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Build, orchestrate, and run AI agent teams — no code needed

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  16. 162C

    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! We built Crewship (https://crewship.dev) because deploying AI agents to production is still unnecessarily painful. If you've built something with CrewAI, LangGraph, or similar frameworks, you know the drill: it works great locally, then you spend days figuring out infrastructure, scaling, monitoring, and artifact management just to get it running for real users. Crewship handles all of that. You add a crewship.toml to your project, run `crewship deploy`, and your agents are live in seconds. It's framework-agnostic — we currently support CrewAI, LangGraph, and LangGraph.js,…

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