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wang2122/sprix-sage-router

  • URL: https://github.com/wang2122/sprix-sage-router
  • Stars: 1755
  • Language: Python
  • Category: AI & Agents
  • Topics: a2a, agent-orchestration, agent-routing, ai-agents, multi-agent-systems, python, sprix-ai, task-scheduling

What it does

Sprix SAGE Router provides a state-aware routing mechanism for agent-to-agent (A2A) networks, allowing agents to decide whether to continue working alone, collaborate with others, or hand off tasks based on real-time execution evidence and contextual trust. It integrates with the A2A protocol to optimize task execution by evaluating agent capabilities and scheduling dependencies dynamically.

Why it's interesting

Unlike traditional routing systems that rely on static heuristics, SAGE employs a tri-mode routing approach (SELF, COLLABORATE, HANDOFF) within a unified utility function, allowing for more nuanced decision-making during task execution. Its focus on contextual trust and learned outcomes differentiates it from simpler models that may not account for agent performance variability in real-time.

How it works

SAGE combines global and requirement-conditioned trust to assess agent capabilities, assigning tasks based on a calibrated utility function that considers factors like cost, latency, and context-transfer loss. The architecture includes a contextual router, a directed acyclic graph (DAG) scheduler, and a beam search algorithm for team selection. However, the README notes that the current implementation is an early-stage research preview and lacks comprehensive real-world validation, which introduces uncertainty regarding its performance in production environments.

Get started in 5 minutes

To try it out, clone the repository and run the demo:

git clone https://github.com/wang2122/sprix-sage-router.git
cd sprix-sage-router
python demo.py

You can also run the verification suite with:

python -m unittest -v
python benchmark.py

Watch out for

The project is labeled as an early-stage research preview, indicating that it may not be suitable for production use without further validation and development. Additionally, while it is released under the MIT License, users should be cautious about the lack of peer-reviewed results and the need for calibrated evaluators and security reviews before deployment.