# Basic Usage

### Setting Up AgentNeo

```python
from agentneo import AgentNeo, Tracer

# Initialize
neo_session = AgentNeo(session_name="my_session")
neo_session.create_project(project_name="my_project")

# Start tracing
tracer = Tracer(session=neo_session)
tracer.start()
```

### Using Decorators

#### LLM Tracing

```python
@tracer.trace_llm("text_generation")
async def generate_text(prompt):
    response = await llm.generate(prompt)
    return response

# Usage
text = await generate_text("Tell me a story")
```

#### Tool Tracing

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

# Usage
result = process_data(raw_data)
```

#### Agent Tracing

```python
@tracer.trace_agent("task_agent")
def execute_task(input_data):
    # Agent logic
    return result

# Usage
output = execute_task(data)
```

### Dashboard Usage

#### Launching

```python
from agentneo import launch_dashboard

# After your traces are complete
tracer.stop()
launch_dashboard(port=3000)
```

#### Viewing Data

1. Open `http://localhost:3000`
2. Navigate through different views:
   * Execution flow
   * Metrics
   * Timeline
   * Analytics


---

# 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/basic-usage.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.