CrewAI vs n8n: Key Differences and Which Platform Wins for AI Agents

Which framework should you use to build efficient agentic AI – CrewAI or n8n? Both are powerful in their own right, but they take very different approaches to orchestrating AI agents.

CrewAI is a code-driven framework born in the LLM era, whereas n8n is a popular general automation tool that recently embraced AI functionality. Choosing the right agent platform can determine how effectively your AI solutions operate in practice.

In this CrewAI vs n8n article, we break down the key differences in framework maturity, core features, and integration capabilities of both platforms. We also discuss how you can leverage them together (with a tool like ZenML) to get the best of both worlds. Let’s get started.

CrewAI vs n8n: Key Takeaways

🧑‍💻 CrewAI: A Python-based framework for creating multi-agent AI systems. It organizes agents into ‘crews’ with defined roles, tasks, and processes, enabling collaborative problem-solving. Developers gain precise control through code, YAML, or enterprise visual tools, perfect for autonomous agents handling research, coding, or analysis.

🧑‍💻 n8n: An open-source workflow automation tool with built-in AI capabilities. It uses a node-based visual editor to connect AI agents with over 1,000 integrations, supporting patterns from single agents to multi-agent teams. This makes n8n strong for hybrid setups where AI enhances traditional automations like data syncing or notifications.

CrewAI vs n8n: Framework Maturity and Lineage

The maturity and development history of CrewAI and n8n provide important context for adoption decisions. CrewAI is a young entrant from the LLMops boom, whereas n8n has been evolving in the automation space for years. Below is a comparison of key metrics for the two projects:

CrewAI vs n8n

Metric	CrewAI	n8n
First public release	v0.1.0 — Nov 2023	v0.1 — Jun 2019
GitHub stars (as of Aug 2025)	~36,000	~133,000
Forks	~5,000	~41,000
Commits (total)	≈ 1,640	≈ 15,100
LangChain dependency	None (built from scratch, independent)	Independent platform (LangChain used via plugin nodes)
Notable users/proof points	100K+ developers certified via CrewAI community courses	Used widely in industry (e.g., Wayfair, Microsoft, Twilio, Zendesk)

👀 Note: Data current as of August 2025 (metrics may change over time).

CrewAI launched in late 2023 (first release November 2023), making it a relative newcomer. Despite its youth, it quickly attracted a large following – on GitHub, it boasts ~36k stars, an impressive number for a project barely two years old.

In terms of development pace, CrewAI’s repository shows ~1.6k commits so far, which is active but still lean compared to mature projects.

n8n, by contrast, has been around since 2019 and has matured into one of the most popular automation tools globally. Its GitHub repo has on the order of 133k stars (placing it among the top 50 public repos on GitHub) and over 15k commits of development history.

CrewAI vs n8n: Features Comparison

Both platforms enable the creation of “agentic” AI workflows, but they differ in how those workflows are constructed and executed. The table below provides a high-level summary of how CrewAI and n8n stack up on core features (which we examine in detail in the following sections):

CrewAI vs n8n Features

Feature	CrewAI	n8n
Agentic Workflows	- Multi-agent by design (agents + tasks form a crew) - Flows for event-driven orchestration of tasks and triggers	- AI agents treated as nodes in workflows - Support for tool use and step-by-step reasoning via LangChain-based agents, with output parsers for structured results
Workflow Authoring	- Code-first (Python SDK + YAML configs) - CLI to scaffold projects - Crew Studio UI (visual editor) available in enterprise	- Low-code canvas - Drag-and-drop nodes with an extensive templates library - Code nodes allow custom logic, but basic flows require no code
Multi-Agent Patterns	- Core capability – supports sequential and hierarchical agent processes - A future consensual (team voting) mode is planned	- Achieved by chaining multiple agent nodes or sub-workflows - Includes built-in nodes for common patterns (e.g., Plan-and-Execute for planner/executor)
Human-in-the-Loop	- Built-in HITL workflow support: pause an agent task for human review/approval, then resume via API/Webhook	- Human oversight is implemented with building blocks (e.g., Wait + email/chat/webhooks/forms) - There’s no dedicated "HITL" node

If you want to learn how each of the above features works for the two platforms, read on. In the next sections, we compare CrewAI and n8n across these four most important aspects of agentic frameworks.

Feature 1. Agentic Workflows

Agentic workflows refer to how each platform defines the behavior of an AI agent or a team of agents to achieve goals. This includes the abstractions used to represent agents, how those agents make decisions or use tools, and how a sequence of agent actions is orchestrated.

