# Activepieces vs Agent-Native

Activepieces records 258,553 monthly visits with +1.5% month-over-month growth and Freemium pricing. Agent-Native records 52,065 monthly visits with +90.7% growth and Free pricing.

| Metric | [Activepieces](https://openaitools.app/tool/activepieces) | [Agent-Native](https://openaitools.app/tool/agent-native) |
| :--- | :--- | :--- |
| Website URL | https://www.activepieces.com | https://www.agent-native.com |
| Categories | AI Workflow, No-Code&Low-Code, AI Code Assistant, AI Agent | AI Agent, AI App Builder, AI Developer Tools, AI Workflow |
| Pricing Model | Freemium | Free |
| Monthly Visits | 258,553 | 52,065 |
| MoM Growth Rate | +1.5% | +90.7% |
| Primary Market | United States | United States |
| Direct Visits | 111,328 | 22,560 |
| Review Score | 4.6 | 4.4 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Activepieces

Activepieces is an AI-first automation platform built around the idea that a team's agents and its people should share one workspace. At the center are AI agents: you describe a job in plain words and the agent works out the steps, the order and which tools to open across your connected apps and any MCP server you point it at. Sensitive steps can be gated behind human approval, so money, customer and production actions stay under control.

Underneath the agent layer sits a mature workflow engine with a visual builder. Chat can draft most of a flow, while branching, retries, waitpoints, subflows, error handling and human approvals handle the hard cases. Where a step needs real logic, you can drop into inline TypeScript and keep the rest visual. Chat, Tables (a lightweight structured data layer), a knowledge base and an MCP server round out the platform so agents and teammates operate on the same data.

The project is open source at its core, with hundreds of contributors maintaining MIT-licensed app pieces, and it deploys flexibly: Activepieces Cloud, your own Docker or Kubernetes infrastructure, or a fully air-gapped install. Enterprise controls include projects and access roles, SSO via Okta or Entra, secret managers such as HashiCorp Vault, audit logs streamed to your SIEM, and bring-your-own AI model keys from Anthropic, OpenAI, AWS Bedrock or Google Gemini.

**Pricing & Plans:** Freemium model: a free plan requiring no credit card lets you start building flows and agents, with paid plans for higher run volumes and enterprise features. Deployment options range from the managed cloud to self-hosted and air-gapped, and open-source users can run the community edition on their own infrastructure.

- **AI agents from plain language:** Describe a task in everyday words and the agent determines the steps, chooses tools from your connected apps and MCP servers, and pauses for approval on sensitive actions.
- **Visual flow builder with inline TypeScript:** Build automations on a canvas with branching, retries, waitpoints, subflows and error handling, then write inline TypeScript for the steps that need real code.
- **770+ open-source app connectors:** Every piece is MIT-licensed and community-built, covering the apps you already run, with an HTTP piece for any REST or GraphQL endpoint with auth and pagination handled.
- **Chat, Tables and knowledge base:** Draft flows by chatting, store structured data in Tables that flows and agents can read and write, and keep internal documents searchable by every agent.
- **Enterprise deployment and governance:** Run on the managed cloud, your Docker/Kubernetes stack or air-gapped, with projects, SSO, role-based access, secret managers, audit logs and bring-your-own AI keys.

**Strengths:** Open-source core with MIT-licensed pieces and a genuinely self-hostable build; Strong blend of AI agents, a visual builder and inline TypeScript; Enterprise-grade controls including SSO, roles, secret managers and audit streaming; Large connector library plus an HTTP piece for anything not covered

**Limitations:** The breadth of agents, tables, flows and MCP features can overwhelm new users; Some advanced enterprise capabilities are reserved for paid and self-managed plans; Self-hosting means you own upgrades, scaling and reliability

[Profile (Markdown)](https://openaitools.app/tool/activepieces.md)

## Agent-Native

Agent-Native is an open-source framework for building what its authors call agentic applications: software where an AI agent and a human interface are first-class peers rather than an agent bolted onto an existing app. The central idea is that a capability should be defined once. When you declare an action with defineAction(), the same code is callable by the embedded agent, by React components in the UI, by external HTTP clients, and by integrations, so there is no drifting duplicate logic between the automation and the interface.

Around that core, the framework wires together the pieces most agent products end up rebuilding: shared application state so the agent knows what the user is viewing or editing, a shared PostgreSQL database as one source of truth for users and agents, persistent memory and reusable skills, scheduled and event-driven automations, multi-agent teams, and authentication with organizations plus resource-level permissions for viewing, commenting, editing and managing.

Agent-Native is deliberately stack-agnostic. It is TypeScript, open source, and designed so you bring your own LLM, your own database and your own hosting, keeping the application code in your own repository. The project also ships a set of open-source reference apps, including Design, Slides, Clips, Analytics, Calendar, Mail, Content, Assets and Forms, which you can use as-is or fork and customize, and you can start a new project from the CLI or build online through Builder.io.

