AI tools for building AI agents
An AI agent picks its own steps and tools, so one request can cost very different amounts and can write to real accounts. Compare five tools on how a run is billed, which actions can wait for your approval and where the agent acts.
Which tool fits, in short?
Use Gumloop if you want every agent run to show a cost breakdown by model, tools and compute. Lindy fits a team delegating requests in Slack from a shared credit pool. n8n suits self-hosting and approval gates on single tools. Make fits agent steps inside existing scenarios. Tabbit acts in your own browser. Require approval for every tool that writes or sends.
This is a source-led shortlist, not a performance ranking. Open each profile for the provider source, check dates and plan conditions.
About this shortlist
What this page compares
- 5 published profiles
- Plans and access shown where documented
- Official source dates stay attached to each profile
- No invented score or hands-on result
How each option fits building ai agents
Each note comes from the tool's source-checked profile. The condition line is what to confirm before you rely on it for this task.
Gumloop
An agent chats, calls connected tools and returns versioned files you can download, and the chat header opens a breakdown of the run's credits by model, tool calls, compute and orchestration.
Check first: Pro (from $37 a month, card-required trial) bills each run as model cost plus at least 1 credit per successful tool call and 5 credits per active minute, then an 8% orchestration fee, or 16% with your own model key.
Lindy
An AI teammate takes requests in Slack, works through connected tools and edits files with version history, drawing on a credit pool shared by every paid seat.
Check first: Plus is USD 29.99 per user for 3,000 credits; Lindy's pricing page puts everyday asks at 2 to 250 credits and deep work at 250 to 1,000, write guardrails cover shared Slack threads only, and credits do not roll over.
n8n
The AI Agent node calls LLM providers, workflow tools and MCP servers inside a normal workflow, and any single tool can be set to pause for a human Approve or Deny in Slack, Telegram or n8n Chat.
Check first: Plans count executions per workflow run regardless of steps, so tool calls do not add executions, but model tokens are billed by your LLM provider; the only lasting free route is self-hosted Community.
Make
The Make AI Agents app runs agentic steps inside a visual scenario, so the agent's tool calls sit next to the routers, filters and error handlers that handle the rest of the process.
Check first: The app is in open beta; a run uses 1 credit per operation plus 1 per called tool operation, Make's own AI Provider adds token-based credits, and a custom OpenAI or Anthropic connection bills tokens separately.
Tabbit
Its agent mode opens pages, clicks and fills forms inside a Chromium browser on your computer, and saved Skills plus scheduled tasks repeat a browser routine without an API integration.
Check first: Usage is rated x1, x10 and x100 with no published base unit; the free Standard tier needs Tabbit as your default browser and allows one scheduled task, while Pro (US$30 a month) allows ten.
Considered but not listed
- Zapier: Zapier's profile documents tasks for Zaps and for MCP tool calls, but not the billing of its separate Agents product, so a per-run agent cost cannot be compared here; Zaps are covered on the workflow automation page.
- Copy.ai: Copy.ai's content agents learn a recurring content type and brand tone; they write rather than act in other apps, and workflow credits start with Growth.
- Jasper: Jasper's marketing agents draft and research copy, and custom no-code agents are listed for Business only; the job is content production rather than acting across tools.
- Jace AI: Jace AI drafts email replies and invites for you to approve in one connected inbox; it fits that single job rather than a general agent, and its credit cost per action is not published.
- ChatGPT: ChatGPT runs scheduled tasks in the background, but its profile does not document an agent that acts in your business apps with per-tool approval, so it is not compared on that basis here.
Compare tools for building AI agents
Use the profiles below to inspect the specific workflow, pricing and limits before making a choice.
Gumloop
AutomationGumloop agents use connected tools and produce downloadable files, with separate charges for models, tools, active compute and orchestration.
Lindy
AutomationLindy is an AI teammate for delegated team tasks, with shared workspace credits, versioned files and channel-specific approval controls.
n8n
Automationn8n is a workflow automation platform you can run on managed Cloud or self-host for free, billed per whole workflow run rather than per step.
Make
AutomationMake is a cloud automation builder that links app modules in visual scenarios and bills each module run in credits.
Tabbit
AutomationTabbit is a free AI browser for macOS and Windows with a chat sidebar, an agent that acts on web pages, Skills and scheduled tasks.
What to compare for this task
These questions keep the shortlist tied to the work instead of turning it into a generic feature list.
Cost per run
Is a run billed by model tokens, tool calls, active minutes, operations or whole executions, and can you see the breakdown after each run?
Approval before writes
Can a person approve a specific tool call, such as sending email or updating a CRM, while read-only tools run freely?
Spend ceiling
When a run or the monthly pool reaches its limit, does the agent pause and ask, or keep spending?
Where it acts
Does the agent act through API connectors, through your own browser session, or on infrastructure you host?
