adk-rust

s compile down to production Rust code - Build and run agents as native executables directly from the studio The goal is to let people prototype agent workflows visually, then ship them as fast, memory-safe Rust binaries instead of long-running JS/Python services. Making AI Agents with ADK Studio is super simple: 1. ADK-Studio install: `cargo install adk-studio` 2. Start ADK Studio server: `adk-studio --port 6000` 3. Open in browser: open http://localhost:6000 I would really appreciate feedback from folks building agent systems, workflow engines, or AI inference infrastructure — especially around design tradeoffs vs existing tools like n8n. Project site: https://adk-rust.com GitHub: https://github.com/zavora-ai/adk-rust Best James

earch, Code Execution) + custom tools MCP support - Model Context Protocol integration Sessions & Memory - InMemorySessionService, DatabaseSessionService Streaming - Full streaming support for real-time responses Telemetry - OpenTelemetry integration for tracing/metrics A2A Protocol - Agent-to-Agent communication Quick Example use adk_rust::prelude::*; #[tokio::main] async fn main() -> Result<()> { let agent = LlmAgentBuilder::new() .name("my_agent") .model(GeminiModel::new("gemini-2.0-flash")?) .instruction("You are a helpful assistant.") .build()?; let response = agent.run("Hello!").await?; println!("{}", response); Ok(()) } Links Crates.io: https://crates.io/crates/adk-rust Docs: https://docs.rs/adk-rust Website: https://adk-rust.com/ GitHub: https://github.com/zavora-ai/adk-rust Looking for Feedback I'd love to hear from the community: What agentic features would you prioritize? Any interest in contributing or testing? Use cases where a Rust implementation would be valuable? This is an independent community project, not officially affiliated with Google, but designed to be compatible with the ADK ecosystem. Thanks for reading!