Quick overview

The top 12 Gumloop alternatives in 2025 range from no-code automators like Zapier and Make to best in class platforms like Vellum. While Gumloop excels at rapid prototyping and PDF parsing, teams scaling AI agents for enterprise use are shifting toward solutions that offer rigorous regression testing, version control, and lower latency [1].

Top 6 shortlist of Gumloop alternatives

1. Vellum: The quickest way for any team to turn ideas into powerful AI agents that handle real work from day one.
2. Zapier: Best for connecting standard business apps with simple AI logic.
3. Make: Best for complex, branching workflows without writing code.
4. LangSmith: Best for technical engineering teams needing deep observability.
5. Retool AI: Best for building internal tools and front-end interfaces quickly.
6. Relevance AI: Best for deploying autonomous AI agents with minimal setup.

Last week a founder I know sent me a screenshot of a Slack thread at 1:17 a.m. Their “perfect” no-code workflow had pulled the wrong clause from a contract, hallucinated a renewal date, and the AE sent it straight to a customer. The customer caught the error before signing and pulled the deal. When I asked what went wrong, the founder said, “We just shipped it after a few manual tests. We had no way to lock the behavior or run it against past deals.”

That conversation made the problem painfully clear. In 2025, spinning up AI workflows is easy. For 2026, trusting them once real revenue is on the line is not. That is why we need to look at tools that do more than draw boxes and actually help teams test, control, and improve how these workflows behave.

What is AI Workflow Automation?

AI workflow automation is using LLMs plus your data, tools, and APIs to get multi step work done with little or no human involvement. Instead of a person moving data between tools or copying text, an AI workflow takes inputs, makes decisions, and triggers actions for you.

Why use dedicated AI automation platforms?

Dedicated AI automation platforms turn scattered experiments into shared, reusable workflows and AI Apps that real teams can rely on.

• Accelerate Time-to-Market: Go from idea to working AI workflows and AI Apps in days instead of months by giving teams natural language, visual builders, and templates to start from.
• Reduce Engineering Overhead: Let product, ops, sales, and support teams update prompts, workflows, and guardrails themselves, while engineers keep control through APIs, SDKs, and versioning.
• Ensure Reliability: Use evaluations, test suites, and observability to validate changes against real or historical data before you roll them out across the org.
• Cost Optimization: Companies using optimized orchestration layers see up to 40% reduction in token costs [2].

Why Use Gumloop Alternatives?

Gumloop is great for getting a first AI workflow up and running, but teams will eventually hit limits as soon as they try to scale beyond simple use cases. The push to explore alternatives usually comes from three places:

• Cost vs. capability concerns
• Gumloop’s pricing can feel steep when you’re just building straightforward automations that burn through tokens quickly. Many teams realize they’re paying enterprise rates for workflows that are still basic or experimental.
• A smaller ecosystem
• Because Gumloop is newer, there are fewer templates, examples, community solutions, and third-party guides to lean on. This makes it harder to troubleshoot issues or adopt best practices compared to platforms with broader ecosystems.
• A low ceiling for complexity
• Gumloop handles linear, simple AI steps well, but the moment you need branching logic, reusable components, rigorous testing, or advanced integrations, the platform starts to get in the way. Power users often move to tools like n8n or Vellum once they need deeper control or cross-team collaboration.

For most teams, this is the point where moving to a more flexible, scalable alternative becomes necessary to reach true AI nativity and ROI.

Who Needs AI Automation Tools?

• Product managers: To experiment with AI behavior, refine workflows, and ship updates without engineering bottlenecks.
• AI and engineering teams: To manage prompts, models, evaluations, and versions in one place while collaborating with non-technical teammates.
• Operations teams: To automate repetitive processes like data entry, QA checks, support routing, and back-office workflows.
• Sales teams: To automate research, lead enrichment, proposal drafting, and follow-up workflows that normally require hours of manual effort.
• Marketing teams: To streamline content operations, audience research, campaign workflows, and data-driven personalization.
• Customer support teams: To power AI-assisted ticket triage, response drafting, and knowledge retrieval across channels.
• Data and analytics teams: To operationalize data pipelines by wrapping them in reusable AI workflows other teams can trigger.
• Revenue operations and business operations: To centralize processes, reduce manual workflows, and enforce consistent logic across teams.
• Enterprise CTOs and platform leaders: To set standards for security, governance, and model usage while enabling every team to build safely and consistently.

