# 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


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://agentneo.raga.ai/features/key-features.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.