n8n AI: Complete Platform Profile

n8n is an open-source workflow automation platform that has evolved into one of the most capable AI-enabled automation tools available. Founded in 2019 by Jan Oberhauser, it began as a Zapier alternative for technical teams who wanted self-hostable, extensible automation. By 2023, n8n had added dedicated AI agent nodes, LLM integrations, and built-in vector store support — transforming it from a pure automation tool into a hybrid platform where AI capabilities are embedded inside the world's richest integration ecosystem.

With more than 48,000 GitHub stars and more than 400 pre-built connectors, n8n occupies a unique position: it is the only tool in this profile guide where "connect an AI agent to Slack, HubSpot, Google Calendar, a SQL database, and a custom webhook" is a five-minute task rather than a multi-day integration project. This integration density makes n8n uniquely valuable for AI use cases that must interact with many existing business systems.

This profile examines n8n's AI capabilities specifically — what it can do with LLMs, agents, and RAG — alongside its automation foundations, honest limitations, and where it fits relative to dedicated AI frameworks.

Browse the full AI agent profiles directory to compare n8n with AI-native alternatives.

Overview#

n8n's core architecture is a visual workflow builder where nodes represent individual actions and connections define the data flow between them. A workflow is a graph of nodes that can be triggered by a schedule, a webhook, a form submission, or manually. Each node processes data from the previous node, performs an action (API call, database query, data transformation, LLM inference), and passes results to the next node.

The AI capabilities were added as first-class node types:

AI Agent node: An LLM agent with tool access and memory, implementing the ReAct pattern
Basic LLM Chain: Simple prompt-in, response-out for straightforward LLM calls
Summarization Chain: Multi-document summarization using map-reduce or recursive patterns
Vector Store nodes: Index and query documents from Pinecone, Qdrant, Supabase, or in-memory stores
Embeddings nodes: Generate text embeddings using OpenAI, Cohere, or local models
Memory nodes: Buffer memory and vector-based memory for maintaining conversation context

The critical insight about n8n's AI architecture is that these AI nodes are peers to the other 400+ integration nodes. An AI agent node can receive data from a Google Sheets trigger, process it, query a vector store, call an OpenAI model, and then write results to Slack, HubSpot, and a PostgreSQL database — all in a single visual workflow. This integration-native AI architecture is n8n's core differentiator.

Core Features#

AI Agent Node (ReAct Architecture)#

The AI Agent node implements the agent loop pattern: an LLM receives a task, selects a tool, observes the result, and iterates until complete. The agent's "tools" in n8n are other nodes — any n8n node can be exposed as a tool to the AI agent. This means an agent can search the web, query a database, read a Google Doc, post to Slack, or call a custom HTTP endpoint as native tool actions, without any custom integration code.

The AI Agent node supports multiple agent architectures: Tools Agent (OpenAI function calling), ReAct Agent (reasoning and action), and Conversational Agent (memory-enabled chat). You select the architecture in the node settings and configure which tools are available.

This is fundamentally different from how AI agents work in dedicated frameworks. In LangChain or CrewAI, you build custom tools to access external services. In n8n, your external service integrations already exist as nodes — you just expose them as agent tools.

For foundational concepts on how tool-using agents work, see the tool use glossary entry.

Integration Ecosystem (400+ Connectors)#

n8n's 400+ native integrations cover the full breadth of business software: CRMs (Salesforce, HubSpot), messaging (Slack, Teams, email), productivity (Google Workspace, Microsoft 365), databases (PostgreSQL, MySQL, MongoDB), developer tools (GitHub, Jira, Linear), marketing (Mailchimp, ActiveCampaign), storage (AWS S3, Google Drive), and hundreds more.

For AI use cases, this integration breadth means you can build AI workflows that touch any part of a business's data and systems. A customer service AI agent that can read CRM history, update ticket status, and send follow-up emails is a one-day project in n8n — a weeks-long integration effort in a pure AI framework. See the n8n vs Make vs Zapier comparison for how this positions against other automation platforms.

