A comprehensive reference for terminology used in AI agent frameworks and basic-open-agent-tools documentation.
Agent : An AI system that can perceive its environment, make decisions, and take actions to achieve specific goals. In the context of this toolkit, agents use functions as tools to perform tasks.
Agent Development Kit (ADK) : A framework or set of tools for building AI agents. Google ADK is a specific implementation that this toolkit is optimized for.
Agent Framework : A software framework that provides the infrastructure for building, deploying, and managing AI agents. Examples include Google ADK, LangChain, and Strands Agents.
Agent-Friendly Signatures : Function signatures designed to work smoothly with AI agent frameworks by using simple types, avoiding defaults, and providing clear parameter specifications.
API Reference : Documentation that provides detailed information about all available functions, their parameters, return values, and usage examples.
BasicAgentToolsError : The standard exception class used throughout this toolkit for consistent error handling across all modules.
Boilerplate Code : Repetitive code that must be written for basic functionality. This toolkit aims to eliminate boilerplate by providing ready-to-use functions.
Chain : In LangChain, a sequence of calls to language models and other tools. Chains can include basic-open-agent-tools functions as part of their execution flow.
CLI (Command Line Interface) : A text-based interface for interacting with software. Some agent frameworks provide CLI tools for development and deployment.
Compatibility Layer : Code that allows different systems to work together. This toolkit includes compatibility layers for various agent frameworks.
Context : Information provided to an AI agent about its current situation, task, or environment. Functions in this toolkit often return context information in their response dictionaries.
Decorator
: A Python feature that modifies the behavior of functions or classes. This toolkit uses decorators like @strands_tool to make functions compatible with specific frameworks.
Dependency Group
: Optional sets of dependencies that can be installed together. Examples include [system], [pdf], and [all] groups in this toolkit.
Dict (Dictionary) : A Python data structure that stores key-value pairs. Most functions in this toolkit return dictionaries with structured information.
Direct Function Usage : Using toolkit functions directly in Python code without an agent framework, as opposed to using them as agent tools.
Error Handling : The process of catching, managing, and responding to errors in software. This toolkit uses consistent error handling patterns across all functions.
Exception
: A Python mechanism for handling errors. All toolkit functions use BasicAgentToolsError for consistent exception handling.
Framework Agnostic : Designed to work with multiple different frameworks rather than being tied to a specific one. This toolkit is framework agnostic.
Function Calling : The ability of AI models to call external functions or tools. Modern language models can analyze function signatures and call appropriate functions based on user requests.
Function Signature : The definition of a function including its name, parameters, parameter types, and return type. Agent-friendly signatures are crucial for LLM integration.
Function Tool : A function that can be used by an AI agent as a tool to perform specific tasks. All functions in this toolkit are designed as function tools.
Google ADK (Agent Development Kit) : Google's framework for building AI agents. This toolkit is specifically optimized for Google ADK compatibility.
Graceful Degradation : The ability of software to continue functioning when some components fail or are unavailable. This toolkit implements graceful degradation for optional dependencies.
Helper Functions
: Utility functions that simplify common tasks. This toolkit provides helper functions like load_all_tools() and merge_tool_lists() for easy integration.
HTTP Client : Software that makes requests to web servers. The network module includes an HTTP client function for making web requests.
Integration : The process of combining different software components to work together. This toolkit focuses on integration with AI agent frameworks.
Introspection : The ability of a program to examine its own structure and behavior. The utilities module includes introspection functions for debugging.
JSON (JavaScript Object Notation) : A lightweight data interchange format. Many functions in this toolkit work with JSON data and return JSON-serializable results.
JSON-Serializable : Data that can be converted to JSON format. All function parameters and return values in this toolkit are JSON-serializable for agent compatibility.
LangChain
: A popular framework for developing applications with large language models. This toolkit provides compatibility with LangChain through StructuredTool integration.
LLM (Large Language Model) : AI models trained on large amounts of text data that can understand and generate human-like text. LLMs use this toolkit's functions as tools.
Local Operations : Operations that run on the same machine as the agent, without requiring network connections or external services. This toolkit focuses on local operations.
MCP (Model Context Protocol) : A protocol for providing context and tools to AI models. This toolkit can be adapted for MCP compatibility.
Metadata : Additional information about data or operations. Toolkit functions often return metadata along with primary results.
Module : A collection of related functions organized together. This toolkit has 12 modules covering different areas of functionality.
Monitoring : The process of observing and tracking system performance or behavior. The monitoring module provides tools for this purpose.
Non-HTTP Actions : Operations that don't require internet connectivity or web services. This toolkit specializes in non-HTTP local actions.
Network Operations : Functions that interact with network resources like DNS, HTTP requests, or port checking.
Optional Dependencies : Dependencies that are not required for core functionality but enable additional features. This toolkit uses optional dependencies for specialized operations.
Orchestration : The coordination of multiple automated tasks or processes. AI agents often orchestrate various tools to complete complex tasks.
Parameter : Input values passed to functions. This toolkit uses simple parameter types for agent compatibility.
Path Validation : Checking that file paths are valid and safe to use. The file system module includes comprehensive path validation.
Performance Monitoring : Tracking system resource usage and performance metrics. The monitoring module provides performance monitoring functions.
