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