CrewAI

In CrewAI, the application is the agentic system. Its architecture is built on a clear hierarchy of components that you compose to create your application.

Agents: These are the fundamental actors. Each agent is a specialized, role-playing entity defined by a role, goal, and backstory.
Tasks: These are the specific assignments given to agents. Each task has a clear description and an expected output, defining what the agent needs to accomplish.
Crews: A crew is the orchestrating entity that brings agents and tasks together. It manages the agents and defines the overall process they will follow to achieve a collective goal.

More recently, CrewAI introduced Flows, a feature that helps you build more structured, event-driven automations. A Flow can orchestrate a series of steps and can incorporate entire Crews to handle ‘pockets of agency’ where autonomous decision-making is needed. This offers a balance between deterministic control and agentic collaboration.

📚 Also read other CrewAI comparisons:

n8n

In n8n, the primary entity is the workflow, not the agent. The AI Agent node is a powerful, self-contained step within that workflow. It acts as an intelligent decision-maker that can reason, use tools, and interact with other parts of the n8n ecosystem.

For example, a typical n8n workflow might be triggered by a new entry in a Google Sheet. The data from that sheet is passed to an AI Agent node, which analyzes the information and decides on a course of action.

The agent's output, a structured piece of JSON, then triggers a downstream node, like one that sends a formatted message to a Slack channel.

The agent in this model is not the entire application; it is a smart component integrated into a broader automation. This integrative architecture makes n8n exceptionally good at adding intelligence to existing business processes that span multiple systems.

📚 Also read n8n vs LangGraph

Feature 2. Workflow Authoring

This feature focuses on how you build and author workflows in each platform. Do you write code, use a UI, or both? How do you express the logic of your AI pipeline?

CrewAI

CrewAI is a code-first framework. As a developer, you write Python to define everything: agents, tasks, crews, and flows.

The framework is distributed as a Python package (crewai), so you can import it into your project.

A typical project might have a crew.yaml or Python configuration where you list your agents with their roles, LLM models, and tools, and tasks with descriptions and assignments. You then write a small amount of code to kick off the crew or define a flow of events.

For example, in CrewAI’s quickstart, you might see something like:


from crewai import Agent, Crew, Process

# Define two agents
analyst = Agent(role="Analyst", llm_model="gpt-4")
assistant = Agent(role="Assistant", llm_model="gpt-4", tools=[...])

# Define tasks and assign agents
tasks = [
    {"task": "Analyze quarterly sales data", "agent": analyst},
    {"task": "Draft insights report", "agent": assistant, "human_input": True}
]

# Create a crew with a sequential process
crew = Crew(agents=[analyst, assistant], tasks=tasks, process=Process.sequential)
crew.run()  # Execute the workflow

In this pseudo-code, I created agent objects and passed them into a Crew definition along with tasks. This all happens in code. If I wanted to make it hierarchical, I’d specify process=Process.hierarchical and maybe add a manager_agent=analyst to have the Analyst oversee the Assistant.

I could also define this in a YAML config and load it – CrewAI supports a YAML definition format for convenience – but ultimately it’s still developer-centric.

Because everything is code, you get version control and the ability to use the full Python ecosystem while authoring workflows.

Want to add a complex conditional? You just write an if statement in Python or use CrewAI’s flow control APIs to add conditional branches.

Need a loop? CrewAI flows allow loops, or you can call crew.run() in a loop in code. This is highly flexible but requires programming skills.

n8n

n8n’s philosophy for workflow authoring is low or no-code wherever possible. You build workflows in n8n using its browser-based visual editor.

The interface is a blank canvas where you can drag nodes from a sidebar, configure them via forms, and connect them by drawing lines (arrows) from one node to the next.

This style is immediately familiar if you’ve used tools like Zapier, Node-RED, or Airflow’s UI – except n8n’s editor is much more powerful in terms of the variety of nodes and logic you can implement.

Authoring an AI agent workflow in n8n is the same as authoring any workflow in n8n. For example, to create a simple email reply agent, you might: add a Gmail Trigger node (fires when a new email arrives), connect it to an AI Agent node that drafts a reply, then connect that to a Gmail Send node to send the reply.

Each node is configured with a few clicks (e.g., authenticate the Gmail node, select mailbox; choose which LLM and tools for the AI node, etc.).

The data (email text, draft reply, etc.) is passed along between nodes automatically. The immediate feedback as you design is fantastic – you can execute the workflow right in the editor for a test run, inspect the output of each node, and adjust as needed.

