HN 표시: Orloj – 코드형 에이전트 인프라(YAML 및 GitOps)

Named after the Prague Orloj, an astronomical clock that has coordinated complex mechanisms for over 600 years. An orchestration runtime for multi-agent AI systems. Declare your agents, tools, and policies as YAML. Orloj schedules, executes, routes, and governs them so you can run multi-agent systems in production with the same operational rigor you expect from infrastructure. Status: Orloj is under active development. APIs and resource schemas may change between minor versions before 1.0. Running AI agents in production today looks a lot like running containers before container orchestration: ad-hoc scripts, no governance, no observability, and no standard way to manage an agent fleet. Orloj provides: - Agents-as-Code -- declare agents, their models, tools, and constraints in version-controlled YAML manifests. - DAG-based orchestration -- pipeline, hierarchical, and swarm-loop topologies with fan-out/fan-in support. - Model routing -- bind agents to OpenAI, Anthropic, Azure OpenAI, Ollama, and other endpoints. Switch providers without changing agent definitions. - Tool isolation -- execute tools in containers, WASM sandboxes, or process isolation with configurable timeout and retry. - Governance built in -- policies, roles, and tool permissions enforced at the execution layer. Unauthorized tool calls fail closed. - Production reliability -- lease-based task ownership, idempotent replay, capped exponential retry with jitter, and dead-letter handling. - Web console -- built-in UI with topology views, task inspection, and live event streaming. Get started in 5 minutes — scaffold with orlojctl init , add your API key, apply manifests, and run a pipeline with orlojctl run . Install orlojctl (CLI) via Homebrew: brew tap OrlojHQ/orloj brew install orlojctl Formula versions follow Orloj releases. Or install all binaries (orlojd, orlojworker, orlojctl) with the install script: curl -sSfL https://raw.githubusercontent.com/OrlojHQ/orloj/main/scripts/install.sh | sh You can also download binaries manually from GitHub Releases. Then run: # Start the server with an embedded worker ./orlojd --storage-backend=memory --task-execution-mode=sequential --embedded-worker Open http://127.0.0.1:8080/ to explore the web console, then apply a starter blueprint. The example manifests live in this repo -- clone it or browse them on GitHub: # Apply a starter blueprint (pipeline: planner -> research -> writer) ./orlojctl apply -f examples/blueprints/pipeline/ --run # Check the result ./orlojctl get task bp-pipeline-task Or build from source (requires Go 1.25+): go build -o orlojd ./cmd/orlojd go build -o orlojctl ./cmd/orlojctl When you are ready to scale, switch to message-driven mode with distributed workers and Postgres persistence. See the Quickstart guide for details. Full walkthrough: 5-minute tutorial. ┌─────────────────────────────────────────────────────┐ │ Server (orlojd) │ │ │ │ ┌──────────────┐ ┌────────────────┐ │ │ │ API Server │──►│ Resource Store │ │ │ │ (REST) │ │ mem / postgres │ │ │ └──────┬───────┘ └────────────────┘ │ │ │ │ │ ▼ │ │ ┌──────────────┐ ┌────────────────┐ │ │ │ Services │──►│ Task Scheduler │ │ │ └──────────────┘ └───────┬────────┘ │ └─────────────────────────────┼───────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────┐ │ Workers (orlojworker) │ │ │ │ ┌──────────────┐ ┌───────────────┐ │ │ │ Task Worker │──►│ Model Gateway │ │ │ │ │ └───────────────┘ │ │ │ │──►┌───────────────┐ │ │ │ │ │ Tool Runtime │ │ │ │ │ └───────────────┘ │ │ │ ◄──────┼───┌───────────────┐ │ │ │ │──►│ Message Bus │ │ │ └──────────────┘ └───────────────┘ │ └─────────────────────────────────────────────────────┘ Server (orlojd ) -- API server, resource store (in-memory or Postgres), background services, and task scheduler. Workers (orlojworker ) -- claim tasks, execute agent graphs, route model requests, run tools, and handle inter-agent messaging. Governance -- AgentPolicy, AgentRole, and ToolPermission resources enforced inline during every tool call and model interaction. Persistence is backed by Postgres (or in-memory for local dev). Message-driven mode uses NATS JetStream for durable agent-to-agent messaging. The v1 REST API is described in openapi/openapi.yaml (OpenAPI 3.1, split schemas under openapi/schemas/). CI runs npx @redocly/cli lint openapi/openapi.yaml . To regenerate the root document from openapi/build_openapi.py, run python3 openapi/build_openapi.py from the repo root. Orloj manages 15 resource types, all defined as declarative YAML with apiVersion , kind , metadata , spec , and status fields: Core | Resource | Purpose | |---|---| | Agent | Unit of work backed by a language model | | AgentSystem | Directed graph composing multiple agents | | ModelEndpoint | Connection to a model provider | | Tool | External capability with isolation and retry | | Secret | Credential storage | | Memory | Vector-backed retrieval for agents | | McpServer | MCP server connection that discovers/syncs MCP to