> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Optimizer
> Automatically improve agent performance
Agents can be optimized automatically - tune prompts and settings for better results.
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
subgraph "Optimization Loop"
A[🤖 Agent] --> B[📊 Evaluate]
B --> C[✏️ Adjust]
C --> D[🎯 Improved]
end
classDef agent fill:#8B0000,color:#fff
classDef tool fill:#189AB4,color:#fff
class A agent
class B,C,D tool
```
## Quick Start
```typescript theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
import { Agent, Optimizer } from 'praisonai';
const agent = new Agent({
instructions: 'You answer questions'
});
const optimizer = new Optimizer({
agent,
examples: [
{ input: 'What is 2+2?', expected: '4' },
{ input: 'Capital of France?', expected: 'Paris' }
]
});
await optimizer.run();
// Agent instructions are now improved
```
```typescript theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
const optimizer = new Optimizer({
agent,
examples: myExamples,
evaluate: (output, expected) => {
return output.toLowerCase() === expected.toLowerCase();
}
});
```
***
## User Interaction Flow
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
participant User
participant Optimizer
participant Agent
User->>Optimizer: Start optimization
loop For each example
Optimizer->>Agent: Test input
Agent-->>Optimizer: Output
Optimizer->>Optimizer: Score result
end
Optimizer->>Agent: Update instructions
Optimizer-->>User: Optimization report
```
***
## Configuration Levels
```typescript theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
// Level 1: Array - Simple examples
const optimizer = new Optimizer({
agent,
examples: [
{ input: 'test', expected: 'result' }
]
});
// Level 2: Dict - With options
const optimizer = new Optimizer({
agent,
examples: myExamples,
iterations: 5,
targetScore: 0.9
});
// Level 3: Instance - Full control
const optimizer = new Optimizer({
agent,
examples: myExamples,
iterations: 10,
evaluate: customEvaluator,
adjustments: ['instructions', 'temperature'],
onIteration: (scores) => console.log(scores)
});
```
***
## How It Works
1. **Test**: Run agent on examples
2. **Score**: Evaluate output quality
3. **Adjust**: Modify prompts/settings
4. **Repeat**: Continue until target score
***
## API Reference
Evaluation and optimization
***
## Best Practices
Cover different types of inputs the agent will see.
Stop optimization when good enough.
Check what the optimizer changed before deploying.
***
## Related
Success conditions
Self-improvement