Vellum enables users to build AI agents by describing tasks in plain English. It automatically creates working agents that can run on schedules, trigger via API, or operate through a UI.
Vellum is an AI agent builder and platform that empowers users to create autonomous assistants by simply describing tasks in plain English. It is designed for developers, power users, and teams who need custom AI agents that function across multiple channels without requiring deep machine learning expertise. The core value of this AI agent platform lies in its ability to automatically generate working agents from natural language descriptions, dramatically reducing setup time and complexity. Vellum's agents are not static; they adapt by learning from user preferences and improve over time, becoming increasingly tailored to individual workflows. With support for scheduling, API triggers, and direct UI interaction, Vellum provides a flexible foundation for automating both routine tasks and complex processes alike.
Many AI tools suffer from persistent memory gaps, forcing users to repeatedly provide context and re-explain their needs. This leads to inefficiency and frustration, especially for complex ongoing tasks. Vellum solves this pain point with a sophisticated managed memory system that includes episodic, semantic, procedural, and emotional layers. Episodic memory records specific past events, such as clearing a user's calendar when they spontaneously help a colleague, allowing the agent to act proactively. Semantic memory stores factual knowledge and user preferences, while procedural memory automates sequences of actions. Emotional memory captures sentiment to adjust tone and responses. Together, these memory types ensure the agent maintains coherent long-term interactions and adapts to evolving user behavior, eliminating the need for constant re-iteration.
The first major feature group is Vellum's multi-layered memory system, which distinguishes it from many basic AI assistants. Episodic memory allows the agent to recall and act on concrete past instances, like noticing a pattern of helping colleagues and automatically rescheduling tasks. Semantic memory retains user-defined facts and general knowledge, such as preferred meeting times or project guidelines. Procedural memory enables the agent to learn and automate sequences, like a routine data backup process. Emotional memory tracks user mood and communication style, adjusting responses to be more empathetic or professional as appropriate. This combination ensures that the agent not only remembers what was said but understands context and intent, providing a truly personalized and intelligent experience that improves with each interaction.
The second major feature group is the skill adaptation and learning system. Vellum's skills explicitly 'adapt to how you work, learning from your preferences and getting better over time.' This means that as users interact with the agent, it fine-tunes its behavior based on repeated actions and explicit feedback. For example, if a user consistently asks for a summary of their morning email in a bulleted list, the agent will learn to default to that format. Users can also pick a name and set a personality, giving the assistant a distinct character that aligns with their brand or personal style. This continuous learning process reduces manual configuration and makes the agent feel like a true collaborative partner that understands individual workflows and anticipates needs.
Publisher
A
admin
Launch Date2026-01-14
Platformweb
Pricingfreemium
Domain Authority
Ahrefs DR74/100