Relevance AI

Platform for building teams of AI agents with tools and shared memory

AI Agents & AutomationFree planOverseasβ˜…β˜…β˜…β˜†β˜† 3.0

What is Relevance AI?

Relevance AI is a platform for assembling AI agents and putting them to work on business processes. You create an agent, give it tools and knowledge, and deploy it into a workflow, a chat interface or an API. Its emphasis is on teams of agents: a coordinator delegating to specialists, each with its own tools, plus shared context so work does not need repeating. There is a no-code builder for configuration and a code path for custom tools, which lets operations teams start and engineering take over where needed.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

  • Multi-agent teams with a coordinator and specialist agents
  • Tool library plus custom tools written as code
  • Knowledge bases for grounded document-based answers
  • Deployment as chat, embed, API or scheduled workflow
  • Shared memory and context across an agent team
  • Templates for sales, marketing, research and support tasks

Pros & cons

Strengths

  • Multi-agent structure is the default, not an add-on
  • No-code setup with a code escape hatch when needed
  • Templates shorten the path to a first working process

Watch out for

  • Credit-based pricing needs monitoring in production
  • Complex agent teams are difficult to evaluate objectively
  • Results vary widely with the quality of the knowledge you load

Best for & use cases

sales research, marketing operations, lead qualification and internal knowledge work

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FAQ

What is an agent team here?

A group of agents with different roles that pass work between them, usually with one coordinator. It mirrors how a small team divides a job rather than asking one agent to do everything.

Do I need to code?

No for most setups. Tools, knowledge and prompts are configured in the interface, with custom code available when a tool needs to call something the library does not cover.

How do I know it is working?

Define a small evaluation set with expected outputs and run it after every change. Without that, agent teams tend to look impressive while quietly drifting on the cases that matter.