What Makes an Ideal Alternative?

• Workflow Consistency: A platform should run your workflows predictably across a wide range of inputs so teams can trust the outcomes.
• Evaluation Capabilities: Tools to compare outputs, catch regressions, and validate changes before sharing workflows across a team.
• Flexible Model Choice: The freedom to use OpenAI, Anthropic, Gemini, or open-source models and switch as needs evolve.
• Collaborative Builder Experience: Visual editors, APIs, and versioning that support both non-technical teammates and engineers working in parallel.
• Security & Governance: Strong data handling practices, permissioning, and compliance standards for organizations that need control and clarity.

Key Trends Shaping AI Automation

• Shift from Chat to Agents: The industry is moving beyond simple chatbots; AI agent adoption for autonomous tasks increased by 340% in 2024 [3].
• Rise of Small Language Models (SLMs): Enterprises are increasingly adopting smaller, specialized models to reduce latency and cost, with 45% of leaders prioritizing model efficiency over raw power [4].
• Demand for Evaluation Frameworks: As prototypes fail in production, the demand for rigorous "LLM Evals" has grown, with 60% of engineering teams now citing testing as their top bottleneck [5].

How to Evaluate Gumloop Alternatives

Choosing the right Gumloop alternative is about real differentiators as capabilities that help teams collaborate, adapt quickly, and maintain confidence as workflows evolve.

The Top 12 Gumloop Alternatives in 2025

1. Vellum AI — The best way to build AI agents

Vellum AI is the fastest way for teams to turn ideas into working AI agents that automate real work across the business. Anyone can describe what they want in plain language, let Vellum generate the workflow, then refine behavior with built in evaluations, versioning, and observability as things scale.

Best For: Teams that want to build, share, and iterate on AI agents and AI Apps quickly across functions with no engineering lift.

Pros:

• Natural Language Agent Builder: Build simple and complex workflows instantly by describing them in plain English.
• AI Apps: Every workflow you build in Vellum can instantly turn into an AI App making it easy for cross-functional teams to run workflows, submit inputs, and collaborate without needing to touch the underlying logic.
• Built-in Evaluations: Run quantitative tests on prompt changes to ensure reliability before deploying.
• End-to-End Observability: Monitor latency, cost, and quality in a single dashboard.
• Model Agnostic: Swap between OpenAI, Anthropic, Gemini, and open-source models with one click.

Cons:

• Geared towards teams building business value rather than casual hobbyists.

Pricing: Free tier available; paid plans starting at $25/mo; enterprise pricing available

2. n8n — Developer Focused Workflow Automation

n8n combines the ease of node-based editing with the power of raw JavaScript. It is a strong alternative for developers who find Gumloop too restrictive regarding custom code execution.

Best For: Technical teams requiring self-hosted data privacy and flexibility.

Pros:

• Self-hostable for maximum data privacy and security.
• Fair, node-based pricing model (execution based).
• Extensive library of 1,000+ integrations.
• Allows execution of custom JavaScript and Python.

Cons:

• Requires server maintenance and setup knowledge (if self-hosting).
• Steeper learning curve for non-technical users.

Pricing: Free (Self-hosted); Cloud starts at $20/month; Enterprise pricing available

3. Make — Visual Automation for SaaS

Make offers a highly visual "scenario" builder that handles branching logic better than most linear automation tools. It is excellent for connecting established apps but less specialized for LLM orchestration than Vellum.

Best For: Complex logic and multi-step integrations between standard SaaS apps.

Pros:

• Intuitive drag-and-drop visual builder.
• Granular control over data mapping and transformation.
• Cheaper than Zapier for high-volume tasks.

Cons:

• Building AI agents requires complex "spaghetti" logic.
• Error handling can be difficult to debug in large flows.

Pricing: Free tier available; core plan starts at $9/month; Enterprise pricing available

4. Stack AI — Enterprise AI Workflow Builder

Stack AI focuses heavily on the "LLM Ops" side of no-code, allowing users to connect vector databases and models easily. It mirrors Gumloop's interface but targets larger organizations.

Best For: Enterprise teams needing a direct Gumloop competitor with SOC2 compliance.

Pros:

• Visual interface specifically designed for LLM chains.
• Native integrations with vector databases (Pinecone, Weaviate).
• Enterprise-grade security features (SOC2, HIPAA).

Cons:

• Pricing scales aggressively compared to general automation tools.
• Limited debugging tools compared to engineering-focused platforms.

