Orloj – agent infrastructure as code (YAML and GitOps)
Hey HN, we're Jon and Kristiane, and we're building Orloj (https://orloj.dev), an open-source orchestration runtime for multi-agent AI systems. You define agents, tools, policies, and workflows in declarative YAML manifests, and Orloj handles scheduling, execution, governance, and reliability. Over the past year we tried to use many different platforms/frameworks to build out agent systems and while building we hit some sort of problem with all of them, so we decided to have a go at it. Jon has worked with kubernettes and terraform for years and always liked the declarative…
In plain words
Orloj is an open-source orchestration runtime for multi-agent AI systems that uses declarative YAML manifests to define agents, tools, policies, and workflows. It handles scheduling, execution, governance, and reliability for AI agent systems. Designed for developers building complex multi-agent applications, Orloj applies infrastructure-as-code principles from Kubernetes and Terraform to agent management, allowing users to compose agents into directed graphs and set execution limits and permissions through configuration files.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN, we're Jon and Kristiane, and we're building Orloj (https://orloj.dev), an open-source orchestration runtime for multi-agent AI systems. You define agents, tools, policies, and workflows in declarative YAML manifests, and Orloj handles scheduling, execution, governance, and reliability. Over the past year we tried to use many different platforms/frameworks to build out agent systems and while building we hit some sort of problem with all of them, so we decided to have a go at it. Jon has worked with kubernettes and terraform for years and always liked the declarative nature so took patterns and concepts from both to build out Orloj. Orloj treats agents the way infrastructure-as-code treats cloud resources. You write a manifest that declares an agent's model, tools, permissions, and execution limits. You compose agents into directed graphs (pipelines, hierarchies, or swarm loops). Governance has been overlooked so we made resource policies (AgentPolicy, AgentRole, and ToolPermission) that are evaluated inline during execution, before every agent turn and tool call. Instead of prompt instructions that the model might ignore, these policies are a runtime gate. Unauthorized actions fail closed with structured errors and full audit trails. You can set token budgets per run, whitelist models, block specific tools, and scope policies to individual agent systems. For reliability, we built lease-based task ownership (so crashed workers don't leave orphan tasks), which allows you to run workers on different machines with whatever compute that’s needed. It helps when we need a GPU for certain tasks (like we did). The scheduler also supports cron triggers and webhook-driven task creation. The architecture is a server/worker split like kubernettes. orlojd hosts the API, resource store (in-memory for dev, Postgres for production), and task scheduler. orlojworker instances claim and execute tasks, route model requests through a gateway (OpenAI, Anthropic, Ollama, etc.), and run tools in configurable isolation (direct, sandboxed, container, or WASM). We work with a lot of MCP servers so wanted to make MCP integration as easy as possible. You register an MCP server (stdio or HTTP), Orloj auto-discovers its tools, and they become first-class resources with governance applied. So you can connect something like the GitHub MCP server and still have policy enforcement over what agents are allowed to do with it. It comes shipped with a built in UI to manage all your workflows and topology to see everything working in real time. There are a few examples and starter templates in the repo to start playing around with to get a feel for what’s possible. More info in the docs: https://docs.orloj.dev We're a small team and this is v0.1.0, so there's a lot still on the roadmap, but the full runtime is open source today and we'd love feedback on what we've built so far. What would you use this for? What's missing?
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