Alternatives
Products that do what Flow Slime does
autonomous systems who needs a coordination layer that saves
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I think graph is a wrong abstraction for building AI agents. Just look at how incredibly hard it is to make routing using LangGraph - conditional edges are a mess. I built Laminar Flow to solve a common frustration with traditional workflow engines - the rigid need to predefine all node connections. Instead of static DAGs, Flow uses a dynamic task queue system that lets workflows evolve at runtime. Flow is built on 3 core principles: * Concurrent Execution - Tasks run in parallel automatically * Dynamic Scheduling - Tasks can schedule new tasks at runtime * Smart Dependencies - Tasks can…
2024 · github.com
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2021 · github.com
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We built an ant colony simulation as an internal hiring challenge at Moment and decided to open it up publicly. You write a program in a custom assembly-like (we call it ant-ssembly) instruction set that controls 200 ants. Each ant can sense nearby cells (food, pheromones, home, other ants) but has no global view. The only coordination mechanism is pheromone trails, which ants can emit and sense them, but that's it. Your program runs identically on every ant. The goal is to collect the highest percentage of food across a set of maps. Different map layouts (clustered food, scattered,…
Mar 2026 · dev.moment.com
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Node-based creative pipelines, now with real-time collab
May 2026 · elevenlabs.io
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Hi HN, we build an open-source operating system extension for orchestrating robot swarms fully decentralized. This first beta version allows you to create fully decentralized robot swarms. The system will set up a wireless mesh network and run a p2p networking stack on top of it, such that nodes can interact with each other through various abstractions using our SDKs (Rust, Python, TypeScript) or a CLI. We hope this is a step toward better inter-robot communication (and a fun project if you have a few Raspberry Pis lying around). Our mesh network is created by B.A.T.M.A.N.-adv and we’ve…
2025 · docs.p2p.industries
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Production-tested architecture for autonomous Claude agents
Apr 2026 · dvdshn.com
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Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
Feb 2026 · github.com
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Hey HN! Pretty excited to show Flows— something that we’ve been working on for past few weeks! What is Flows? Flows lets you create multi-step AI workflows in minutes by chaining modular blocks like: - LLM calls - API calls, - Code execution - RAG - Document Parsing …and 50+ more tools. Why You'll Love Flows - Test Flows on your datasets—with thousands of rows effortlessly! - Deploy workflows to production and scale your AI applications. - Collaborate with your team or share workflows publicly. Which workflow would you build with Flows? Share your ideas below!
2025 · app.athina.ai
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It all started with a conversation among friends about limitations in current multi-agent orchestration frameworks. We faced issues like limited control over agent memory and state, complicated persistence, scaling problems, and lack of type safety in Python-based tools. These challenges inspired us to try something different. The result was GraphFlow, a Rust-based lean framework for orchestrating multi-agent workflows that's simple, scalable, and robust. Its key features include: Graph-based orchestration: Easily define workflows using nodes and edges. Lean Execution Engine: A minimal and…
2025 · github.com
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