AI glossary for content assistants
Plain-English definitions of 13,917 AI terms for branded assistant teams.
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13,917 terms. Open one for definitions and related concepts.
Computer-use Agent
An AI agent that can operate a computer desktop, using the mouse, keyboard, and screen like a human user to interact with any software application.
API Agent
An AI agent that interacts with external services through their APIs, making HTTP requests and processing responses to accomplish tasks programmatically.
Data Agent
An AI agent specialized in working with data, capable of querying databases, analyzing datasets, creating visualizations, and generating insights.
Retrieval Agent
An AI agent that specializes in finding and retrieving relevant information from knowledge bases, documents, and other data sources to answer user queries.
LangChain
A popular open-source framework for building applications with large language models, providing components for chains, agents, memory, retrieval, and tool use.
LangGraph
A framework from LangChain for building stateful, multi-actor agent applications using graph-based workflows with cycles, branching, and persistence.
LlamaIndex
An open-source framework focused on connecting LLMs with data, providing optimized tools for indexing, retrieval, and RAG application development.
CrewAI
A framework for orchestrating multiple AI agents that work together as a team, with each agent having a defined role, tools, and objectives.
AutoGen
A Microsoft framework for building multi-agent conversational systems where AI agents can chat with each other and with humans to accomplish tasks.
Semantic Kernel
Microsoft's open-source SDK for integrating LLMs into applications, providing an orchestration layer for AI plugins, planners, and memory management.
Haystack
An open-source framework by deepset for building production-ready LLM applications with a focus on search, RAG, and question-answering pipelines.
Dify
An open-source platform for building AI applications with a visual interface, supporting RAG, agents, workflow orchestration, and model management.
Flowise
An open-source visual tool for building LangChain-based LLM applications through a drag-and-drop interface without writing code.
Botpress
An open-source conversational AI platform for building, deploying, and managing chatbots with visual flow design and LLM integration.
Rasa
An open-source machine learning framework for building conversational AI assistants with custom NLU, dialogue management, and integration capabilities.
Vercel AI SDK
A TypeScript library for building AI-powered user interfaces, providing streaming, tool calling, and generative UI primitives for web applications.
AutoGPT
One of the first widely known autonomous AI agent projects, demonstrating how LLMs can be given goals and tools to accomplish tasks independently.
BabyAGI
A minimalist autonomous agent framework that maintains a task list, prioritizes tasks, executes them, and creates new tasks based on results.
MetaGPT
A multi-agent framework that assigns real-world software engineering roles to agents, enabling them to collaboratively produce software through structured processes.
SWE-agent
A system that turns language models into software engineering agents capable of fixing real GitHub issues by navigating codebases and making targeted code changes.
Devin
An AI software engineering agent by Cognition that can plan, write, debug, and deploy code autonomously, operating as a virtual developer teammate.
Cursor
An AI-powered code editor that integrates LLM capabilities directly into the development workflow, offering intelligent code completion, editing, and chat.
Aider
An open-source AI pair programming tool that works in the terminal, allowing developers to make code changes through natural language conversation with git integration.
ReAct
Reasoning and Acting is an agent pattern where the model alternates between thinking through a problem (reasoning) and taking actions (acting) in an interleaved loop.
Plan-and-execute
An agent pattern that separates planning from execution: first create a complete plan of steps, then execute each step, replanning as needed based on results.
Self-reflection
An agent pattern where the model evaluates its own outputs, identifies errors or areas for improvement, and revises its work based on this self-assessment.
Self-correction
An agent's ability to detect errors in its own outputs or actions and automatically fix them without human intervention, improving reliability.
Inner Monologue
An agent pattern where the model generates internal reasoning text that guides its actions but is not shown to the user, improving decision quality.
Tool Selection
The process by which an AI agent decides which available tool to use for a given task, based on the tool's description, the user's intent, and the current context.
Tool Routing
A technique for directing agent requests to the appropriate tool or sub-system, especially useful when many tools are available and selection becomes complex.