Pricing: Free tier available; Enterprise pricing available

5. Zapier — The Universal Connector

Zapier remains the industry standard for connectivity. While its AI features are basic compared to Vellum, its "Central" and "Canvas" features are attempting to bridge the gap for simple agentic behaviors.

Best For: Simple, linear automations connecting disparate apps without code.

Pros:

• Largest ecosystem of app integrations (6,000+).
• "Zapier Central" allows for basic AI behaviors on top of data.
• Extremely easy to start for non-technical users.

Cons:

• Becomes very expensive at enterprise scale.
• Lacks deep evaluation or prompt engineering tools.

Pricing: Free tier available; paid plans start at $29.99/month; Enterprise pricing available

6. Relevance AI — Multi-Agent Orchestration

Relevance AI specializes in "AI Agents" that run in loops. It is a strong alternative for users who want pre-built agent templates rather than building custom workflows from scratch.

Best For: Teams building autonomous AI workforces for sales and research.

Pros:

• Focus on autonomous loops and multi-agent collaboration.
• "BDR" and "Researcher" agent templates included.
• Visual builder for agent tools.

Cons:

• Can be difficult to control agent hallucinations.
• Less flexible for general business logic than Make or n8n.

Pricing: Free tier available; paid plans start at $19/month; Enterprise pricing available

7. LangSmith — LLM Engineering & Tracing

LangSmith is a Langchain product and developer-first tool. It is not a no-code builder like Gumloop but is essential for teams that code their agents and need to understand why an LLM failed.

Best For: Developers needing deep debugging and tracing for LangChain applications.

Pros:

• Best-in-class tracing for complex LLM calls.
• Deep integration with the LangChain ecosystem.
• Detailed cost and latency tracking.

Cons:

• Requires coding knowledge (Python/TypeScript).
• Not a workflow builder; it is an observability platform.

Pricing: Free tier available; Paid plans starting at $39/mo per use; Enterprise pricing available

8. Retool AI — Internal Tooling & UI Builder

Retool combines AI workflows with a drag-and-drop UI builder. It is the best choice if your AI agent needs a human-facing dashboard or admin panel.

Best For: Building front-end interfaces that trigger AI workflows.

Pros:

• Builds actual UIs (dashboards, forms) alongside workflows.
• Retool Vectors makes RAG (Retrieval-Augmented Generation) easy.
• Secure connection to internal databases (Postgres, Snowflake).

Cons:

• Not designed for background, event-driven automation.
• Pricing is per-user, which gets costly for large teams.

Pricing: Free tier available; Paid plans starting at $10/user/month.

9. Clay — AI Data Enrichment

Clay is a specialized spreadsheet that acts like a workflow tool. It excels at "waterfalling" data providers and using AI to write personalized emails based on that data.

Best For: Sales and GTM teams automating outreach and lead scoring.

Pros:

• Superior data enrichment capabilities (50+ providers).
• Spreadsheet interface is intuitive for data teams.
• "Claygent" allows for web scraping and research tasks.

Cons:

• Very specific to sales/marketing use cases.
• High starting price compared to general tools.

Pricing: Free trial available; Paid plans starting at $134/mo; Enterprise pricing avaible

10. Microsoft Power Automate — Corporate Automation

Microsoft Power Automate is for organizations already paying for Microsoft 365, Power Automate offers "Copilot" features to build flows. It is clunky but compliant and free for many corporate users.

Best For: Enterprises deeply embedded in the Microsoft 365 ecosystem.

Pros:

• Deep integration with Excel, SharePoint, and Teams.
• Enterprise governance and access controls built-in.
• "Process Mining" to identify automation opportunities.

Cons:

• Steep learning curve and dated user interface.
• Debugging AI flows is difficult.

Pricing: Often included in O365; Premium starts at ~$15/user/month.

11. Lindy AI — The all around AI builder

Lindy is positioned to build AI agents and vibe-coded apps. It frames its agents as "employees" (e.g., Medical Scribe, Recruiter). It is less of a builder and more of a platform of pre-configured personas.

Best For: Individuals needing an out-of-the-box AI personal assistant.

Pros:

• Fastest setup for standard roles (Calendar management, email).
• Voice capabilities for conversational interaction.
• Simple "instruction" based setup.

Cons:

• Black-box nature limits customization.
• Difficult to integrate into complex custom software stacks.

