Gravity vs Relevance AI: No-Code Canvas vs Outcome Prompt (2026)

Relevance AI built a category around the AI workforce metaphor, branded sales agents that act like teammates. Gravity is the opposite, anonymous agents defined entirely by what they do. The marketing is different. The runtime trade-offs are also different.

What Relevance AI is, and where it actually shines

Relevance AI is a no-code agent platform from a team out of Sydney. The product is known for its AI workforce framing and named agents that handle sales and other go-to-market functions. The builder is a canvas where you configure tools, prompts, and triggers per agent.

Where it shines:

• Go-to-market teams who want a named SDR-style agent with an identity.
• Marketing teams that want to attach a face to the automation, for change management with stakeholders.
• Companies that want to embed agents in their customer-facing surface.
• Teams that like a flexible canvas with chains, tools, and conditional logic.
• Buyers who respond to the AI workforce pitch.

The product is mature and the team has been at this for several years. The canvas is one of the cleaner no-code agent builders on the market.

What Gravity does differently

Gravity does not give the agent a name. It does not give it a face. The agent is whatever the outcome sentence describes.

"Every Monday at 9am IST, pull the last week's customer support tickets from Zendesk. Cluster them by topic. Post the top three trending issues in our #product Slack channel with example ticket links."

That is a Gravity agent. There is no avatar, no name, no AI worker metaphor. The agent is defined by the work. The runtime composes the schedule, the tickets pull, the clustering, the Slack post, and the rate limits. Describing outcomes is the whole interface.

Side-by-side capability comparison

The AI-workforce vs outcome-runtime split

The deepest difference is metaphor. Relevance wants you to think of agents as digital employees with a role. Gravity wants you to think of agents as goal-bound processes.

The metaphor matters because it shapes how you scope, change, and trust an agent. If you have an SDR agent named Bosh, the team has a clear mental model and can debug interactions like they would with a junior teammate. The downside is that the metaphor leaks. An agent is not a person. It cannot exercise judgement beyond its tools. When the metaphor leaks, trust breaks.

Headless agents do not carry that leakage. You describe what the agent does. If it does it, great. If it does not, you edit the sentence. There is no roleplay layer. Why most AI agents stop after one task is partly a story about over-anthropomorphising agents.

Pricing reality

• Relevance AI: Tiered pricing with credits per run. Higher tiers unlock more agents, integrations, and seats.
• Gravity: Bundled monthly fee that includes runtime, observability, and connectors.

For sales-heavy teams using one or two high-volume agents, Relevance can be cost-effective if credits align with usage. For teams running many low-volume operational agents, the bundled Gravity fee tends to be simpler to forecast.

When Relevance AI is the right choice

• You are running outbound sales and like the AI worker metaphor.
• Your stakeholders need a named agent to feel comfortable.
• You want a canvas builder that has been hardened over years.
• Sales-specific templates and patterns match your need.
• You want an agent embedded in a customer-facing flow.

When Gravity is the right choice

• You want operational agents without anthropomorphising them.
• Your agents change weekly and you prefer editing a sentence over moving boxes on a canvas.
• You want native scheduling, stop conditions, and approvals.
• You want one bundled bill.
• You do not want a credits-based meter.

Migration: what changes if you switch

1. Pick a Relevance AI agent. Write down its job in one sentence.
2. Add stop conditions and schedule to the sentence.
3. Connect OAuths in Gravity.
4. Run a dry run.
5. Cut over.

If your Relevance agent has a deep custom logic per node, expect to spend a session translating that logic into the outcome sentence and stop conditions.

Frequently asked questions

Is Relevance AI really no-code?

Mostly yes. The agent builder is a canvas with configurable nodes. For most use cases you do not write code, but custom logic in a node still requires expressions, and integrations with non-supported tools need workarounds.

What is Relevance AI best known for?

AI workforce style products, particularly sales agents like Bosh and similar named agents. The product positions strongly toward go-to-market teams.

Where does Gravity fit?

Gravity is also targeting operational agents, but the surface is different. You write one sentence, no canvas. The agents are not segmented by role like a sales agent or a research agent. They are defined by the outcome you describe.

Can Relevance AI agents run on a schedule?

Yes, with trigger nodes and external schedulers. Gravity has scheduling as a first-class property of the prompt.

Which is better for non-technical founders?

Both serve non-technical users better than code-first tools, but the canvas paradigm still requires you to think in nodes. The outcome prompt paradigm requires you to think in results. Most non-technical operators find results easier.

Three takeaways before you close this tab

1. Relevance leans into the AI worker metaphor. Gravity leans away from it.
2. The right metaphor depends on the stakeholder. Sales teams like Bosh. Ops teams just want the inbox triaged.
3. Pick by your scope. Sales-specific use cases tilt Relevance. Broad operational scope tilts Gravity.

Sources

• Relevance AI. "Official product page." relevanceai.com
• Relevance AI. "Pricing page." relevanceai.com/pricing
• Relevance AI. "Documentation." relevanceai.com/docs