Agent Entropy Meter

Measure information entropy and redundancy in agent group communications. Use when user asks about agent communication efficiency, information redundancy, entropy metrics, or how to quantify knowledge overlap across agents.

Quantify information diversity and redundancy across agent group communications.

When to Use

• User asks "如何衡量Agent群体中的信息熵和冗余？"
• Need to analyze agent communication efficiency
• Detecting knowledge silos or redundancy bottlenecks
• Evaluating multi-agent system health

Core Metrics

1. Shannon Entropy (H)

Measures uncertainty/information content in agent messages:

text

H(X) = -Σ p(xᵢ) log₂ p(xᵢ)

Where p(xᵢ) is the probability of message type/category xᵢ.

2. Redundancy Ratio (R)

Measures how much repeated/overlapping information exists:

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R = 1 - H(X) / H_max

H_max = log₂(N) where N = number of distinct message categories.

3. Inter-Agent Mutual Information

Measures how much knowing one agent's output tells you about another:

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I(A;B) = H(A) + H(B) - H(A,B)

High I(A;B) = high redundancy (agents say the same things). Low I(A;B) = high diversity (agents contribute unique info).

4. Knowledge Overlap Coefficient

For two agents with topic sets T_A and T_B:

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KO(A,B) = |T_A ∩ T_B| / |T_A ∪ T_B|

Jaccard similarity of knowledge domains.

API

js

const meter = require('./skills/agent-entropy-meter');

// Compute Shannon entropy from message distribution
meter.shannonEntropy([0.5, 0.3, 0.2]); // => 1.485

// Compute redundancy ratio
meter.redundancyRatio([0.5, 0.3, 0.2]); // => 0.065

// Compute mutual information between two agents
meter.mutualInformation(agentAmsgs, agentBmsgs, allCategories);

// Compute knowledge overlap (Jaccard)
meter.knowledgeOverlap(setA, setB);

// Full report
meter.report(agentData);

Interpretation Guide

Metric	Low (Good)	High (Bad)	Meaning
Redundancy R	< 0.2	> 0.6	Low = diverse info; High = echo chamber
Mutual Info I	< 0.3	> 0.7	Low = independent; High = redundant
Knowledge Overlap	< 0.3	> 0.7	Low = complementary; High = duplication
Entropy H	> 0.7·H_max	< 0.3·H_max	High = diverse; Low = concentrated

Visualization

The report() output includes ASCII bar charts for quick assessment. For richer visualization, pipe output to mermaid-visualizer or excalidraw-diagram.