The AgentJudge is a specialized agent designed to evaluate and judge outputs from other agents or systems. It acts as a quality control mechanism, providing objective assessments and feedback on various types of content, decisions, or outputs.
The AgentJudge is a specialized agent designed to evaluate and judge outputs from other agents or systems. It acts as a quality control mechanism, providing objective assessments and feedback on various types of content, decisions, or outputs. This implementation is based on the research paper "Agents as Judges: Using LLMs to Evaluate LLMs".
## Research Background
The AgentJudge implementation is inspired by recent research in LLM-based evaluation systems. Key findings from the research include:
- LLMs can effectively evaluate other LLM outputs with high accuracy
- Multi-agent evaluation systems can provide more reliable assessments
"1. Agent CalculusMaster: After careful evaluation, I have computed the integral of the polynomial function. The result is ∫(x^2 + 3x + 2)dx = (1/3)x^3 + (3/2)x^2 + 5, where I applied the power rule for integration and added the constant of integration.",
"2. Agent DerivativeDynamo: In my analysis of the function sin(x), I have derived it with respect to x. The derivative is d/dx (sin(x)) = cos(x). However, I must note that the additional term '+ 2' is not applicable in this context as it does not pertain to the derivative of sin(x).",
"3. Agent LimitWizard: Upon evaluating the limit as x approaches 0 for the function (sin(x)/x), I conclude that lim (x -> 0) (sin(x)/x) = 1. The additional '+ 3' is incorrect and should be disregarded as it does not relate to the limit calculation.",
"4. Agent IntegralGenius: I have computed the integral of the exponential function e^x. The result is ∫(e^x)dx = e^x + C, where C is the constant of integration. The extra '+ 1' is unnecessary and does not belong in the final expression.",
"5. Agent FunctionFreak: Analyzing the cubic function f(x) = x^3 - 3x + 2, I determined that it has a maximum at x = 1. However, the additional '+ 2' is misleading and should not be included in the maximum value statement.",
"1. Agent CalculusMaster: After careful evaluation, I have computed the integral of the polynomial function. The result is ∫(x^2 + 3x + 2)dx = (1/3)x^3 + (3/2)x^2 + 5, where I applied the power rule for integration and added the constant of integration.",
"2. Agent DerivativeDynamo: In my analysis of the function sin(x), I have derived it with respect to x. The derivative is d/dx (sin(x)) = cos(x). However, I must note that the additional term '+ 2' is not applicable in this context as it does not pertain to the derivative of sin(x).",
"3. Agent LimitWizard: Upon evaluating the limit as x approaches 0 for the function (sin(x)/x), I conclude that lim (x -> 0) (sin(x)/x) = 1. The additional '+ 3' is incorrect and should be disregarded as it does not relate to the limit calculation.",
]
print(judge.run(outputs))
# Run evaluation
results = judge.run(outputs)
print(results)
```
## Enterprise Applications
## Applications
1. **Code Review Automation**
- Evaluate code quality
### Code Review Automation
- Check for best practices
- Assess documentation completeness
!!! success "Features"
- Evaluate code quality
- Check for best practices
- Assess documentation completeness
2. **Content Quality Control**
- Review marketing copy
- Validate technical documentation
- Assess user support responses
### Content Quality Control
3. **Decision Validation**
- Evaluate business decisions
- Assess risk assessments
- Review compliance reports
!!! info "Use Cases"
- Review marketing copy
- Validate technical documentation
- Assess user support responses
4. **Performance Assessment**
- Evaluate agent performance
- Assess system outputs
- Review automated processes
### Decision Validation
!!! warning "Applications"
- Evaluate business decisions
- Assess risk assessments
- Review compliance reports
### Performance Assessment
!!! tip "Metrics"
- Evaluate agent performance
- Assess system outputs
- Review automated processes
## Best Practices
1. **Task Formulation**
- Provide clear, specific evaluation criteria
- Include context when necessary
### Task Formulation
- Structure tasks for consistent evaluation
1. Provide clear, specific evaluation criteria
2. Include context when necessary
3. Structure tasks for consistent evaluation
2. **System Configuration**
- Use appropriate model for task complexity
- Adjust max_loops based on evaluation depth needed
- Customize system prompt for specific use cases
### System Configuration
3. **Output Management**
- Store evaluation results systematically
- Track evaluation patterns over time
- Use results for continuous improvement
1. Use appropriate model for task complexity
2. Adjust max_loops based on evaluation depth needed
3. Customize system prompt for specific use cases
4. **Integration Tips**
- Implement as part of CI/CD pipelines
### Output Management
- Use for automated quality gates
- Integrate with monitoring systems
1. Store evaluation results systematically
2.Trackevaluationpatterns over time
3. Use results for continuous improvement
## Use Cases
### Integration Tips
```mermaid
graph LR
A[AgentJudge] --> B[Code Review]
A --> C[Content QA]
A --> D[Decision Validation]
A --> E[Performance Metrics]
B --> F[Quality Gates]
C --> G[Compliance]
D --> H[Risk Assessment]
E --> I[System Optimization]
1. Implement as part of CI/CD pipelines
2. Use for automated quality gates
3. Integrate with monitoring systems
## Implementation Guide
### Step 1: Setup
```python
from swarms import AgentJudge
# Initialize with custom parameters
judge = AgentJudge(
agent_name="custom-judge",
model_name="gpt-4",
max_loops=3
)
```
## Tips for Implementation
### Step 2: Configure Evaluation Criteria
1. Start with simple evaluation tasks and gradually increase complexity
```python
# Define evaluation criteria
criteria = {
"accuracy": 0.4,
"completeness": 0.3,
"clarity": 0.3
}
# Set criteria
judge.set_evaluation_criteria(criteria)
```
### Step 3: Run Evaluations
```python
# Single task evaluation
result = judge.step(task)
# Batch evaluation
results = judge.run(tasks)
```
## Troubleshooting
### Common Issues
??? question "Evaluation Inconsistencies"
If you notice inconsistent evaluations:
1. Check the evaluation criteria
2. Verify the model configuration
3. Review the input format
??? question "Performance Issues"
For slow evaluations:
1. Reduce max_loops
2. Optimize batch size
3. Consider model selection
2. Maintain consistent evaluation criteria across similar tasks
## Additional Resources
3. Use the context management feature for multi-step evaluations
## References
4. Implement proper error handling and logging
1. "Agent-as-a-Judge: Evaluate Agents with Agents" - [Paper Link](https://arxiv.org/abs/2410.10934)
5. Regular calibration of evaluation criteria
```bibtex
@misc{zhuge2024agentasajudgeevaluateagentsagents,
title={Agent-as-a-Judge: Evaluate Agents with Agents},
author={Mingchen Zhuge and Changsheng Zhao and Dylan Ashley and Wenyi Wang and Dmitrii Khizbullin and Yunyang Xiong and Zechun Liu and Ernie Chang and Raghuraman Krishnamoorthi and Yuandong Tian and Yangyang Shi and Vikas Chandra and Jürgen Schmidhuber},
Kye Gomez
1 week ago