Which AI agent should you use?

The right answer depends on three things: your team's technical level, your primary use case, and how much control you need over the infrastructure. There is no single best AI agent.

The picker above asks five questions and matches you to one of fifteen platforms we've tested. Quick rule of thumb if you don't want to take it:

• Non-technical, need it working this week: Lindy.
• Already using Claude and want to do more: Claude Code.
• Have a developer, want maximum flexibility: n8n.
• Running multiple agents, need orchestration: Paperclip.

For most B2B sales teams: Lindy. The pre-built sales templates are genuinely polished — meeting prep, follow-up sequences, lead enrichment, CRM updates. Non-technical ops and sales people can ship working automations in hours, not weeks.

For more sophisticated outbound research workflows where you need conditional logic and multi-step research: Relevance AI. Steeper learning curve but more powerful.

For teams with a developer who want full control: n8n handles complex sales pipelines with native AI agent steps and 400+ integrations.

Run the cost calculator on the "sales follow-up" or "cold outreach personalisation" workflow to see what each model costs at your expected volume.

For ticket triage and first-response drafting: Lindy on the no-code path, n8n on the developer path. Both handle the standard pattern: read incoming ticket, classify by urgency, draft a response, route to the right queue.

For document Q&A — answering customer questions against your internal knowledge base, SOPs, or product docs: Stack AI. The RAG implementation is one of the better ones in the no-code category.

The cost calculator's "customer support reply" workflow shows the model spread for this use case. Most support workloads run well on Claude Haiku 4.5 or Gemini 2.5 Flash — frontier-tier models are usually overkill.

A chatbot answers questions. An AI agent takes actions.

ChatGPT can tell you how to draft a follow-up email. An AI agent drafts it, finds the contact details, sends it, and logs the activity in your CRM — without you touching a keyboard. The underlying language model is often the same. What's different is the layer on top: the harness that connects the model to your tools, your data, and your workflows.

We cover this in more depth in the FAQ.

All three are AI coding tools but they answer different questions:

• Claude Code is the most agentic — terminal-first, reads your codebase, makes coordinated multi-file changes. Best if you already pay for Claude Pro and want to do more with it.
• Cursor is an AI-first IDE — VS Code fork with multi-model flexibility (Claude, GPT, Gemini in the same session). Best for rapid prototyping and exploration. 360k paying users — the largest by far.
• GitHub Copilot is autocomplete-first, with a newer agent mode. Best if you want one tool that lives in the IDE you already use, and you're already on GitHub Business or Enterprise.

Most power users run more than one. Cursor for daily editing, Claude Code for autonomous tasks, Copilot for the team chat features. The picker above asks the right questions to point you at the right starting point.

Yes. Two paths:

• Lindy is the most operator-friendly platform we tested. Describe what you want in plain English; Lindy builds it. No-code, no setup, working in an afternoon.
• Relevance AI sits between pure no-code and developer tools. Has more flexibility than Lindy but takes a builder mindset to use well.

A growing pattern is non-developers using Claude Code to build internal tools. Claude writes and runs the code; the builder describes what they want in plain English. It's technically "coding" but you don't need to know how to code yourself. Sometimes called "vibe coding."

Two layers: the platform cost and the model cost. Platform costs range from free (open-source like OpenClaw, Hermes) to $19–$199 per month (no-code SaaS) to enterprise contracts. Model costs are per-token API charges that scale with how much your agent does.

For a typical builder workflow at moderate volume (1,000 tasks per month), expect total monthly costs in the $20–$300 range depending on model choice. The AI Agent Cost Calculator shows exact numbers for any combination of workflow, volume, and model.