**Pricing & Plans:** Agent-Native is an open-source framework that is free to use; the project itself is distributed at no cost, and you pay only for whatever you supply yourself, such as your own LLM, database and hosting. Reference apps are described as free to use or customize.

- **One action, every surface:** Declare a capability once with defineAction() and it is automatically available to the agent, the React UI, HTTP clients and integrations, eliminating duplicated logic.
- **Shared state and data:** The agent sees what users are browsing, selecting and editing, and both read and write the same PostgreSQL database as a single source of truth.
- **Skills and memory:** Give agents reusable expertise, persistent context and long-term memory so they stop repeating mistakes and do not need re-explaining.
- **Automations and agent teams:** Run agent work on schedules or in response to events, and delegate to specialist agents within a workspace or across connected agents.
- **Auth, orgs and sharing built in:** Sign-in, user accounts, organization membership, and per-resource permissions for view, comment, edit and manage come wired into the framework.

**Strengths:** Capability is defined once and reused by agent, UI, APIs and integrations, which cuts duplication; Brings your own LLM, database and host, so there is no model or cloud lock-in; Open source TypeScript with application code kept in your repository; Batteries-included auth, organizations, permissions, memory, skills and automations

**Limitations:** Requires TypeScript and full-stack comfort; it is a framework, not a no-code builder; You are responsible for hosting, database operations and LLM costs; The ecosystem and third-party documentation are still relatively young

[Profile (Markdown)](https://openaitools.app/tool/agent-native.md)

## Frequently Asked Questions

### How do Activepieces and Agent-Native compare in monthly traffic?

Activepieces has 258,553 monthly visits, while Agent-Native has 52,065. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Activepieces has a month-over-month growth rate of +1.5%; Agent-Native has +90.7%.

### How does pricing compare?

Activepieces: Freemium. Freemium model: a free plan requiring no credit card lets you start building flows and agents, with paid plans for higher run volumes and enterprise features. Deployment options range from the managed cloud to self-hosted and air-gapped, and open-source users can run the community edition on their own infrastructure. Agent-Native: Free. Agent-Native is an open-source framework that is free to use; the project itself is distributed at no cost, and you pay only for whatever you supply yourself, such as your own LLM, database and hosting. Reference apps are described as free to use or customize.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Lovable](https://openaitools.app/tool/lovable) | 31,052,090 | +3.8% | Freemium | Describe your idea in chat and Lovable turns it into a working app or website | [.md](https://openaitools.app/tool/lovable.md) |
| 2 | [Cursor](https://openaitools.app/tool/cursor) | 23,297,751 | +8.4% | Freemium | The AI coding agent for building ambitious software | [.md](https://openaitools.app/tool/cursor.md) |
| 3 | [Google Antigravity](https://openaitools.app/tool/google-antigravity) | 16,054,109 | +8.1% | Freemium | Google's agent-first development platform for building and shipping software with AI agents | [.md](https://openaitools.app/tool/google-antigravity.md) |
| 4 | [Base44](https://openaitools.app/tool/base44) | 14,039,232 | +0.5% | Freemium, Paid, Contact for Pricing | Build working apps, websites, and AI agents by describing them | [.md](https://openaitools.app/tool/base44.md) |
| 5 | [Replit](https://openaitools.app/tool/replit) | 10,016,437 | -7.7% | Freemium, Paid | Describe your idea and Replit builds a working app in minutes | [.md](https://openaitools.app/tool/replit.md) |
| 6 | [Zapier](https://openaitools.app/tool/zapier-com) | 5,065,248 | +0.4% | Freemium, Free Trial, Paid | Massive prebuilt app ecosystem and polished UX, but significantly more expensive and proprietary. | [.md](https://openaitools.app/tool/zapier-com.md) |
| 7 | [n8n](https://openaitools.app/tool/n8n) | 4,522,449 | -7.4% | Freemium | Open-source workflow automation built for AI agents and technical teams | [.md](https://openaitools.app/tool/n8n.md) |
| 8 | [MindStudio](https://openaitools.app/tool/mindstudio) | 1,793,337 | +14.2% | Contact for Pricing | Streamlined interface for deploying custom AI agents, but less flexible for traditional data integration pipelines. | [.md](https://openaitools.app/tool/mindstudio.md) |
| 9 | [Dify](https://openaitools.app/tool/dify-ai) | 819,691 | -15% | Freemium | Build, deploy, and govern production-ready agentic AI workflows | [.md](https://openaitools.app/tool/dify-ai.md) |
| 10 | [Gumloop](https://openaitools.app/tool/gumloop) | 379,557 | -21.1% | Freemium, Paid, Contact for Pricing | Excellent browser and web scraping automation features for AI agents, but fewer standard enterprise app integrations. | [.md](https://openaitools.app/tool/gumloop.md) |

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Source: https://openaitools.app/compare/activepieces-vs-agent-native

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