9 Best AI Workflow Automation Tools in 2026

The best AI workflow automation tool depends on what you’re building and who will maintain it. Business teams may need a visual builder that connects their apps, while developers may need custom logic and control over deployment.

This guide compares nine tools for building AI workflows, covering their strengths, pricing, and tradeoffs so you can narrow down the right fit.

Updated on Sept 24, 2026: removed Relay as they shut down, updated pricing and features for all the tools listed and clarified how to choose the best AI workflow automation tool at the bottom

9 Best AI workflow automation tools at a glance

1. Mastra: best for owning AI workflow automation in your company

Mastra is an open-source TypeScript framework for building AI agents and workflows. It is the most developer-native tool on this list.

Where Zapier, Make, and Gumloop are platforms for assembling automations, Mastra is infrastructure for building AI workflow automation for your customers or internal systems.

If you are building a research agent, support triage system, or document-processing pipeline for mission critical work, you probably do not want the whole system trapped in a third-party vendor. You want code, version control, deployment options on your preferred runtime, tracing, the ability to run experiments and record evals, and clear separation between workflows, environments, and memory.

Memory is especially important for AI workflow automation because agent workflows generate long histories that are difficult for agents to manage. If the system keeps all of that raw context, the agent gets slower, more expensive, and more likely to drift. Mastra's Observational Memory compresses workflow history into a dense log, so the workflow can remember what matters and stay on track.

Mastra also offers Mastra Factory, an open-source environment that applies these building blocks to software delivery. It connects coding agents to an existing GitHub repository, taking work from issue intake and triage through planning, implementation, and pull request review.

Factory keeps people involved at defined checkpoints. Teams can answer the agent's questions, approve or edit plans, and decide whether to merge changes.

Mastra is best for teams that want to own their AI workflow automations. The framework provides the building blocks for custom agents and workflows, while Factory gives teams a starting point for automating their software development process.

Pricing: Mastra's open-source framework can be self-hosted. Mastra Platform has a starter plan at $0/month, a teams plan at $250/month, and custom enterprise pricing.

2. Gumloop: best for business teams building shared AI agents

Gumloop is a no-code platform for building AI agents that use company knowledge and connected business apps. Teams can share agents and reusable skills, then run them from chat, recurring schedules, or app events.

Gumloop's current product centers on agents, while its pricing page labels visual workflows as legacy. Agents can work across tools such as Slack, Gmail, and Microsoft Teams. Teams can evaluate their outputs, track activity and spending, and set permissions for who can edit or use them.

Gumloop is best for business teams that want colleagues to reuse agents with shared company context. Enterprise options add organization-wide policies and private cloud deployment.

Pricing: Gumloop offers a 14-day free trial. Pro starts at $37/month with 20,000 credits/month and unlimited agents, seats, and teams. Usage reflects model and compute costs with an 8% orchestration fee. Enterprise has custom pricing.

3. n8n: best for technical teams that want visual workflows plus code

n8n is a fair-code, source-available workflow automation platform that combines a visual canvas with custom JavaScript and Python. Teams can connect app integrations, API calls, and AI steps, then deploy on n8n Cloud or their own infrastructure.

n8n supports inspecting inputs and outputs, rerunning individual steps, and evaluating AI behavior. Paid governance features include access controls, separate environments, and Git integration, with availability depending on the plan.

n8n is best for technical teams that want to maintain visual workflows while adding code where needed. Its execution-based billing can suit workflows with many steps, but the total cost depends on how often they run and any external model usage.

Pricing: n8n Cloud Starter is US$20/month for 2,500 workflow executions, and Pro starts at US$50/month for 10,000 executions, both billed annually. Paid plans include unlimited users and workflows. A free self-hosted Community Edition is also available, with paid plans for additional collaboration and governance features.

4. Zapier: best for broad app coverage and business-user automation

Zapier connects more than 9,000 apps through workflows called Zaps. A trigger starts the workflow, and actions perform the work. Its platform also includes Tables, Forms, and MCP support, while Zapier Agents is a separate add-on.

Zapier is best for business teams whose processes span common SaaS tools. Enterprise plans include observability, audit logs, and controls over app access and actions. Task usage varies by action type, with different rates for AI, code, and other services, so estimating costs requires checking the steps a workflow will run.

Pricing: Zapier offers a Free platform plan with 100 tasks/month. Professional starts at $19.99/month billed annually. Agents Free includes 400 activities/month, while Agents Pro includes 1,500 activities/month for $33.33/month billed annually. Agent activities are a separate allowance from platform tasks.

5. Make: best for visual AI workflow design

Make is a visual automation platform for building scenarios across more than 3,000 apps. Its canvas shows how data moves through branches, filters, routers, and transformations, making it useful for processes with several possible paths.

Make AI Agents, listed as beta on the pricing page, adds agents that can choose tools and take actions within those workflows. Teams can inspect agent decisions in the canvas, add approvals, and reuse agents across scenarios. Make also lets users build automations and agents through conversation.

Make is best for operations teams and agencies that need to combine fixed business rules with AI decisions. For example, a scenario could classify an incoming request with an agent, then use explicit routing rules to assign it and update the relevant records.

Pricing: Make has a Free plan with 1,000 credits/month. At the 10,000-credit monthly allowance, Core starts at $9/month and Pro at $16/month when billed annually. AI and code features can consume credits at different rates from standard app actions.

