The 13 Best AI Agent Builders in 2026: Screenshare-to-API, Record-and-Replay, No-Code and Vibecoding

AI agent builders come in four kinds: screenshare-to-API builders that turn a narrated demonstration into an agent that calls APIs, record-and-replay recorders that capture clicks and play them back, no-code builders where you prompt a copilot or draw a canvas, and vibecoding tools where an AI writes the agent as code. The best of each in 2026: Caddi for screenshare-to-API; the UiPath and Power Automate recorders for record-and-replay; Microsoft Copilot Studio, Salesforce Agentforce, Google Gemini Enterprise, Zapier Agents, Relevance AI, Lindy, Gumloop and n8n for no-code; and Claude Code, OpenAI Codex with the Agents SDK, and CrewAI for vibecoding. If the builder is your operations team at a law firm, RIA or insurance agency, Caddi builds the agent with them.

What sets the builders apart in 2026 is how you tell them what to build. Most wait for you to write the prompt or draw the flow, step by step. With Caddi you either show the task on a screen share or chat about it, and Caddi asks the questions, proposes the steps and builds the agent. It is free to try, and businesses can start with a two-week pilot.

Four ways to build an AI agent

Every builder on this list answers one question differently: how does the process get out of your head and into the agent? There are four answers in 2026, and they decide who can build, how much upkeep the agent needs, and what happens when the work does not go to plan.

Screenshare-to-API and record-and-replay look alike from the outside: both start with you doing the task on screen. Underneath they are opposites. A recorder keeps the clicks. Caddi keeps the intent: it watches the screen, reads the network requests behind every click and listens to you explain why, then builds an agent that talks to the apps directly.

What an AI agent builder is, and why the choice is harder than it looks

An AI agent builder is software for creating agents: programs that take a goal, use a language model to decide on the next step, and act in other systems through connectors or APIs. Every major platform vendor now ships one, and demand for the category is rising fast. The products look similar in a demo. They differ in five ways that matter once an agent is doing real work.

• How you tell it what to build. Most builders wait for instructions: a prompt, a canvas of nodes, or code. A few let you show the task instead. The more of the process you have to spell out, the more the agent depends on what you remembered to say.
• Who builds it. Some builders are frameworks for developers, some are canvases for business users, and some ask nobody to build at all. The person who knows the process best is rarely the person the tool was designed for.
• How a run is controlled. An agent that re-plans every step is flexible. A workflow that runs the same steps every time is repeatable. Most builders let you mix the two; the defaults differ.
• How usage is billed. Credits, actions, activities, executions and seats are all in use. The unit decides what a high-volume back-office process costs.
• Where it connects. A builder with thousands of SaaS connectors may still not reach the document management or practice management system your firm runs.

One tool is missing on purpose. OpenAI launched Agent Builder as part of AgentKit, but its developer documentation now says OpenAI is deprecating it and the product is scheduled to shut down on November 30, 2026, so we did not rank it.

Show it or chat with it: the most intuitive way to build an agent

The hard part of building an agent is not the model. It is getting the process out of the head of the person who does it every day. Prompt-first builders put that work on you: you describe each step, each exception and each system, then test until it behaves. Caddi takes two shorter routes.

• Show it. Share your screen and do the task once. Screen-watching agents see the pixels and replay the clicks. Caddi also reads the network requests behind your clicks, so it learns the API calls each app actually makes, which is what the finished agent uses instead of clicking through the screen.
• Chat with it. Describe the task in plain language. Caddi knows the work at law firms, RIAs, accounting firms and insurance agencies, so it asks about the edge cases and proposes the next steps instead of waiting for you to think of them.

It is proactive before you build, too. Caddi Discover reads the tools your team already uses and ranks the repetitive work by the hours it takes, so you do not need to know what to automate first. Most builders on this list assume you already do. Once built, the agent runs the same way every time as deterministic code, and Govern logs every run. For the bigger picture of how a show-once agent replaces a hand-built flow, see our workflow automation guide.

Which type of agent builder fits you?

Pick the type before the tool. The four questions that separate them are who builds, who maintains, what happens on an exception, and what you pay for.

