- name
- agent-entropy-meter
- description
- 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.
Agent Entropy Meter
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:
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:
R = 1 - H(X) / H_maxH_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:
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:
KO(A,B) = |T_A ∩ T_B| / |T_A ∪ T_B|Jaccard similarity of knowledge domains.
API
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.