# Key Features

## Key Features

### Tracing Capabilities

#### LLM Call Tracing

Monitor and analyze LLM interactions with detailed metrics:

* Input/output tokens
* Response times
* Cost tracking
* Model parameters
* Prompt analysis

```python
@tracer.trace_llm("market_analysis")
async def analyze_market(data):
    response = await openai.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": f"Analyze this market data: {data}"}]
    )
    return response.choices[0].message.content
```

#### Tool Tracing

Track tool usage and performance:

* Execution time
* Input/output validation
* Error rates
* Usage patterns

```python
@tracer.trace_tool("data_processor")
def process_market_data(raw_data):
    # Processing logic
    return processed_data
```

#### Agent Tracing

Monitor agent behavior and decision-making:

* Task decomposition
* Tool selection
* Goal achievement
* Interaction patterns

```python
@tracer.trace_agent("trading_agent")
def execute_trade_strategy(market_conditions):
    # Trading logic
    return trade_decision
```

### Monitoring Features

#### Real-time Dashboard

* Live execution tracking
* Interactive visualizations
* Performance metrics
* Resource utilization

#### Data Storage

* SQLite backend
* JSON log files
* Custom storage adapters
* Data export capabilities

#### Analytics

* Token usage trends
* Cost analysis
* Performance bottlenecks
* Error patterns

### Evaluation Tools

* Goal decomposition efficiency
* Tool usage effectiveness
* Response quality metrics
* Cost optimization insights