• 2. Implement minimal agent example using FauxProvider (no API key).
• 3. Define custom tool (AddTool) and integrate via AgentBuilder.
• 4. Bridge async Provider to sync StreamFn for compatibility.
• 5. Run example with cargo run and observe streaming output.
• 6. Replace FauxProvider with real provider (Anthropic/OpenAI) for production use.
• 7. Extend with native Rust cdylib plugins via rpi‑plugin‑sdk ABI.
• 8. Install extensions with rpi install <name> and use in CLI or embedded runtime.

证据

I was running an agent on a low-spec server and kept hitting memory limits. So I ported the core runtime to Rust.

来源

rpi is split into independent crates. Each layer can be used on its own: rpi‑ai, rpi‑agent, rpi‑tools, rpi‑harness, rpi‑cli.

来源

Run it with: cargo run

来源