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High-performance framework for building interactive multi-agent workflow systems in Rust

332 starsRust

Graph-flow – LangGraph-inspired AI agent workflows in Rust

Name: Graph-flow – LangGraph-inspired AI agent workflows in Rust
Availability: InStock
Author: alonagmon

by alonagmon·Apr 27, 2026·2 points·0 comments

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AI Analysis

●●SolidBig BrainShip It

LangGraph patterns in Rust with type safety, 300 stars and real production examples.

Strengths

•300 GitHub stars, 6,000 crates.io downloads — real adoption, not vaporware
•Production examples: insurance claims service with human-in-the-loop approval flows
•Combines Rig crate for LLM integration with custom graph execution engine

Weaknesses

•Agent orchestration is crowded — LangGraph, LlamaIndex, Temporal all compete here
•Rust AI ecosystem still early; smaller community than Python alternatives

Post Description

300 GitHub stars and 6,000 crates.io downloads later, graph-flow has been a fun experiment in building AI-agent workflows in Rust.

It is a LangGraph-inspired library focused on graph-based orchestration, stateful execution, conditional routing, persistence, and type safety. The goal was to keep the core small and useful not only for AI agents, but for any Rust application that needs robust workflow execution.

Rust in AI still feels early, but projects like Rig, LanceDB, and others make it feel like the ecosystem is starting to take shape.

If you’re building AI systems in Rust, I’d love for you to check it out and share feedback.

GitHub: https://github.com/a-agmon/rs-graph-llm