# Glossary

Core Concepts

#### A

* **Agent**: An autonomous component that performs specific tasks
* **AgentNeo**: The main framework for AI application observability

#### D

* **Dashboard**: Web interface for visualizing trace data and metrics
* **Decorator**: Python syntax for adding tracing functionality

#### E

* **Evaluation**: Framework for assessing agent performance
* **Event**: A tracked occurrence in the system

#### L

* **LLM**: Large Language Model
* **Log**: Record of system events and traces

#### M

* **Metric**: Measurement of system or agent performance
* **Monitoring**: Real-time observation of system behavior

#### P

* **Project**: Logical grouping of related traces
* **Provider**: LLM service provider (e.g., OpenAI)

#### S

* **Session**: Container for related traces and projects
* **Storage**: System for persisting trace data

#### T

* **Trace**: Record of execution flow and performance metrics
* **Tool**: Function or service that agents can use

### Technical Terms

#### Performance Metrics

* **Latency**: Time taken for operation completion
* **Throughput**: Rate of operation processing
* **Token Usage**: Number of tokens consumed in LLM calls

#### System Components

* **Buffer**: Temporary storage for trace data
* **Flush**: Process of writing buffered data to storage
* **Hook**: Extension point for custom functionality