The power of n8n’s canvas is that it’s very quick to iterate. You don’t have to restart a server or write boilerplate; you just make changes and hit execute. For custom logic, use the Code node; it supports JavaScript and Python (via Pyodide; packages/IO are limited in Cloud)

Bottom line: CrewAI’s workflow authoring is code-driven – ideal for engineers who want everything in Git and who prefer the flexibility of writing Python code to define complex logic.

n8n’s workflow authoring is visual and configuration-driven – ideal for rapid development and for involving a broader team, since you can assemble an AI workflow with clicks instead of code.

If you’re building a large-scale system where code quality, testing, and integration into a larger codebase matter, CrewAI gives you that control. If your priority is to get something working quickly and iteratively refine it (or to enable non-engineers to build automation with AI), n8n’s authoring experience is hard to beat.

Feature 3. Multi-Agent Patterns

Modern AI tasks often benefit from multiple agents working together – whether cooperating on sub-tasks or handling different roles. Here we compare how CrewAI and n8n support multi-agent workflows and what patterns they enable.

CrewAI


from crewai import Crew, Process

# Example: Creating a crew with a sequential process
crew = Crew(
    agents=my_agents,
    tasks=my_tasks,
    process=Process.sequential
)

# Example: Creating a crew with a hierarchical process
# Ensure to provide a manager_llm or manager_agent
crew = Crew(
    agents=my_agents,
    tasks=my_tasks,
    process=Process.hierarchical,
    manager_llm="gpt-4o"
    # or
    # manager_agent=my_manager_agent
)

Multi-agent orchestration is CrewAI’s bread and butter. The very name ‘CrewAI’ suggests a team of agents working in concert. As discussed, CrewAI provides built-in processes for multi-agent coordination:

In a Sequential Process, you could still involve multiple agents by assigning different tasks to different agents in sequence. It’s simple: Agent A does Task 1, then Agent B does Task 2, and so on. Each agent knows who is responsible for what.
The Hierarchical Process truly shines for multi-agent scenarios. Here, you designate one agent as the manager, and that agent can dynamically delegate tasks to other worker agents. This pattern is analogous to a manager in a company assigning tasks to team members.

Because the hierarchical pattern is so central, CrewAI encourages designing agents with specific roles (this agent is good at X, that one is good at Y) and letting the manager coordinate. It brings structure to multi-agent systems, preventing chaos by having a clear chain of command.

n8n

n8n doesn’t have a native concept called ‘multi-agent teams,’ but you can absolutely orchestrate multiple agents within an n8n workflow. The approach is more manual: you string together multiple AI agent nodes and let them exchange information via the workflow.

The n8n team has documented a few patterns for multi-agent workflows in their guides. Two notable ones are:

Gatekeeper (or Planner/Executor) Pattern: This is where one agent acts as a supervisor or planner, and it delegates tasks to another agent or set of agents. In n8n, you might implement this by using the ‘Plan and Execute’ nodes that they provide.
Multi-Agent Conversation (Teams) Pattern: If you want two or more agents to truly interact (say, bounce ideas back and forth), you can simulate a loop in n8n. For example, Agent A node -> Agent B node -> back to Agent A, continuing until some condition is met.

In contrast to CrewAI, coordination logic in n8n is explicit in the workflow structure rather than implicit in a framework. You’re responsible for deciding, say, ‘after Agent A, go to Agent B, and here’s how to break out if needed.’

CrewAI might handle those decisions internally. The upside for n8n is that if your multi-agent interaction follows a common pattern, there might already be a node or recipe for it. The downside is that if you want something very custom, you have to orchestrate it by hand on the canvas, which can get a bit complex.

Bottom line: CrewAI provides first-class support for multi-agent patterns – sequential and hierarchical teamwork are built in, making it straightforward to spin up an AI ‘team’ with roles and a manager coordinating them.

n8n can definitely do multi-agent workflows, but you have to construct them using its generic workflow tools and LangChain-based nodes. It makes common multi-agent scenarios like a planner and an executor, or a couple of agents in sequence, quite accessible without coding.

However, orchestrating more complex or bespoke agent interactions in n8n may require careful planning and can become a bit unwieldy in a visual diagram if there are many back-and-forth loops. CrewAI, being code-centric, lets you implement advanced coordination logic programmatically when needed, whereas n8n sticks to the paradigms provided by its nodes.

Feature 4. Human-in-the-Loop

Human oversight is critical in many AI workflows – either to ensure quality, handle exceptions, or inject expertise. Here’s how each platform facilitates Human-in-the-Loop (HITL) interactions in agent workflows.

