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Alternatives

Products that do what Savine AI does

Deploy AI agents like APIs. Runtime infrastructure handled.

  1. 1
    Runtime303

    Sandboxed coding agents for everyone on your team

    May 2026 · runtm.com

  2. 2SD
  3. 3
    AgentSky430

    Any harness, any LLM — cloud-hosted agents on demand.

    Aug 2026 · agentsky.dev

  4. 4
    Rerun308

    The easiest way to build AI agents for all your tasks

    Jul 2026 · rerun.build

  5. 5
    Openbase216

    Manage your team of AI agents by voice, from anywhere

    Jul 2026 · openbase.cloud

  6. 6

    Deploy your AI Agents in 60 seconds

    Mar 2026

  7. 7

    Bring the world's best AI agents into your app, with one API

    Jul 2026 · harnessrouter.ai

  8. 8

    Build & scale AI \ agents as microservices with IAM

    Dec 2025

  9. 9

    Deploy AI agents to use your production app like a real user

    Jan 2026

  10. 10

    Build, customize, deploy – AI Agents your way with OAK

    2025

  11. 11RM

    RunAgent eliminates the complexity of AI agent deployment across different frameworks and languages. Today's developers face deployment nightmares with fragmented frameworks (LlamaIndex, LangChain, LangGraph, CrewAI, Letta, Agno, etc.) each requiring different deployment processes, creating unnecessary friction. The Solution: Like MCP (Model Context Protocol), RunAgent provides a standardized approach to agent deployment. Developers simply provide a config file and their agent code - RunAgent handles the rest with REST API and WebSocket (Streaming and non streaming). Our open-source platform…

    2025 · github.com

  12. 12

    Pump up your SaaS with embedded AI agents

    2025

  13. 13

    Open-source runtime for durable AI agents

    May 2026 · orkes.io

  14. 14

    Skip the setup and run OpenClaw & Hermes, fully managed

    17d ago · cloudways.com

  15. 15RA

    Hi, founder of Okteto here! We’ve been experimenting with AI agents in our workflows at Okteto. Running them locally worked at first, but quickly became painful. git worktrees, multiple terminals, and messy context switches slowed us down. So we built Agent Fleets: ephemeral, fully managed environments for AI agents, built on top of Okteto’s development platform. Each agent runs in its own containerized environment on your infrastructure, with the services, tools, and policies it needs. You can spin up agents with a single click or API call. No local setup. No git worktrees. The beta…

    2025 · okteto.com

  16. 16
    Clawezy77

    Deploy autonomous OpenClaw AI agent servers in seconds

    Feb 2026

  17. 17

    Build AI Agents. Run Them Anywhere.

    Feb 2026

  18. 182C

    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

  19. 19OS

    We build runtime security for AI agents. The playground started as an internal tool that we used to test our own guardrails. But we kept finding the same types of vulnerabilities because we think about attacks a certain way. At some point you need people who don't think like you. So we open-sourced it. Each challenge is a live agent with real tools and a published system prompt. Whenever a challenge is over, the full winning conversation transcript and guardrail logs get documented publicly. Building the general-purpose agent itself was probably the most fun part. Getting it to reliably use…

    Mar 2026 · github.com

  20. 20

    Deploy AI agents with one API call

    Mar 2026 · gopilot.dev

  21. 21RA

    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

  22. 22

    Open-source AI agent runtime — build Agents in plain English

    Jul 2026 · syntheticbrew.ai

  23. 23CD

    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,…

    Feb 2026 · crewship.dev

  24. 24

    Deploy AI agents that run your business workflows easily

    Jun 2026

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