Pricing: Free tier available; paid plans starting at $39.99/mo; enterprise pricing avaible

12. Flowise — Open Source Visual Builder

Flowise is an open-source drag-and-drop tool specifically for building LLM apps. It is a strong alternative for those who like Gumloop's interface but want to run it locally or on their own infrastructure.

Best For: Developers wanting a free, visual way to build LangChain flows.

Pros:

• Completely open-source and free to self-host.
• Visualizes LangChain components (chains, agents, prompts).
• Active community and frequent updates.

Cons:

• Requires technical knowledge to deploy and maintain.
• UI is less polished than commercial SaaS tools.

Pricing: Free tier available; paid plans starting from $35/mo.

Top 12 Gumloop Alternatives in 2025 Comparison Table

Why Vellum is the Best Gumloop Alternative

Where Gumloop is great for getting a first workflow live, Vellum is built for teams that want AI agents to actually run real work across the business. It takes the core pain points you feel with Gumloop

(complexity ceiling, limited testing, harder collaboration) and removes them.

• Idea to agent in minutes: With Vellum, you do not start from a blank canvas. You describe the workflow or agent you want in plain language, and Vellum generates the full agent for you: steps, logic, tools, and document handling. This lets you go from idea to working AI agent in minutes instead of spending hours wiring nodes.
• AI Apps for instant sharing: Every workflow you build in Vellum can instantly become an AI App that anyone on your team can use. Product, ops, sales, and support can run the same agent through a clean interface, submit inputs, and collaborate without touching the underlying logic. This is a big step up from Gumloop when you want more than one power user owning the flow.
• Visual builder plus real SDKs: Non-technical teammates get a simple visual builder to see and adjust how the agent works. Engineers get a TypeScript and Python SDK to extend that same workflow in code when they need deeper control. The result is one shared system instead of separate no-code prototypes and shadow engineering projects.
• Built in evaluations and versioning: Vellum has testing built in, so you can compare prompt changes, catch regressions, and roll out updates safely. You can keep multiple versions of an agent, run them against real or historical data, and only promote the one that actually performs better. This directly addresses the “we shipped after a few manual tests” problem that tools like Gumloop leave you with.
• End to end observability: Every run is traceable. You can see inputs, outputs, intermediate steps, latency, and cost in one place. When something goes wrong or performance drifts, you have the context to fix it instead of guessing which node broke.
• Governance, security, and deployment options: Vellum supports role based access, audit trails, and modern compliance needs such as SOC 2 and GDPR. You can run it in the cloud, in a private VPC, or on your own infrastructure so it fits your security posture instead of fighting it.

Who Vellum is best fit for

• Product managers: Shaping AI behavior, testing variants, and shipping new agents without waiting on engineering sprints.
• AI and engineering teams: Centralizing prompts, models, evaluations, and versions in one place while still having real SDKs and APIs when they need to go deeper.
• Operations teams: Turning repetitive work like QA checks, ticket routing, data entry, and approvals into shared AI agents and AI Apps that anyone on the team can run.
• Sales teams: Automating research, enrichment, and follow up workflows so AEs and SDRs spend more time talking to customers and less time in tabs.
• Marketing teams: Powering content operations, campaign workflows, and audience research with reusable AI agents instead of one off prompts in separate tools.
• Customer support teams: Running triage, suggested replies, and knowledge lookups through agents that can be tested, monitored, and improved over time.
• Data and analytics teams: Wrapping data pipelines and queries in AI Apps so the rest of the business can self serve insights without touching SQL.
• Revenue operations and business operations: Standardizing key processes in Vellum so logic, guardrails, and routing rules live in one place and are easy to update.
• Enterprise CTOs and platform leaders: Giving every team a safe, governed way to build and run AI agents while keeping control over models, data, and security.

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Extra Resources

• Top n8n alternatives →
• Top Langchain alternatives →
• 2025 Guide to AI Agent Workflows →
• Beginners Guide to Building AI Agents →
• Ultimate LLM Agent Build Guide →

FAQs

1. What is Gumloop actually good at?

Gumloop is good for quickly spinning up simple AI workflows and PDF parsing without much setup. If you need a fast proof of concept or a one-off internal tool, it gets you there quickly. The friction starts when more people rely on those workflows or you try to layer on real complexity.