Tool Chaining
The pattern of using the output of one tool as the input to another, creating a sequence of tool calls that accomplishes complex multi-step tasks.
Agent Loop
The core execution cycle of an AI agent: observe the current state, reason about what to do, take an action, observe the result, and repeat until the goal is achieved.
Observation-action Loop
An agent execution pattern that alternates between observing the environment state and taking actions, forming the basic cycle of agent interaction with its environment.
Goal Decomposition
Breaking a high-level goal into smaller, manageable sub-goals that an agent can achieve incrementally, enabling complex task completion.
Task Decomposition
Breaking a complex task into simpler, executable sub-tasks that an agent can complete sequentially or in parallel to accomplish the overall objective.
Iterative Refinement
An agent pattern where outputs are progressively improved through multiple rounds of generation, evaluation, and revision until quality standards are met.
Error Recovery
An agent's ability to detect, diagnose, and recover from errors during task execution, maintaining progress and finding alternative approaches when problems occur.
Retry Logic
A mechanism that automatically retries failed operations with modifications, such as different parameters, backoff delays, or alternative approaches.
Fallback Strategy
A predefined alternative approach that an agent uses when its primary method fails, ensuring task completion through backup methods.
Function Calling
A capability of LLMs to generate structured function calls with appropriate parameters, enabling them to use tools and interact with external systems.
Tool Definition
A structured description of a tool's purpose, parameters, and expected behavior that enables an AI model to understand when and how to use it.
Tool Parameters
The input values that must be provided when calling a tool, defined by a schema that specifies types, constraints, and descriptions for each parameter.
Tool Schema
A formal specification of a tool's interface, defining its parameters using a structured format like JSON Schema that enables validation and documentation.
JSON Schema
A vocabulary for annotating and validating JSON data, widely used in AI to define tool parameters, structured outputs, and data contracts.
Tool Invocation
The act of an AI agent calling a tool with specific parameters, triggering the execution of an external function or API to accomplish a task.
Tool Execution
The actual running of a tool function with provided parameters, separate from the AI model's generation of the tool call.
Parallel Tool Calls
The ability of an AI model to generate multiple independent tool calls simultaneously, which are then executed in parallel for faster task completion.
Structured Output
The ability of LLMs to generate responses in specific structured formats like JSON, following a defined schema for reliable data extraction and tool integration.
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InsertChat is a white-label AI assistant for your website. Train it, brand it, publish it, and learn from visitor questions.
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Connect approved pages, docs, videos, FAQs, policies, and other sources. InsertChat turns them into source-backed answers and next steps.
Can I control the assistant's tone and sources?
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Can it collect leads or route support questions?
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Can I control how the assistant behaves?
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Which AI models can I use?
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Can I pick different models for different workflows?
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Where can I deploy an assistant?
Use a widget, embed, full-page assistant, custom domain, in-app embed, or API. Reuse one setup across surfaces.
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No. Build and deploy AI assistants using our visual builder. The embed code is one line of JavaScript.
Can I customize the branding and UI?
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Can I use my own domain?
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Does InsertChat support voice?
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Does InsertChat support vision?
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Zendesk, HubSpot, Shopify, WooCommerce, calendar booking, web search, Perplexity, and webhooks for your own systems.
Can I control which tools the assistant is allowed to use?
Yes. Tool access is controlled per assistant so you enable only what you need.
Can the agent hand off to a human?
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Do you provide analytics?
Yes. Track chats, leads, feedback, top questions, unanswered questions, most-used sources, and content gaps.
Is it mobile friendly?
Yes. The widget and embeds work well on desktop and mobile with no separate experience needed.
What's the fastest path to a successful deployment?
Start with one assistant and a small set of high-value sources. Iterate using real questions from analytics.
What is the fastest way to get started?
Create an account. Connect one key source. Ask a test question, brand the assistant, then publish it on one page.