CrewAI

CrewAI has explicit support for human-in-the-loop built into its task management. As a developer, when you define a task in a CrewAI workflow, you can mark it with human_input=True.

What does that do? It tells CrewAI that after the assigned agent completes that task (produces an output), the workflow should pause and await human approval or input before moving forward.

This means the agent’s output is not immediately accepted as final – instead, CrewAI will surface that output and wait for you (the human operator) to intervene.

You could either approve the output, modify it, or provide additional info, and then resume the execution so the next tasks can proceed using the possibly corrected output.

This pattern is quite handy. Imagine an agent that drafts an email response to a customer. You might trust the agent to do 90% of the work, but you want a person to quickly glance at the draft before it’s sent.

By marking the drafting task as human_input, CrewAI will pause after drafting. You could even integrate a simple UI or command-line prompt for the human to edit the draft.

Once the human confirms, CrewAI takes that edited text and feeds it forward to perhaps another task or just completes the workflow. If you have multiple points in the workflow that need a human check, you can mark each of those tasks accordingly.

n8n

n8n, being a general workflow tool, doesn’t have a one-click ‘human approval’ switch, but it provides the building blocks to implement any human-in-the-loop logic you need. The strategy usually involves a combination of Wait nodes and communication/integration nodes.

Here’s a common pattern in n8n for HITL: After an AI agent node produces something that needs review, you can add a Send Email node (or Slack, Microsoft Teams, etc. – any communication method) to notify a human and perhaps provide a link or a summary of what happened.

Then you place a Wait node that halts the workflow until a certain event occurs. That event might be the human replying to the email, clicking an approval link, or even manually pressing a resume button in the n8n interface.

Another way to implement HITL in n8n is by using the Webhook node. Suppose the AI fills out a draft form, and you want a human to confirm.

You could have the workflow pause at a Wait node and provide the human with a special URL (webhook link) that, when visited, will resume the workflow.

The human reviews the draft on an internal app (which you built to show the data) and clicks ‘Approve.’ That click calls the n8n webhook with some payload like approved=true. The waiting workflow receives it and continues. n8n’s Wait node can be configured to listen to an incoming webhook as the resumption event.

Bottom line: CrewAI builds HITL into the agent task logic – you declare ‘stop here for human input’ and the framework pauses at that spot every time, ensuring no uncontrolled output passes through. It’s a very direct and code-centric way to put up guardrails.

n8n uses its general-purpose nature to achieve the same ends: you can insert human review at any step by literally adding nodes to communicate with a person and waiting for their response. It requires a bit more setup, but it’s extremely flexible – you can involve humans via email, chat, web dashboards, or any medium you prefer.

CrewAI vs n8n: Integration Capabilities

An agent's ability to interact with the outside world is what makes it useful. Here, the philosophies of CrewAI and n8n diverge sharply, leading to different strengths.

CrewAI

CrewAI comes with its own integrations and also uses external Python libraries. It natively includes a library of 40+ built-in tools that comprise:

LLMs: Groq, OpenAI, Anthropic
Services: Revium, RagaAI, StartSE
Education: PWC, DeepLearning, K2 Consulting
Applications: Composio, Chroma, Cloudera
Integrations: Notion, Slack, Replit
Infrastructure: Microsoft Azure, MongoDB, Nexla

n8n

n8n's core value proposition is its massive library of over 500 pre-built nodes for popular SaaS applications, databases, and APIs. These nodes abstract away the tedious work of handling authentication, managing API keys, dealing with pagination, and checking for errors.

Bottom line: I've experienced this difference firsthand. Building a workflow to read new leads from HubSpot and create corresponding tasks in Asana is a 10-minute, two-node process in n8n.

In CrewAI, this would require installing two separate client libraries, managing API keys for both, and writing custom tools to handle the specific API endpoints. The difference in speed for integration-heavy tasks is undeniable.

CrewAI vs n8n: Pricing

In this section, we discuss pricing for both CrewAI and n8n.

CrewAI

CrewAI’s core framework is also MIT-licensed and open-source. But the platform offers several paid plans to choose from:

Basic: $99 per month
Standard: $6,000 per year
Pro: $12,000 per year
Enterprise: $60,000 per year
Ultra: $120,000 per year

n8n

n8n offers paid cloud plans with usage-based pricing and no active-workflow limits; see the pricing page for current tiers and quotas.

Starter: €24 per month. 2.5k workflow executions.
Pro: €60 per month. 10k workflow executions.
Enterprise: Custom pricing.

👀 Note: n8n also has a Community edition - a basic version of n8n that’s available on GitHub.