6. Pipedream: best for developers integrating APIs

Pipedream Workflows lets developers combine app triggers and prebuilt actions with custom Node.js, Python, Go, or Bash code. Pipedream hosts the workflows and provides logs, errors, and timing details for each step.

Pipedream Connect addresses a related need: adding integrations to an agent or application you build yourself. It manages user authentication, token refresh, and API access, with prebuilt tools available through an SDK or MCP. This lets an agent act through each user's connected accounts.

Pipedream is best for developers whose main challenge is connecting services. Workflows suits hosted API automation and webhooks, while Connect suits teams that already own the agent logic and need integrations for their users.

Pricing: Pipedream offers up to 100 credits/month and three active workflows on its Free plan. Basic starts at $29/month and Advanced at $49/month, each with 2,000 credits/month, billed annually. Production use of Connect starts at $99/month billed annually. Workflow credits depend on compute time, memory, and execution segments, with minimum charges per segment.

7. Activepieces: best for business automation with a choice of hosting

Activepieces combines workflow automation with AI agents and shared data tables. Teams can connect their business apps, build flows from triggers and actions, or describe a task in chat to create an automation. It supports cloud and self-hosted deployment.

Flows and agents can work together in Activepieces. A flow can call an agent when a step needs reasoning, and an agent can run an existing flow for a defined process. Both can read and update the same tables. This makes it useful for processes that combine AI decisions with repeatable steps, such as classifying an incoming request and routing it to the right team.

Activepieces is best for teams that want business users to build automations while IT manages hosting and access. Teams considering self-hosting should account for edition differences: the free Community Edition provides the automation core, while Agents, Chat, and advanced governance require paid capabilities.

Pricing: Activepieces has a Free plan with 100 credits/day. Billed monthly, Plus is $20/month with 10,000 credits and Team is $200/month with 50,000 credits. Overage costs $0.007/credit on both paid plans. A flow run uses one credit, with AI steps and agent actions consuming additional credits. Ultimate has custom pricing.

8. Dify: best for building AI applications visually

Dify is a platform for building AI applications on a visual canvas. Its Workflow Studio connects model calls, knowledge retrieval, tools, and custom code with branches and human review steps. Workflows can start from a schedule, webhook, or event in a connected service.

Dify is useful when the work depends on documents and model reasoning. For example, a document review workflow could retrieve a policy, use a model to check a submission, and pause for a person to approve or edit the result. Teams can inspect node outputs and execution logs, restore published versions, and send traces to external observability tools.

Dify is best for product and development teams that want to build AI applications visually, including assistants that answer questions from company documents. Workflows can be published as an API, hosted app, or MCP tool, so the same logic can support users directly or run behind another application.

Pricing: Dify Cloud has a free Sandbox plan with 200 message credits. Professional is $59/workspace/month with 5,000 message credits/month, and Team is $159/workspace/month with 10,000, when billed monthly. Teams can switch to their own provider API keys after using the included credits. A free self-hosted Community Edition is available under Dify's license, with custom Enterprise pricing.

9. Relevance AI: best for coordinating teams of AI agents

Relevance AI is a platform for building and running specialized AI agents across business processes. Teams can connect agents, tools, and approval steps on a visual canvas, with handoffs between agents and branches for different outcomes.

Relevance AI combines agents with predefined actions and loops. Runs can start from schedules, app events, or webhooks, with approval rules and pauses when agents reach cost or autonomy limits. Teams can test agents against examples before publishing and monitor samples of their work in production.

Relevance AI is best for enterprise teams that want several agents to handle different parts of a recurring process using shared business knowledge. For example, a sales process could use one agent to research an account, another to prepare a meeting brief, and a third to draft follow-up material for review.

Pricing: Relevance AI lists custom Enterprise pricing with negotiated action and vendor credit allowances. The published plan includes unlimited agents, tools, users, and projects, plus evaluations, SSO, role-based access controls, and audit logs. Contact sales for a quote.

How to choose the best AI workflow automation tool

Choose around a real process, the team responsible for it, and the controls it needs.

Define the workflow

Map the trigger, required inputs, actions, and expected result. Identify which steps follow fixed rules, which need AI judgment, and which need human approval. A support workflow might use AI to classify a message, then apply explicit rules to assign it to a team.

Decide who will build and maintain it

Match the tool to the people who will update workflows and resolve failures. Make and Gumloop suit business users assembling automations or agents. n8n combines visual workflows with code for technical teams. Mastra suits developers building custom agents and workflows into applications, while Pipedream helps connect those applications to external services.

Check integrations and data control

Verify the specific triggers and actions your process needs, including custom API calls and account permissions. Check where data is stored and which model providers receive it. If you need self-hosting, compare the available editions and account for who will operate the infrastructure.

Evaluate testing and oversight

Test incomplete inputs, incorrect model responses, and failed API calls. Confirm that you can inspect what happened, recover from failures, and pause for approval before consequential actions. For recurring AI decisions, keep a set of representative examples to measure quality when models or instructions change.

Compare total cost with a real pilot

Estimate costs at normal and peak volume, including platform fees, model usage, hosting, and maintenance. Pay attention to whether billing counts tasks, credits, compute, or entire workflow runs. Pilot one representative process with real data and measure cost per completed task, manual corrections, and time spent maintaining it. Use those results to decide whether the tool fits your budget and quality requirements.