Vibecoding is the quickest route to a demo, and for a developer it is often the right one. The catch for an operations team is everything after the demo: someone has to own the code, review each change and keep it running, and an agent that calls a model on every run reasons fresh each time unless you pin its steps. No-code builders lower the bar to start but still assume you can spell out the whole process. Screenshare-to-API starts from what the person already does, which is why it suits work nobody has ever written down.

How we picked

We compared each builder on six criteria, weighted toward regulated back-office work: who builds the agent (developer, ops staff or business user), time to a first working agent, the maintenance burden when an input or a system changes, the pricing model at volume, whether it reaches the systems law firms, RIAs and insurers run (Clio, NetDocuments, iManage, Salesforce, Wealthbox, Box, DocuSign), and whether every run leaves a reviewable record. Prices, plans and certifications were read from each vendor's own pages in October 2026. Where a vendor does not publish a price, we say so instead of repeating a third-party estimate. We reviewed documentation and public product pages for every tool; we did not run a benchmark. For the category as a whole, see our guide to AI agents.

Disclosure: Caddi makes one of the tools on this list. We include it where it fits and say where it does not.

AI agent builders compared

The 13 best AI agent builders in 2026

Ranked within four groups, by how you build. Caddi is first because this guide is written for operations teams; if you are a developer, start with the vibecoding group.

Screenshare-to-API builders

A screenshare-to-API builder learns the task the way a new hire does: you do it on a screen share and talk it through. It takes in three things at once (what is on screen, the network traffic behind each click, and your narration) and turns them into an agent that calls the apps' APIs. It is the best fit when the process lives in someone's routine rather than in a document.

01

1. Caddi

Best for regulated ops

Best for

Operations teams at law firms, RIAs, accounting firms and insurance agencies who want to build agents by showing or chatting

Pricing

Free Individual plan; Startup $250 per user per month; Business $6,500 per month; Enterprise custom; two-week pilot on Business and Enterprise (pricing)

Choose something else if you want a customer-facing chat or voice agent, a general assistant for every employee, or a framework your engineers code against. Copilot Studio, Agentforce or CrewAI will suit you better.

Strengths and trade-offs8 strengths · 3 trade-offs

Strengths

• Build by showing the task on a screen share or by chatting; no prompts to engineer, no canvas to draw
• Watches the network requests behind your clicks, so the agent uses the real API calls, not screen clicks
• Proactive: knows professional services work, so it suggests next steps and edge cases
• Discover finds the repetitive work worth automating in the tools you already use
• Runs the same way every time as deterministic code, with no IT project
• Govern: every run is logged, reviewable and traceable to source
• Live connectors for Clio, NetDocuments, iManage, Wealthbox, Salesforce, Box and DocuSign
• Handles varied PDFs and shared-inbox email as part of the workflow

Trade-offs

• Built for back-office work, not customer-facing chat agents
• Not a framework: developers who want to write their own agent code will find it limiting
• Not open source or self-hostable

Caddi is the agent that builds back-office agents: a narrower product than most on this list, aimed at the operations lead who owns a process but not an engineering team. The free plan includes Automate and Discover, so you can build a real agent before talking to anyone; businesses usually start with a two-week pilot on one high-value workflow. See Caddi Automate, Discover, Govern and pricing.

Record-and-replay builders

Record-and-replay is the older way to show a task: the recorder captures your clicks and keystrokes and plays them back on the screen. It is still the only option for apps with no API.

Record-and-replay keeps the clicks; screenshare-to-API keeps the intent. Caddi watches the screen too, but it also reads the network requests behind each click and listens to your narration, so the agent it builds calls the apps' APIs and runs as deterministic code instead of clicking through the interface. For a closer look, see UiPath alternatives and Power Automate alternatives.

No-code AI agent builders

No-code builders let a business user or maker assemble an agent by prompting a copilot, filling in a form or dragging nodes on a canvas. They are strongest inside the platform you already pay for.

03

3. Microsoft Copilot Studio

Best for

Firms standardised on Microsoft 365 that want agents in Teams and Outlook

Pricing

$200 per month per pack of 25,000 Copilot Credits, or pay-as-you-go; internal agents included with the $30 per user per month Microsoft 365 Copilot license (Microsoft pricing)

Choose Microsoft Copilot Studio if your firm lives in Microsoft 365 and you have makers or an IT team who can own agents inside Teams, Outlook and SharePoint.

