Alternatives

Products that do what Kodosumi 1.0.0 does

Run AI Agents at Scale, Reliable and Fast

1. 1
   Kodosumi▲167

   Runtime environment to execute agentic services at scale

   2025

2. 2
   Agentfield▲91

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3. 3
   AgentOS▲100

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4. 4
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5. 5
   Kodus▲145

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6. 6
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7. 7
   Okareo▲127

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8. 8
   Openbase▲216

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9. 9
   Agent Mode on Arena▲188

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10. 10
    Cosine Swarm▲127

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11. 11
    Kimi K2.5▲205

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12. 12
    KushoAI▲153

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13. 13
    Kodey.ai▲163

    Build real AI agents that work - not just chat.

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14. 14
    Koxy AI▲118

    Build your AI-powered serverless backend with no-code

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15. 15
    Kompas AI▲161

    Sophisticated AI agents at Work

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16. 16
    Sokosumi▲83

    Hire your first AI Agent teammates today

    Nov 2025

17. 17
    ROMA▲121

    The backbone for open-source meta-agents

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18. 18
    Montage▲129

    The runtime framework for agentic user interfaces!

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19. 19
    Shinkai: Local AI Agents▲30

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20. 20
    Kuberns▲40

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21. 21
    Statewave▲32

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    25d ago · statewave.ai

22. 22AM
    Agno – multi-agent framework, runtime and control plane▲9

    Hi HN, Excited to share Agno, a framework and runtime for multi-agent systems. Think of it as FastAPI for AI Agents. At its core is the AgentOS, a high-performance server/runtime that helps you run and manage AI agents, multi-agent teams, and step-based agentic workflows — all inside your own cloud, with full privacy and no external data sharing. What makes it different • Fast & lightweight — Agents instantiate in ~3μs and use ~6.6 KiB of memory on average (tested on M4 MacBook Pro). • Runtime architecture — Async, stateless, horizontally scalable runtime built on FastAPI. • Integrated…

    Oct 2025 · agno.link

23. 23AA
    Agyn, an open-source Kubernetes runtime for AI agents▲9

    May 2026 · github.com

24. 24RA
    Running AI agents across environments needs a proper solution▲8

    Hi HN folks, I have been building AI agents for quite some time now. The shift has gone from LLM + Tools → LLM Workflows → Agent + Tools + Memory, and now we are finally seeing true agency emerge: agents as systems composed of tools, command-line access, fine-grained system capabilities, and memory. This way of building agents is powerful, and I believe it is here to stay. But the real question is: are the systems powering these agents ready for that future? I do not think so. Using Docker for a single agent is not going to scale well, because agents need to be lightweight and fast. LLMs…

    Mar 2026 · github.com

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