Vector Store and RAG Pipeline#

n8n supports building complete RAG pipelines visually. Supported vector stores include Pinecone, Qdrant, Supabase Vector, Weaviate, Chroma, and an in-memory store for testing. The workflow pattern for a RAG pipeline: HTTP Request node (fetch documents) → Text Splitter node → Embeddings node → Vector Store node (upsert). Retrieval uses the AI Agent node with a vector store retrieval tool.

While n8n's RAG pipeline is less polished than Dify's dedicated RAG module (no chunk preview, limited retrieval quality controls), it is functional and has the significant advantage of connecting to any document source that n8n can access — which is essentially every cloud service.

Sub-Workflows and Modular Design#

n8n supports calling one workflow from another (sub-workflow execution). This enables building modular AI systems: a main orchestration workflow calls specialized sub-workflows for research, writing, or analysis. This is n8n's approach to multi-agent patterns — each "agent" is a workflow that can be called by the orchestrator.

This compositional design works well for use cases where you want to combine automation logic with AI inference at multiple points in a larger process.

Self-Hosting and Data Control#

n8n's self-hosted version gives complete data control — no workflow data passes through n8n's servers. For enterprise use cases with sensitive data (customer records, financial data, medical information), self-hosting on your own infrastructure ensures compliance requirements can be met. The self-hosted version is free and includes all automation features; AI features require valid LLM API keys but no additional n8n licensing.

n8n Cloud (Managed)#

For teams that prefer not to manage infrastructure, n8n Cloud provides managed hosting with automatic updates, monitoring, and support. AI features are available on all paid tiers with no additional configuration.

Pricing and Plans#

Self-Hosted Community Edition (Free): Fully open-source under the Sustainable Use License. All features including AI nodes, the full integration library, and unlimited workflows are available. Requires a server to host (a $5-10/month VPS is sufficient for small deployments).

n8n Cloud (Paid):

Starter: $20/month — 2,500 workflow executions/month, community support
Pro: $50/month — 10,000 executions/month, up to 15 active workflows, email support
Enterprise: Custom pricing — unlimited executions, SSO, on-premise option, dedicated support

Execution limits apply to cloud plans. Heavy automation workflows (thousands of daily triggers) scale costs quickly. Teams with high execution volume should evaluate self-hosting for cost control. The $20 Starter tier is a reasonable entry point for teams just getting started with AI automation.

Strengths#

Integration depth is unmatched. No other platform in this guide comes close to n8n's integration coverage. If you need AI that interacts with business software, n8n eliminates most of the integration work that would otherwise be required.

AI in context of real data flows. n8n places AI inside operational workflows — triggered by real events (new support ticket, weekly schedule, form submission) and taking real actions (update record, send message, generate report). This operational integration is more valuable for many business use cases than standalone AI applications.

Self-hostable with no feature restrictions. The community edition is genuinely full-featured. For cost-conscious teams or those with data privacy requirements, self-hosting provides enterprise-grade automation at infrastructure cost only.

Code when needed, visual when possible. n8n nodes support JavaScript/TypeScript expressions and a full Code node for custom logic. This escape hatch is important for cases where the visual model is insufficient.

Active community and template library. n8n's community workflow templates number in the thousands, including many AI-specific templates (AI email assistant, AI customer support, AI research workflow). These templates are starting points that dramatically reduce setup time.

Limitations#

Not a dedicated AI framework. n8n's AI agent and chain nodes are simpler than equivalent components in LangChain or LangGraph. Complex agent patterns (multi-agent coordination, stateful workflows with branching based on intermediate results, human-in-the-loop approval gates) require workarounds or are not supported.

Execution model is batch-oriented. n8n workflows process data in batches as each node completes. For streaming LLM responses or long-running agent tasks that need real-time output, n8n's architecture is less suitable than purpose-built frameworks.

