Relevance AI targets business teams with a low-code platform for building an AI workforce: agents assembled from templates and no-code tools, often for sales and operations. AgentRow targets builders who need to see and control the execution graph, the tool policy and the trace of every run, and who deploy the result to their own users through a widget or an API.
This page describes Relevance AI as it presents itself in its own documentation at the time of review. Check the current docs before relying on any cell. Competitor column last reviewed 2026-09-10. Relevance AI's own site: relevanceai.com
Choose AgentRow when an engineer will own the agent after launch and needs the executable graph, typed tools with safety levels, and a complete trace to debug from.
Choose Relevance AI when an operator will assemble the agent from templates and ready-made connectors, and business automation matters more than graph-level control.
Use both when business teams automate internal tasks in Relevance AI while the product team ships a customer-facing assistant with AgentRow.
| Capability | AgentRow | Relevance AI |
|---|---|---|
| Building approach | Visual, executable graph. The workflow drawn in the editor is the workflow the runtime runs, validated with one rule set in the dashboard and the runtime. | Low-code agent builder with templates and a no-code tool builder where tools are sequences of steps (LLM prompts, API calls, code). Execution logic is expressed as agent instructions and tool steps rather than an editable workflow graph. |
| Multi-agent orchestration | Apps attach several agents; a router selects the agent per message, runs it as a nested run, gates the output and applies an app-level safety check. Agents can also call agents as tools. | Multi-agent teams where an agent can delegate to sub-agents, positioned as an AI workforce. Routing is driven by agent instructions. |
| Tools and integrations | Typed tools with JSON Schema, eight auth types including the end user's own token, five safety levels, approval flag, rate limits, retries, allowed domains. HTTP API, webhook, browser and agent tools run today. | A catalogue of pre-built integrations for common SaaS products plus custom tools built in the platform. Per-tool safety levels and forwarding the end user's own token are not documented tool properties. |
| Knowledge and RAG | Built-in knowledge bases on Postgres with pgvector: chunking, embeddings, hybrid keyword and vector retrieval with reciprocal rank fusion, reranking, citations, retrieval test. | Knowledge tables and document uploads that agents can query; retrieval configuration is simpler and less exposed than a node-level retriever. |
| Memory | Scoped memory (user, conversation, agent, app, workspace) with explicit read and write nodes; candidates require approval by default. | Agent-level memory and conversation context; approval of extracted memory is not a documented feature. |
| Observability | Native. Every run records steps, exact prompts and responses, tool calls, retrievals, memory activity, tokens, cost and storage. No tracing SDK to add. | Task and run history per agent with the steps taken; exact prompts, retrieval candidates and per-call cost are less exposed than in a runtime trace. |
| Versioning and publishing | Agents and graphs have draft, published and archived versions; the test chat runs drafts, end users run only published versions; earlier versions can be restored. | Agents are edited live; version history exists for tools in some form. Draft-versus-published gating for end users is not a documented platform concept. |
| Deployment surface | Embeddable chat widget and end-user API with anonymous or verified identity (signed JWT), per-app credentials and sessions. | Chat embeds, API triggers, scheduled runs and messaging integrations, aimed at internal automation as much as customer-facing chat. |
| Guardrails and approvals | Blocked topics and deny or allow rules on the final answer, app-level safety check, tool safety levels and permission scopes. Interactive approval previews are on the roadmap. | Human approval of specific agent actions and escalation flows are part of the product; check the current documentation for how they apply per tool. |
| Hosting model | Hosted platform, early access via the waitlist. Bring your own model keys or run local models. | Hosted SaaS with usage-based pricing and a free tier at the time of review. |
| Best for | Product and engineering teams shipping an observable assistant to their own users. | Sales, marketing and operations teams automating work with templates and connectors, without engineers in the loop. |
Each run records one step per node, the exact prompt and response of every model call, every tool call with its input and output, every retrieval with candidates and scores, and the cost. When an answer is wrong, the cause is in the trace.
A tool declares its safety level from safe read to destructive, whether it needs approval, its rate limits and its allowed domains, and can act as the signed-in user with that user's own token. The toolbox enforces those properties on every call.
Knowledge bases expose chunk size, hybrid search, reranking, minimum score and token caps, with a retrieval test before you attach anything. The answer engine is not a black box you feed documents into.
End users run only published agent and app graphs; drafts stay in the test chat. Changes reach users when you publish, and earlier versions can be restored.
| In Relevance AI | In AgentRow |
|---|---|
| An agent with instructions and tools | An AgentRow agent with instructions, a model and tools attached in the Tools tab, running the default workflow. |
| A no-code tool of several steps | One HTTP API or webhook tool per external call, described for the model, chained by the model inside the toolbox loop. |
| Knowledge tables and uploads | A knowledge base with an index and a retrieval test. |
| Sub-agent delegation | An app with several agents and a router, or an agent tool inside a workflow. |
Choose by who will own the agent after launch.
If an engineer will debug it from traces and needs to control what each tool may do, AgentRow.
If an operator will assemble it from templates and connectors for internal automation, Relevance AI.
Yes for most agents: instructions, model, tools, knowledge and memory are dashboard settings and the default workflow needs no editing. The graph editor and tool schemas are there when you need control.
AgentRow connects to external systems through typed tools you define, mostly HTTP API and webhook tools. Integration guides cover common systems; a catalogue of one-click connectors is not the model today.
Both, differently. In AgentRow a tool marked as requiring approval does not execute silently, and interactive approval previews are on the roadmap. Relevance AI documents approval and escalation flows for agent actions; check its current documentation.
AgentRow ships an embeddable widget with anonymous or verified end-user identity and an end-user API. Relevance AI offers chat embeds among other deployment options.
Connect AI agents to HTTP APIs, webhooks, web pages and other agents with typed tools: JSON schemas, auth types, safety levels, limits and call logs.
Native LLM observability for AI agents: every run records steps, exact prompts, tool calls, retrievals, memory, tokens and cost. No tracing SDK needed.
Ground AI agents in your documents: knowledge bases with chunking, embeddings, hybrid keyword and vector retrieval, reranking, citations and full logs.
Start simple and scale when your agents do.