Strengths and trade-offs3 strengths · 2 trade-offs

Strengths

• Build agents in natural language or a low-code designer
• More than 1,400 external connectors plus MCP servers, per Microsoft
• Multi-agent orchestration and autonomous agents for business processes

Trade-offs

• Credits are consumed per action or response, which takes modelling at volume
• Someone still designs, tests and maintains each agent

Caddi vs. Microsoft 365 CopilotMicrosoft Copilot alternativesMicrosoft 365 integration

Microsoft positions Copilot Studio as the place to create, customize, and launch AI agents across Microsoft 365 and connected systems. For a comparison with Caddi on the Microsoft side, see Microsoft Copilot alternatives.

04

4. Salesforce Agentforce

Best for

Agents that act on Salesforce CRM and Data 360 records

Pricing

Flex Credits $500 per 100,000 (a standard action is 20 credits); $2 per conversation; add-ons $125 per user per month; Agentforce 1 Editions from $550 per user per month (Salesforce pricing)

Choose Salesforce Agentforce if Salesforce is your system of record and you have admins who already build flows and Apex there.

Strengths and trade-offs3 strengths · 2 trade-offs

Strengths

• Low-code Agent Builder plus Agent Script for precise control
• Grounded in Salesforce CRM data and Data 360
• Builder included at no charge in Salesforce Foundations

Trade-offs

• Strongest when the work lives in Salesforce
• Admins or developers configure subagents, instructions and actions

Caddi vs. AgentforceAgentforce alternativesSalesforce integration

Google splits the category in two. The Gemini Enterprise app includes Workflow Builder, which Google describes as a no-code, low-code platform for chat agents and workflows. Developers use Gemini Enterprise Agent Platform, the renamed Vertex AI, where new customers get up to $300 in free credits.

Zapier describes Agents as AI teammates you equip with company knowledge. More on the core product in Caddi vs. Zapier and Zapier alternatives.

08

8. Lindy

Best for

A personal AI assistant for email, meetings and scheduling that you can show a task once

Pricing

Plus $29.99, Pro $99.99, Max $199.99 per user per month; Enterprise custom; 7-day trial (Lindy pricing)

Choose Lindy if the work you want off your plate is your own email, meeting notes and scheduling.

Strengths and trade-offs3 strengths · 2 trade-offs

Strengths

• Works in Gmail, Slack and iMessage and joins Meet, Zoom and Teams calls
• Show Lindy a task once and it is saved for the team
• 1,000+ integrations plus MCP servers, per the vendor

Trade-offs

• Assistant-shaped: strongest for one person's inbox and calendar
• Credits are pooled and pause the assistant when they run out

09

9. Gumloop

Best for

Letting anyone at the company build agents while IT controls access

Pricing

Pro from $37 per month with 20,000 credits; Enterprise custom; 14-day trial, no permanent free tier (Gumloop pricing)

Choose Gumloop if you want a company-wide agent canvas for marketing, sales and support tasks, with IT in control of access.

Strengths and trade-offs3 strengths · 2 trade-offs

Strengths

• Describe work in plain language and get an agent that runs on triggers
• Model-agnostic, with enterprise controls including SSO, audit logging and VPC deployments
• Agents can produce documents, dashboards and web apps

Trade-offs

• No depth in legal or wealth-management systems
• Each team still owns its agents after they are built

Caddi vs. GumloopGumloop alternatives

10

10. n8n

Best for

Technical teams that want fixed workflow steps and AI agents on one canvas

Pricing

Self-hosted Community Edition; Cloud Starter 20 EUR per month billed annually (2,500 executions), Pro 50 EUR; Enterprise custom (n8n pricing)

Choose n8n if you have engineers who want to control the model, the prompts and the hosting, and to combine agents with deterministic workflow steps.

Strengths and trade-offs3 strengths · 2 trade-offs

Strengths

• AI Agent node works with any LLM or vector store you choose
• Human-in-the-loop approval steps and fallback logic
• Self-host for full control over where it runs

Trade-offs

• Developer-oriented: code nodes, expressions and JSON handling
• Self-hosting puts servers, updates and monitoring on your team

Caddi vs. n8nn8n vs Zapiern8n vs Maken8n alternatives

n8n bills per workflow execution rather than per step, which makes long agent runs easier to price. See n8n's AI agents page, and our n8n alternatives guide if you are weighing a hosted option.