How ZenML Helps In Closing the Outer Loop Around Your Agents

Both CrewAI and n8n are excellent at what we might call the ‘inner loop’ of AI development – that is, creating and executing the agent workflows themselves.

CrewAI gives you the building blocks to craft intelligent agents and orchestrate their behavior; n8n lets you quickly automate processes and embed AI into them.

However, deploying and managing these agents in production involves a bigger ‘outer loop’: monitoring performance, versioning changes, handling data pipelines around the agent, and ensuring reproducibility. This is where ZenML comes into play.

ZenML is an open-source MLOps + LLMOps framework that complements tools like CrewAI and n8n by managing the end-to-end lifecycle of ML and AI workflows. Here’s how ZenML can help you leverage both platforms:

1. Unified Orchestration

You can use ZenML to create a pipeline that includes steps running CrewAI or n8n workflows.

For example, you might have a ZenML pipeline that prepares data, then calls a CrewAI agent to analyze it, then maybe uses an n8n workflow to notify stakeholders of the results, and finally logs the outcomes.

ZenML orchestrates these steps reliably. By doing so, it embeds your agent into a larger MLOps process.

This means even if your inner logic is handled by CrewAI or n8n, the overall scheduling, scaling, and sequencing can be managed by ZenML. You get a single control plane for all parts of the system.

2. Visibility and Monitoring

ZenML tracks everything that happens in a pipeline run. It will log the inputs, outputs, and even intermediate artifacts of each step.

If one of those steps is an AI agent - say, a CrewAI crew execution - you can capture its output and any metrics and have ZenML log them.

Over time, you accumulate a history of runs, which provides insight into how your agents are performing. Are they getting slower? Did accuracy drop after a certain date? ZenML’s metadata store will have the answers.

Essentially, ZenML gives you experiment tracking and observability on top of your agent workflows.

CrewAI and n8n by themselves will execute the logic, but they don’t automatically keep long-term tabs on how things evolve – ZenML fills that gap.

3. Quality Control and Feedback

Because ZenML sits above AI agents, it can incorporate evaluation steps. For instance, after an agent produces an output, a ZenML step could evaluate that output against some ground truth or use an automated metric (or even route it to a human feedback step, integrating with your HITL process).

This helps you understand if your agent is performing well or not. CrewAI and n8n define the agentic behavior, but ZenML can help judge and record the agent’s effectiveness in a systematic way.

If you’re using ZenML’s evaluation components or comparing across different agent configurations, you get a clear picture of what works best.

4. Reproducibility and Versioning

ZenML facilitates versioning of your pipelines, data, and models. If you update a CrewAI agent prompt or an n8n workflow logic, ZenML can version that change as part of a pipeline.

Later, you can refer back to ‘Pipeline run 42 used CrewAI v0.1 with prompt X and produced result Y.’

This is crucial in an enterprise setting where you need to audit what your AI did. ZenML can also cache steps – so if your n8n workflow step hasn’t changed and the inputs are the same, it won’t rerun unnecessarily, saving time/cost.

👀 Note: At ZenML, we have built several such integrations with tools like CrewAI, LangGraph, LlamaIndex, and more. We are actively shipping new integrations that you can find on this GitHub page: ZenML Agent Workflow Integrations.

📚 Other comparison articles to read:

Which AI Agent Builder Is the Best for You?

The choice between CrewAI and n8n depends entirely on your project's goals, your team's expertise, and your requirements for control versus speed.

✅ Choose CrewAI if...

You are a Python developer who needs granular control and deep customization over agent behavior.
Your project involves complex, stateful agent interactions and custom logic that goes beyond simple API calls.
The core of your application is the multi-agent system, not just a feature within a larger automation.

✅ Choose n8n if...

Your primary goal is to automate business processes that span multiple third-party SaaS applications.
You need to quickly embed AI decision-making into existing workflows with minimal custom code.
The visual representation of the workflow is important for collaboration with less technical team members.

✅ Use ZenML when...

You are ready to move your agent from a local script or notebook to a robust production system.
You need reproducibility, scalability, automated evaluation, and a unified platform to manage the entire lifecycle of your AI agents, regardless of the framework used to build them.

If you’re interested in taking your AI agent projects to the next level, consider joining the ZenML waitlist. We’re building out first-class support for agentic frameworks (like CrewAI, LangGraph, and more) inside ZenML, and we’d love early feedback from users pushing the boundaries of what AI agents can do. With ZenML, you can seamlessly integrate whichever agent framework you choose into robust, production-grade workflows. Join our waitlist to get started.👇

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