Openflows

Signal

Sage Multi-Agent Framework · GitHub (ZHangZHengEric/Sage) · 2026-03-20

Multi-Agent System Framework For Complex Tasks | agents, ai, llm, manus, muilt-agents, workflow

Context

The complexity of autonomous agent workflows requires robust orchestration layers beyond simple chaining. Sage addresses the need for structured execution modes (sequential, parallel, declarative) to manage task dependencies and resource allocation effectively. The framework emphasizes stability on smaller parameter models, suggesting an optimization strategy for cost-effective local or edge inference.

Relevance

This entry documents a specific orchestration capability within the open-source agent ecosystem. It is treated as infrastructure rather than a product, focusing on the structural patterns of agent communication and execution. The framework's modular design allows integration with existing skill libraries and model providers without enforcing vendor lock-in.

Current State

Version 1.0.0, MIT license, Python 3.11+ requirement. Key components include TaskExecutor (Sequential), FibreAgent (Parallel), and AgentFlow (Declarative). Optimizations target Qwen3.5 35B-A3B and similar architectures. Visual workbench and chat interfaces are provided for debugging and monitoring. Documentation hosted at wiki.sage.zavixai.com.

Open Questions

• Validation of stability claims on smaller models compared to baseline implementations.
• Long-term maintenance cadence and community adoption metrics.
• Specific implementation details of the "High-Stability Skills" module.
• Compatibility with existing MCP (Model Context Protocol) servers.

Connections

The framework aligns with existing agent orchestration infrastructure, offering an alternative to established players. It shares functional overlap with CrewAI regarding role-based coordination and OpenClaw regarding agent framework standards. The focus on Qwen3.5 optimization creates a direct technical bridge to the Qwen-Agent ecosystem.