Vibecoding and code-first agent builders

Vibecoding means describing the agent in plain language and letting an AI write it as code. Code-first frameworks sit underneath. Both give developers full control, and both leave the code with you.

11

11. Claude Code

Best for

Developers who want an AI to write, run and fix an agent's code from a plain-language description

Pricing

Included with paid Claude plans, or billed through the Anthropic API; see Anthropic pricing

Choose Claude Code if you have developers who want to vibecode a custom agent and own the code that results.

Strengths and trade-offs3 strengths · 3 trade-offs

Strengths

• Describe the agent and Claude Code writes the code, runs it and iterates in your terminal or IDE
• Works in your own repository, with your tools, tests and hosting
• Fast from idea to working prototype for a developer

Trade-offs

• The output is code: someone has to review, test, host and maintain it
• Connecting to legal, wealth or practice management systems is your integration work
• Not built for an ops person to build or own an agent alone

Caddi vs. Claude

Anthropic describes Claude Code as an agentic coding tool. It is a strong way to build an agent if you are a developer; it is not an agent builder for the operations team that will live with the result.

See the Agents SDK documentation for what the framework covers.

CrewAI's framework is on GitHub under the MIT license; the commercial platform adds deployment and governance.

Deterministic workflows vs. agentic runtime

The biggest design difference between these builders is what happens on the hundredth run, not the first. There are two broad approaches, and most products let you mix them.

Agentic runtime. The model decides the next step each time the agent runs: it reads the input, picks a tool, looks at the result and plans again. This is what makes agents good at open-ended work like research, triage or answering questions, and it is the default in Copilot Studio, Agentforce, Lindy and most canvases. The trade-off is that two runs on similar inputs can take different paths, so you test with evaluations and add guardrails and approval steps.

Deterministic workflow. The steps are fixed in advance and run the same way every time; a model may be called for one bounded step, such as reading a field from a PDF. n8n, Zapier and Copilot Studio flows all support this pattern if you design for it. It is less flexible and more predictable, which is usually what a back-office process with an audit requirement wants.

Caddi uses AI to build the workflow and runs it as deterministic code. AI builds it, verified code runs it, and any model step inside a run is scoped, logged, and reviewable. We have written about why long multi-step agent runs drift in why AI agents fail on long-running tasks, and about the build-verify-run loop in loop engineering.

For law firms, RIAs and insurance agencies

Generic lists rank agent builders by connector count. For a regulated back office the questions are narrower: does the agent reach the systems the work lives in, who keeps it running when a template changes, and can you show a reviewer exactly what it did? Before you pick a horizontal builder, check its directory for the systems below; several are specialist tools that general catalogues do not cover.

Law firms. The repetitive work sits between the inbox, the document management system and the practice management system: an engagement letter comes back signed through DocuSign, the matter is opened in Clio, and the documents are filed to NetDocuments or iManage. See AI automation for law firms.

RIAs. Account opening and client service follow the same shape: custodian paperwork is checked, the household in Wealthbox or Salesforce is updated, and portfolio data from Orion or Addepar is pulled into a review. See AI automation for RIAs.

Insurance agencies. Submissions and renewals arrive as PDFs and email attachments that have to be read, checked against the account and filed, often to Box or SharePoint. See AI automation for insurance.

In all three, the agent is only as useful as the person who can keep it running. If that person is an operations lead rather than an engineer, weight the “who builds” column above more heavily than the connector count.

How to choose

• Who builds it: a developer, a business user, or nobody on your side?
• How you build: prompts, a canvas, code, or showing the task? The closer it is to how the work is already done, the less gets lost.
• Who decides what to build: do you need a list of use cases up front, or does the builder find the work for you?
• Runtime: does each run re-plan, or run the same steps every time?
• Systems: does it reach your DMS, practice management system or CRM, not just email and Slack?
• Meter: credits, actions or seats, and what happens at ten times today's volume?
• Audit: can you show what ran, when, on what data, and who approved it?

Start from a list of the work, not the builder. If you do not have one, Caddi Discover reads the tools your team already uses, read only, and ranks the repetitive work by the hours it takes.

Related guides: Agentforce alternatives, Gumloop alternatives, Caddi vs. Microsoft 365 Copilot, Caddi vs. Agentforce, ServiceNow alternatives and AI agents for operations.