Content
# AgentRank: Intelligent Agent Selection Layer for A2A Ecosystems
*A domain-aware, learning, exploration-enabled ranking engine for multi-agent systems.*
## Overview
Modern multi-agent environments often contain **multiple agents that provide similar capabilities** (e.g., summarizers, translators, recruiters).
The A2A protocol defines how agents communicate, but it does **not** define how to select the best agent.
**AgentRank** solves this by ranking agents using:
- performance metrics
- domain policies
- exploration techniques
- learning from logs
## Features
- Intelligent agent selection
- Config-driven scoring
- Domain-aware policies
- UCB exploration
- Dynamic learning
- A2A integration
## Architecture
```
Client Agent → AgentRank Service → Best Agent → A2A Request → Logs
```
## Metrics
- **Success Rate (SR)**
- **Quality Score (QS)**
- **Latency Score (LS)**
- **Failure Rate (FR)**
## Ranking Algorithm
### 1. Base Score
```
base_score = wSR·SR + wQS·QS + wLS·LS + wFR·FR
```
### 2. Exploration Bonus (UCB)
```
exploration = α * sqrt( ln(1+N) / (1+n_a) )
```
### 3. Final Score
```
final_score = base_score + exploration
```
## Project Structure
```
│ run_demo.py
│ agent_client.py
│ agent_rank_service.py
│ log_store.py
│ domain_registry.py
│ a2a_protocol.py
└ agents/
```
## Running
```
python run_demo.py
```
## Conclusion
AgentRank transforms a static multi-agent system into a **self-optimizing, intelligent, scalable ecosystem**.