Compare the shortlist
Values come from the linked profile fields. Blank or unverified details stay visible rather than being filled by assumption.
| Documented detail | Gumloop | Lindy | n8n | Make | Tabbit |
|---|---|---|---|---|---|
| Access | Free trial only | Free trial only | Free access available | Free access available | Free access available |
| Paid plans from | From $37/month; confirm checkout currency and tax | USD 29.99 per user per month | EUR 20 per month, billed annually | Free + paid; quote varies | US$30 per month, billed monthly |
| Best starting point | Agent tasks that need an inspectable file handoff | Teams drafting work from shared context | Self-hostable workflow and AI-agent automation | Visual automation for app workflows | Browser tasks and research handed to an AI agent |
| Source status | Source-checked: 27 Sep 2026Pricing checked: 27 Sep 2026 | Source-checked: 27 Sep 2026Pricing checked: 1 Oct 2026 | Source-checked: 24 Sep 2026Pricing checked: 24 Sep 2026 · Update due | Source-checked: 24 Sep 2026Pricing check incomplete | Source-checked: 29 Sep 2026Pricing checked: 29 Sep 2026 |
| Profile | Gumloop profile | Lindy profile | n8n profile | Make profile | Tabbit profile |
Head-to-head comparisons for this task
Both tools in each comparison are on this shortlist.
One fixed task to compare them
Proposed evaluation, not performed: run the same input through two or three shortlisted tools and judge each against the same acceptance line. We have not run it; results depend on your plan and account.
Sample input
A test spreadsheet of 12 inbound leads, L-01 to L-12, with name, company, website and message. L-04 has no website, L-09 duplicates L-02 and L-11 asks not to be contacted. Goal in one instruction: research each company's website, write a two-line summary into a new column and draft (not send) a reply for leads worth contacting.
Steps
- Connect only the test sheet and a drafts-only email destination; set the email tool to require approval where per-tool approval exists, and record it if it does not.
- Give the goal as one instruction without scripting the per-row steps, and note the balance before the run.
- Deny one approval request on purpose and note what the agent does next.
- Read the units spent and the number of tool calls from the tool's own run log or cost breakdown.
- Passes if
- Ten summaries are written, L-04 is flagged instead of given an invented site, L-09 is recognised as a duplicate, no draft exists for L-11, nothing is sent, the denied action is not retried silently, and the cost comes from the tool's log.
- Fails if
- The agent sends or could send without approval, invents a website, drafts a reply to L-11, loops on the denied call, or the run's cost cannot be found afterwards.
Guides for this task
Browse by category
The categories of the shortlisted tools, with every profile in each.
Questions about building AI agents with AI
What is the difference between an AI agent and a workflow automation?
In a workflow you draw every step and branch in advance; with an agent you state a goal and the model chooses which tools to call and in what order. Agents cope better with messy input but are harder to predict and to budget, so a practical pattern is a fixed workflow with an agent step only where judgment is needed.
What does an AI agent actually do?
An AI agent takes a goal, decides which connected tools to call and in what order, and returns a result or an action. Among the tools compared here, Gumloop's agent chats, calls tools and returns downloadable files, Lindy takes requests in Slack, n8n's AI Agent node calls model providers, workflow tools and MCP servers, and Tabbit's agent opens pages and fills forms in your browser.
Is it safe to give an AI agent access to email or a CRM?
Limit it first. Start with test or read-only accounts, connect only the tools the job needs, and require approval for anything that writes or sends. n8n can gate a single tool behind Approve or Deny, Gumloop offers per-tool approval modes, and Lindy documents write guardrails for shared Slack threads with different boundaries in private chats.
Can I self-host an AI agent?
Yes with n8n: its Community edition runs AI Agent workflows on your own servers without a time limit, though hosting, updates, backups and the model provider remain your job. Tabbit's agent works in a browser on your own computer, but its models are hosted. Gumloop, Lindy and Make are hosted services, so ask about data handling before connecting regulated data.
Why did one agent request use so many credits?
Agents loop: each step can reread the growing conversation, call a tool and reason again, so one request may trigger many model and tool charges. Gumloop notes that longer chats cost more per message as context grows and that every connected tool adds its definition to the prompt. Disconnect unused tools and open a fresh chat for each new job.
Sources for this task
These checks cover the task conditions below. Each linked profile keeps its own feature and pricing evidence.
- Gumloop creditsdocs.gumloop.com · Checked 2026-10-02
Credit value, the four agent cost components, failed tool calls not charged, 8% or 16% orchestration fee and per-chat credit warnings; not an observed bill.
- Lindy pricinglindy.ai · Checked 2026-10-02
Plan credits per user, example credit ranges per request type, no rollover and pause when the pool runs low; computer use listed on all plans.
- n8n human-in-the-loop for toolsdocs.n8n.io · Checked 2026-10-02
Approve or Deny before a specific AI Agent tool runs, with Slack, Telegram or n8n Chat as channels; not a plan or price check.
- Make credit usage for AI agentshelp.make.com · Checked 2026-10-02
1 credit per operation, 1 per called tool operation and token-based credits with Make's AI Provider; no complete run example.
All 5 task sources
- n8n pricingn8n.io · Checked 2026-10-02
Cloud plans priced by monthly workflow executions regardless of complexity, with agents, MCP and AI nodes listed on Starter.
Related tasks
Tasks whose shortlists share at least two tools with this one.
How to use this shortlist
Start with the task fit, then open the profile whose documented workflow matches your constraints. Check the source date, plan terms and limits on the provider site before you subscribe or publish an output.