[{"data":1,"prerenderedAt":4},["ShallowReactive",2],{"readme:hive":3},"\u003Cp align=\"center\">\n  \u003Cimg width=\"100%\" alt=\"Hive Banner\" src=\"https:\u002F\u002Fasset.acho.io\u002Fgithub\u002Fimg\u002Fbanner.gif\" \u002F>\n\u003C\u002Fp>\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Faden-hive\u002Fhive\u002Fblob\u002FHEAD\u002FREADME.md\" rel=\"nofollow ugc noopener\">English\u003C\u002Fa> |\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Faden-hive\u002Fhive\u002Fblob\u002FHEAD\u002Fdocs\u002Fi18n\u002Fzh-CN.md\" rel=\"nofollow ugc noopener\">简体中文\u003C\u002Fa> |\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Faden-hive\u002Fhive\u002Fblob\u002FHEAD\u002Fdocs\u002Fi18n\u002Fes.md\" rel=\"nofollow ugc noopener\">Español\u003C\u002Fa> |\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Faden-hive\u002Fhive\u002Fblob\u002FHEAD\u002Fdocs\u002Fi18n\u002Fhi.md\" rel=\"nofollow ugc noopener\">हिन्दी\u003C\u002Fa> |\n  \u003Ca 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\u002F>\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fwww.ycombinator.com\u002Fcompanies\u002Faden\" rel=\"nofollow ugc noopener\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FY%20Combinator-Aden-orange\" alt=\"Y Combinator\" \u002F>\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fdiscord.com\u002Finvite\u002FMXE49hrKDk\" rel=\"nofollow ugc noopener\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F1172610340073242735?logo=discord&labelColor=%235462eb&logoColor=%23f5f5f5&color=%235462eb\" alt=\"Discord\" \u002F>\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fx.com\u002Faden_hq\" rel=\"nofollow ugc noopener\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002Fteamaden?logo=X&color=%23f5f5f5\" alt=\"Twitter Follow\" \u002F>\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fteamaden\u002F\" rel=\"nofollow ugc noopener\">\u003Cimg src=\"https:\u002F\u002Fcustom-icon-badges.demolab.com\u002Fbadge\u002FLinkedIn-0A66C2?logo=linkedin-white&logoColor=fff\" alt=\"LinkedIn\" \u002F>\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMCP-102_Tools-00ADD8?style=flat-square\" alt=\"MCP\" \u002F>\n\u003C\u002Fp>\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAgent_Harness-Runtime_Layer-ff6600?style=flat-square\" alt=\"Agent Harness\" \u002F>\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAI_Agents-Self--Improving-brightgreen?style=flat-square\" alt=\"AI Agents\" \u002F>\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMulti--Agent-Systems-blue?style=flat-square\" alt=\"Multi-Agent\" \u002F>\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FHeadless-Development-purple?style=flat-square\" alt=\"Headless\" \u002F>\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FHuman--in--the--Loop-orange?style=flat-square\" alt=\"HITL\" \u002F>\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FBrowser-Use-red?style=flat-square\" alt=\"Browser Use\" \u002F>\n\u003C\u002Fp>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FOpenAI-supported-412991?style=flat-square&logo=openai\" alt=\"OpenAI\" \u002F>\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAnthropic-supported-d4a574?style=flat-square\" alt=\"Anthropic\" \u002F>\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGoogle_Gemini-supported-4285F4?style=flat-square&logo=google\" alt=\"Gemini\" \u002F>\n\u003C\u002Fp>\u003Cp align=\"center\">\u003Cem>The agent harness for production workloads — state management, failure recovery, observability, and human oversight so your agents actually run.\u003C\u002Fem>\u003C\u002Fp>\u003Ch2>Overview\u003C\u002Fh2>\n\u003Cp>OpenHive is a zero-setup, model-agnostic execution harness that dynamically generates multi-agent topologies to tackle complex, long-running business workflows without requiring any orchestration boilerplate. By simply defining your objective, the runtime compiles a strict, graph-based execution DAG that safely coordinates specialized agents to execute concurrent tasks in parallel. Backed by persistent, role-based memory that intelligently evolves with your project's context, OpenHive ensures deterministic fault tolerance, deep state observability, and seamless asynchronous execution across whichever underlying LLMs you choose to plug in.\u003C\u002Fp>\n\u003Ch2>Features\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>✅ Multi-Agent Coordination for parallel task execution \u003C\u002Fli>\n\u003Cli>✅ Graph-based execution for recurring and complex processes \u003C\u002Fli>\n\u003Cli>✅ Role-based memory that evolves with your projects \u003C\u002Fli>\n\u003Cli>✅ Zero Setup - No technical configuration required\u003C\u002Fli>\n\u003Cli>✅ General Compute Use and Browser Use with Native Extension \u003C\u002Fli>\n\u003Cli>✅ Custom Model Support\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Visit \u003Ca href=\"https:\u002F\u002Fadenhq.com\" rel=\"nofollow ugc noopener\">adenhq.com\u003C\u002Fa> for complete documentation, examples, and guides.\u003C\u002Fp>\n\u003Cp>Visit \u003Ca href=\"http:\u002F\u002Fhoneycomb.open-hive.com\u002F\" rel=\"nofollow ugc noopener\">HoneyComb\u003C\u002Fa> to see what jobs are being automated by AI. It’s a stock market for jobs, driven by our community’s AI agent progress. You can long and short jobs (with no real money but compute token)based on how much you think a job is going to be\u003C\u002Fp>\n",1785276849646]