Plugin Architecture : A design that allows additional functionality to be added through plugins. Some agent frameworks use plugin architectures.
Query : A request for information or action. Agents process user queries and determine which tools to use.
Quality Assurance (QA) : Processes for ensuring software meets quality standards. This toolkit includes comprehensive QA processes.
Return Type : The type of value that a function returns. This toolkit uses consistent return types (usually dictionaries) across all functions.
Runtime Environment : The environment in which software runs, including the operating system, Python version, and available libraries.
Schema Validation : Checking that data conforms to a defined structure or format. The data module includes schema validation functions.
Serialization : Converting data structures into a format that can be stored or transmitted. JSON serialization is important for agent compatibility.
Signature Complexity : How complex a function's signature is. Complex signatures can cause "signature too complex" errors in agent frameworks.
Strands Agents
: An agent framework that this toolkit supports through @strands_tool decorators.
Structured Data : Data organized in a predictable format. This toolkit returns structured data for predictable agent integration.
Structured Tool : In LangChain, a tool with a defined schema for inputs and outputs. Toolkit functions can be wrapped as StructuredTools.
Timeout : A limit on how long an operation can run before being cancelled. Many toolkit functions accept timeout parameters.
Tool : In the context of AI agents, a function or capability that the agent can use to perform tasks. All functions in this toolkit are designed as tools.
Tool Loading : The process of making functions available to an agent framework. This toolkit provides helper functions for tool loading.
Type Annotation : Python syntax for specifying the types of variables and function parameters. This toolkit uses comprehensive type annotations.
Type Safety : Programming practices that prevent type-related errors. This toolkit emphasizes type safety for reliable agent integration.
Union Types : Python type hints that allow a variable to be one of several types. This toolkit avoids Union types for agent compatibility.
Utility Functions : General-purpose functions that provide common functionality. The utilities module contains debugging and timing functions.
Validation : Checking that data meets certain criteria or requirements. Many toolkit functions include input validation.
Versioning : The practice of assigning version numbers to software releases. This toolkit follows semantic versioning.
Workflow : A sequence of tasks or operations. Agents often execute workflows using multiple tools from this toolkit.
Wrapper Function : A function that calls another function while adding additional functionality. Some frameworks require wrapper functions for integration.
XZ Compression : A compression algorithm supported by the archive module for efficient file compression.
YAML (YAML Ain't Markup Language) : A human-readable data serialization format. The data module includes YAML processing functions.
Zero Dependencies : Software that doesn't require additional packages to function. The core functionality of this toolkit has zero dependencies beyond Python's standard library.
Agent : The main class representing an AI agent in Google ADK.
Function Tool : The Google ADK concept for functions that agents can call.
LiteLlm : A model interface in Google ADK for working with various language models.
Model : The language model used by an agent in Google ADK.
Chain : A sequence of calls to language models and tools.
StructuredTool : LangChain's wrapper for functions with defined input/output schemas.
Tool : LangChain's base class for agent tools.
Toolkit : A collection of related tools in LangChain.
@strands_tool : A decorator that makes functions compatible with Strands Agents framework.
Strands Agent : An AI agent built using the Strands framework.
Decorator
: A function that modifies the behavior of another function, used with @ syntax.
Dict[str, Union[str, int]] : Type annotation for a dictionary with string keys and values that can be strings or integers.
List[str] : Type annotation for a list containing string elements.
Optional[str] : Type annotation indicating a parameter can be a string or None (avoided in this toolkit).
API (Application Programming Interface) : A set of functions and protocols for building software applications.
CLI (Command Line Interface) : A text-based interface for interacting with software.
SDK (Software Development Kit) : A collection of tools, libraries, and documentation for developing software.
REST API : A type of web API that follows REST architectural principles.
CSV (Comma-Separated Values) : A file format for storing tabular data.
JSON (JavaScript Object Notation) : A lightweight data interchange format.
TOML (Tom's Obvious, Minimal Language) : A configuration file format.
YAML (YAML Ain't Markup Language) : A human-readable data serialization format.
| Acronym | Full Form | Context |
|---|---|---|
| ADK | Agent Development Kit | Google's agent framework |
| AI | Artificial Intelligence | General AI technology |
| API | Application Programming Interface | Software interfaces |
| CLI | Command Line Interface | Text-based interfaces |
| CSV | Comma-Separated Values | Data file format |
| DNS | Domain Name System | Network name resolution |
| FAQ | Frequently Asked Questions | Documentation |
| HTTP | HyperText Transfer Protocol | Web communication |
| JSON | JavaScript Object Notation | Data format |
| LLM | Large Language Model | AI model type |
| MCP | Model Context Protocol | AI context protocol |
| Portable Document Format | Document format | |
| QA | Quality Assurance | Testing processes |
| REST | Representational State Transfer | API architecture |
| SDK | Software Development Kit | Development tools |
| SSL | Secure Sockets Layer | Security protocol |
| TOML | Tom's Obvious, Minimal Language | Configuration format |
| URL | Uniform Resource Locator | Web addresses |
| UUID | Universally Unique Identifier | Unique identifiers |
| YAML | YAML Ain't Markup Language | Data format |
For more specific terminology related to individual modules, see the respective module documentation linked in the API Reference.