Debugging non-trivial workflows. When a complex workflow fails, the execution log shows which node failed, but tracing data through multiple transformation steps and conditional branches can be time-consuming.

Sustainable Use License. n8n uses the "Sustainable Use License" rather than a pure open-source license like MIT or Apache 2.0. The restriction: you cannot use the community edition to offer a competing automation service. For most users this is irrelevant, but it is worth being aware of.

Ideal Use Cases#

n8n AI excels for:

AI-powered automation pipelines: New customer sign-up triggers an AI to summarize their profile, enriches CRM data, and routes to the right sales representative
Content automation: AI generates content on a schedule, publishes to CMS, posts to social media, and logs to a spreadsheet
AI customer service workflows: Incoming support tickets classified by AI, routed to the right team, and auto-responded to for common questions
Data enrichment pipelines: Batch process records through an LLM for classification, sentiment analysis, or summary generation
Internal tools: Weekly AI-generated reports from business data, distributed via email or Slack
RAG-powered Slack bots: A Slack message triggers a workflow that queries a knowledge base and replies with AI-generated answers

For workflows where the AI logic is the primary challenge (complex reasoning, multi-agent coordination, fine-grained state management), dedicated frameworks are more appropriate. For workflows where connecting AI to existing business systems is the primary challenge, n8n is likely the best tool available.

Getting Started#

Self-hosted setup:

Install via npm: npm install n8n -g then n8n start
Or use Docker: docker run -it --rm --name n8n -p 5678:5678 n8nio/n8n
Open browser to localhost:5678 and create your account
Configure LLM credentials in Settings > Credentials
Browse the template library for AI workflow starting points

Building an AI Agent workflow:

Create a new workflow
Add a trigger (Schedule, Webhook, or Chat trigger for interactive agents)
Add the AI Agent node, configure your LLM model and system prompt
Add tool nodes (HTTP Request, database query) and connect them to the agent
Add output nodes (Slack, email, database write)
Test and activate

The n8n community forum and template library provide extensive AI workflow examples. The LangGraph multi-agent tutorial is useful background reading on the agent patterns n8n implements, even if you are using n8n rather than LangGraph.

How It Compares#

n8n vs Zapier (with AI): Zapier has added AI features but remains proprietary, more expensive, and has far fewer customization options. n8n's self-hosting, code execution, and deeper AI node configuration make it significantly more capable for technical teams. See the Zapier vs AI Agents comparison for more detail.

n8n vs Dify: Dify is purpose-built for LLM applications with better RAG tooling, prompt versioning, and conversation analytics. n8n has vastly better integration coverage and is better for automation-heavy workflows. Use Dify when AI application management is the primary need; use n8n when connecting AI to many business systems is the primary need.

n8n vs LangChain: LangChain is a code-first Python framework for building sophisticated AI applications. n8n is a visual platform for automation with embedded AI capabilities. They serve different needs and different users. See the LangChain profile for comparison.

n8n vs Flowise: Flowise is purpose-built for LangChain-based AI flows. n8n is an automation platform with AI capabilities. For pure AI chatbot or RAG applications, Flowise's LangChain.js integration is deeper. For AI that must integrate with business systems, n8n's 400+ connectors win clearly. See the Flowise profile for more detail.

Bottom Line#

n8n's AI capabilities are not the most sophisticated in the market — but they do not need to be. n8n's value proposition for AI use cases is its extraordinary integration ecosystem and its ability to embed AI inference inside operational workflows that connect to real business systems. For enterprises that want AI to participate in their existing data flows and processes — not as a standalone application but as a step in automated pipelines — n8n is arguably the most practical tool available at any price point. Teams building sophisticated, custom AI reasoning applications should look at dedicated frameworks. Teams building AI-powered business automation with broad integration requirements should seriously evaluate n8n.

Best for: Technical teams and operations teams building AI-powered automation that must integrate with many existing business systems and cloud applications.