n8n vs. Langflow

n8n treats AI as a core part of the platform by providing dozens of LangChain-based nodes. You can use the AI Agent node to let a model choose tools and decide what to do next, while memory nodes help it retain context across interactions.

You can also:

• Connect vector stores to build RAG workflows that ground the agent in your own data.
• Use n8n as an MCP server (via the MCP Server Trigger node, exposing workflows as callable tools) and as an MCP client (via the MCP Client Tool node, calling tools on other MCP servers).

With n8n, you can place AI reasoning inside a wider process. For example, an agent might classify a request before the platform applies a business rule, pauses for approval, and updates another system.

You aren’t locked into one model provider, either. You can move between AI models like OpenAI, Anthropic, and other providers, or a self-hosted model without rebuilding the surrounding workflow.

n8n combines AI agents with deterministic steps on the same canvas

Langflow is a specialized environment for building the AI application itself. You can create flows, agents, and chains, then configure the models and retrieval components behind them. Its RAG-first approach also makes it easier to connect knowledge sources and vector stores. The Playground interface feature lets you test behavior in real time, so you can easily adjust prompts and model settings before deployment.

Langflow also supports MCP as both a client and server. Once a flow is ready, you can publish it as an API or MCP server, making it simpler to share or connect to another application.

Langflow offers a visual canvas, however it supports a limited amount of non-AI integrations

You can choose the managed n8n Cloud service or host the platform on your own infrastructure. For larger workloads, queue mode separates the editor from workflow execution, so heavy processing doesn’t compete with the main application. Redis acts as the message broker, passing jobs to worker processes that you can add as demand increases. You can deploy this setup through Docker, Kubernetes, or your preferred cloud environment.

You can run Langflow through its desktop app, install the Python package, or deploy it with Docker. It also provides a headless runtime for production and Kubernetes tooling that can scale across replicas. This gives you greater control over how the system runs and scales, but you’ll still have to operate the surrounding infrastructure, including monitoring, storage, and access controls.

n8n builds controls into its project and workflow model. Lower pricing tiers provide shared projects and a smaller role set, while Enterprise adds Project Viewer access, custom project roles, audit logging, and log streaming. SAML, LDAP, and SSO are also available on the self-hosted Business plan. When you self-host, you choose where execution data and credentials stay, which can help meet data residency requirements.

Langflow is MIT-licensed and you can self-host, giving you control over where its data and models run. However, the platform offers fewer multi-user features, such as flow sharing and collaborative editing. Langflow also supports external credential providers via an SSO plugin.

n8n has 1000+ pre-built app integrations for connecting agents to business systems. When a native node doesn’t cover what you need, you can install community nodes, create a custom one, or use the HTTP Request node to call almost any service with an API.

Langflow’s integration system centers on the AI stack. Its components and third-party bundles connect flows to data sources, model providers, and vector stores, making it well suited to RAG and LLM application development. You can publish a completed flow through an API, but you still need to connect its endpoint to the wider operational process. n8n can fill that gap by triggering Langflow and handling the surrounding applications, business rules, and approvals.

n8n takes a visual-first approach via an accessible canvas, with code available when the workflow needs more than built-in nodes can provide. Add JavaScript or Python in the Code node, or use expressions for smaller tasks like mapping fields and reshaping data. You can also move repeated logic into sub-workflows, making large automations easier to reuse and maintain instead of rebuilding them in multiple tools.

Langflow is a more Python-first tool. Every visual component has underlying Python code you can inspect and edit, while custom components are classes built on Langflow’s Component base class. You can export flows as JSON and generate Python or JavaScript snippets to call them through the API. Langflow is highly flexible for Python teams, but it keeps you closer to the code than n8n’s visual-plus-code approach.

n8n records each workflow execution and shows the input and output at every node. You can reopen a failed run, re-execute one node with the same data, and check whether the issue came from your logic or an external service. Dedicated error workflows can also send alerts or trigger fallback actions when failures happen in production. Finally, n8n can stream traces via OpenTelemetry to an external monitoring system.

With n8n you can debug your workflow on the same canvas where you build it. The logs pane shows all steps that were taken in a compact form.

Langflow lets you test the AI flow as you build it. You can use the Playground tool to interact with agents and review their activity, and you can also run individual components in isolation and inspect their inputs and outputs. This approach helps you trace whether prompts or component settings cause an unexpected result before you deploy the flow.

Langflow’s Playground shows a high-level overview of interactions with the app

n8n is source-available under its fair-code Sustainable Use License (not OSI open source). You can self-host the Community Edition free for permitted internal use, while n8n Cloud charges by complete workflow executions rather than individual steps. It’s easier to forecast complex automations in n8n because adding more nodes doesn’t increase the cost.

Langflow uses the permissive MIT License, so you can self-host and modify the software without a platform subscription. It has no first-party managed cloud pricing tier — you’ll have to deploy it yourself or go through third-party cloud infrastructure. With Langflow, the majority of your costs come from hosting, storage, observability tools, and the model APIs each flow uses.