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Basic Information

Rill Flow is an open-source distributed workflow orchestration service designed to run and manage large volumes of tasks across heterogeneous systems. It targets teams that need a cloud-native orchestration layer able to schedule and execute workflows with low latency and high throughput. The README emphasizes operational readiness with Docker and Docker Compose for local deployment, a web-based management UI and an example sample-executor service. The project supports visual flow definition and YAML-based flow import, execution tracking, and integration points for function and container-based workloads. It also highlights built-in support for integrating LLM model services for AIGC use cases. The project is licensed under Apache 2.0 and includes documentation and contributor listings for maintainers and community members.

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App Details

Features
Rill Flow lists several core capabilities: high performance execution capable of tens of millions of tasks per day with task latency under 100ms, distributed orchestration across heterogeneous systems, visual process orchestration with plug-in access and one-click YAML import, cloud-native support for container deployment and function orchestration, and rapid integration of LLM model services for AIGC scenarios. The repo provides a Docker Compose deployment with components such as MySQL, Redis, Jaeger, a UI service and a sample executor to demonstrate task patterns like synchronous tasks, input mappings and tolerance settings. The README also provides a QuickStart, execution examples and links to multilingual documentation.
Use Cases
Rill Flow helps teams deploy, design and operate automated workflows at scale by providing a ready-to-run orchestration platform and tooling. Users can locally deploy the full stack using Docker Compose to evaluate or develop flows, use the web UI to create and visualize flow graphs, import YAML definitions, and run executions to observe status and details. The built-in sample executor and example YAML accelerate getting started and verifying integrations. Cloud-native features enable running tasks as containers or functions and integrating external services including LLMs for AIGC pipelines. Observability components and execution records aid troubleshooting and operational monitoring. The project’s documentation and Apache 2.0 license support adoption and contribution.

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