A high-performance, scalable distributed workflow orchestration engine designed for large-scale tasks and LLM integration, capable of processing tens of millions of tasks daily with sub-100ms latency.
One-Minute Overview#
Rill Flow is a high-performance, scalable distributed workflow orchestration service designed for large-scale distributed workloads and LLM integration. It supports processing tens of millions of tasks daily with sub-100ms latency while providing visual process orchestration and plugin access capabilities.
Core Value: Enables efficient coordination and execution of complex distributed systems through a unified orchestration platform.
Quick Start#
Installation Difficulty: Medium - Requires Docker and Docker Compose environment, but provides a one-click deployment script
# Clone the project source code
git clone https://github.com/weibocom/rill-flow.git
# Start the service
cd rill-flow/docker
docker-compose up -d
Is this suitable for my scenario?
- ✅ Large-scale task scheduling: Enterprise applications requiring processing of millions of tasks
- ✅ LLM integration scenarios: Enterprises needing rapid integration of multiple LLM model services
- ❌ Single-node applications: Overly complex for simple tasks without distributed coordination
- ❌ Resource-constrained environments: Docker deployment requires significant system resources
Core Capabilities#
1. High-Performance Execution Engine - Handling Large-Scale Tasks#
- Supports executing tens of millions of tasks daily with task execution latency under 100ms Real Value: Ensures system responsiveness and stability under high-load scenarios
2. Distributed Coordination Capability - Managing Heterogeneous Systems#
- Supports orchestration and scheduling of heterogeneous distributed systems Real Value: Unified management of system components across different technology stacks, simplifying distributed system architecture
3. Visual Orchestration Interface - Simplifying Process Design#
- Provides a graphical process editor with drag-and-drop workflow design Real Value: Lowers workflow design barriers, improving intuitiveness and efficiency of business logic implementation
4. Cloud-Native Deployment - Adapting to Modern Infrastructure#
- Supports cloud-native container deployment and cloud-native function orchestration Real Value: Easy integration into modern cloud-native environments like Kubernetes for elastic scaling
5. AIGC Integration - Rapid LLM Service Access#
- Supports rapid integration of multiple LLM model services Real Value: Simplifies AI-native application development processes, accelerating AI capability implementation
Technology Stack & Integration#
Development Languages: Java, Python, Go, Shell Main Dependencies: MySQL (data storage), Redis (caching), Jaeger (tracing), Tomcat (application server), FastAPI (sample executor) Integration Method: API / Web interface / SDK
Maintenance Status#
- Development Activity: Actively developed with multiple maintainers
- Recent Updates: Continuously updated recently
- Community Response: Maintained by the Weibo team with a stable contributor community
Commercial & Licensing#
License: Apache-2.0
- ✅ Commercial Use: Allowed
- ✅ Modification: Permitted
- ⚠️ Restrictions: Must include license and copyright notices
Documentation & Learning Resources#
- Documentation Quality: Comprehensive with Chinese documentation and quick start guides
- Official Documentation: Available in the GitHub repository
- Example Code: Provides complete workflow examples and quick start tutorials