2. Where does Gumloop start to break down as teams scale?

Gumloop tends to struggle when you need branching logic, reusable components, and rigorous testing across many inputs. Its smaller ecosystem and limited evaluation tooling make it harder to keep behavior consistent as more teams and use cases pile on. That is usually the moment teams start looking at alternatives like Vellum or n8n.

3. How do I know if it is time to move from Gumloop to Vellum?

You are ready to move when your workflows stop feeling like experiments and start touching real revenue or customer experience. If you are worried about regressions, need proper evaluations, or have multiple teams asking to use the same agents, Vellum gives you testing, versioning, and AI Apps so those workflows can be shared and improved safely.

4. How does Vellum compare to Gumloop for building AI agents?

Gumloop gives you a node editor; Vellum gives you a natural language agent builder plus a visual editor and SDKs. In Vellum you describe the agent you want, get a working workflow in minutes, then refine it with evaluations, observability, and model controls. It is built for teams that want AI agents to handle real work, not just live in a demo.

5. Can non technical teams use Vellum instead of Gumloop?

Yes. Non technical users can ask Vellum to create agents in plain language and then run them through AI Apps with a clean interface. Engineering can still go deeper with TypeScript or Python when needed, but day to day ownership can sit with product, ops, sales, or support.

6. Which Gumloop alternative is best if I need self hosted control?

If you prioritize self hosting and custom code, n8n and Flowise are strong options. n8n gives you deep integration coverage and JavaScript or Python nodes, while Flowise focuses on visual LLM flows you can run on your own infra. Both require more technical setup than Gumloop or Vellum.

7. How does Vellum help prevent the kind of “hallucinated contract” incident described earlier?

Vellum lets you create evaluations and test suites around your agents so you can replay them against past deals, tickets, or documents before anyone in sales or CS uses them live. You can compare versions side by side and only promote changes that actually improve quality. Combined with observability, this makes it much harder for a bad prompt tweak to slip into a critical workflow unnoticed.

8. Do I still need tools like Zapier or Make if I use Vellum?

Often, they can coexist. Zapier or Make are great at pure SaaS plumbing, while Vellum is better at the “thinking” part of the workflow where AI needs context, decisions, and evaluations. A common pattern is using Vellum to power the AI agent and Zapier/Make to move data between systems before and after the agent runs.

9. What is the best Gumloop alternative for debugging complex LLM behavior?

For teams that are coding their own agents, LangSmith is the best fit because it gives deep tracing, step level inspection, and tight integration with LangChain. For teams mixing no code and low code, Vellum combines observability, logs, and evaluations in a way non engineers can understand while still giving engineers the detail they need to debug.

10. Can Vellum sit on top of my existing data stack?

Yes. Vellum is designed to plug into your existing data warehouses, APIs, and knowledge sources rather than replace them. You can use it to wrap your data pipelines and services in agents and AI Apps so the rest of the business can trigger work without touching SQL or internal APIs directly.

11. How hard is it to migrate workflows from Gumloop to Vellum?

Most teams start by moving their highest impact workflows first instead of doing a big bang migration. You can recreate the core steps in Vellum with natural language, then plug in the same APIs, documents, or tools and begin adding evaluations and versions. Over time, more of your critical logic lives in Vellum, and Gumloop is left handling only the low stakes experiments.

12. Which Gumloop alternative is best for sales and GTM teams specifically?

Clay and Relevance AI are strong for outbound, enrichment, and research. Clay shines at data waterfalling and personalized outreach, while Relevance AI focuses on prebuilt “BDR” and “Researcher” agents. Many GTM teams still pair these with Vellum when they need custom agents that follow stricter guardrails or touch internal systems.

13. Why choose Vellum over all the other Gumloop alternatives in this list?

Most other tools focus on either simple automations, deep engineering control, or narrow use cases. Vellum is built to be the fastest way for any team to turn ideas into AI agents and AI Apps that can be tested, shared, and trusted across the business. If your goal is to get real work off people’s plates while keeping control over behavior, cost, and risk, Vellum is the clear upgrade path from Gumloop.

Citations

[1] Gartner. (2024). Gartner Survey Finds 55% of Organizations are in Piloting or Production Mode with Generative AI.
[2] Datadog. (2024). State of Application Security: AI and LLM Adoption.
[3] Capgemini. (2024). Harnessing the value of Generative AI: Top trends 2024.
[4] Stanford HAI. (2024). Artificial Intelligence Index Report 2024.
[5] Sequoia Capital. (2023). Generative AI’s Act Two.