# Quick Start Guide

### Basic Setup

```python
from agentneo import AgentNeo, Tracer, Evaluation, launch_dashboard

# Create session and project
neo_session = AgentNeo(session_name="my_session")
neo_session.create_project(project_name="my_project")

# Initialize tracer
tracer = Tracer(session=neo_session)
tracer.start()
```

### Adding Traces

#### Trace LLM Calls

```python
@tracer.trace_llm("my_llm_call")
async def get_llm_response(prompt):
    # Your LLM call here
    pass
```

#### Trace Tools

```python
@tracer.trace_tool("my_tool")
def calculate_metrics(data):
    # Your tool logic here
    pass
```

#### Trace Agents

```python
@tracer.trace_agent("my_agent")
def process_task(input_data):
    # Your agent logic here
    pass
```

### Viewing Results

```python
# Stop tracing
tracer.stop()

# Launch dashboard
launch_dashboard(port=3000)
```

Visit `http://localhost:3000` to view your traces and metrics.

### Complete Example

```python
from agentneo import AgentNeo, Tracer, Evaluation, launch_dashboard

# Setup
neo_session = AgentNeo(session_name="example_session")
neo_session.create_project(project_name="example_project")
tracer = Tracer(session=neo_session)
tracer.start()

# Define your functions
@tracer.trace_tool("data_processor")
def process_data(data):
    return {"processed": data}

@tracer.trace_agent("main_agent")
def main_agent(input_data):
    processed = process_data(input_data)
    return processed

# Run your application
result = main_agent({"data": "example"})

# Stop and view
tracer.stop()
launch_dashboard(port=3000)
```


---

# 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/getting-started/quick-start-guide.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.