> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/n8n-io/n8n/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Workflows Overview

> Learn how to build powerful AI-powered workflows in n8n using LangChain nodes, agents, and vector stores

# AI Workflows in n8n

n8n provides a comprehensive suite of AI nodes built on top of LangChain, enabling you to create sophisticated AI-powered automation workflows. From simple LLM chains to complex autonomous agents with memory and tool use, n8n makes it easy to integrate AI capabilities into your workflows.

## What You Can Build

With n8n's AI nodes, you can build:

<CardGroup cols={2}>
  <Card title="AI Agents" icon="robot" href="/ai/agents">
    Autonomous agents that can use tools, make decisions, and interact with your workflows
  </Card>

  <Card title="RAG Pipelines" icon="database">
    Retrieval Augmented Generation systems that combine vector stores with LLMs for context-aware responses
  </Card>

  <Card title="Document Processing" icon="file-text">
    Extract, analyze, and summarize information from documents using AI
  </Card>

  <Card title="Conversational AI" icon="message-circle">
    Build chatbots and conversational interfaces with memory and context
  </Card>
</CardGroup>

## Core AI Components

### Language Models

Connect to various LLM providers to power your AI workflows:

* **OpenAI**: GPT-4, GPT-3.5-turbo, and other OpenAI models
* **Anthropic**: Claude models for advanced reasoning
* **Google**: Gemini and Vertex AI models
* **Open Source**: Ollama, Hugging Face, and more
* **Others**: Cohere, Groq, Mistral, DeepSeek, and many more

### Chains

Chains are pre-built workflows for common AI tasks:

* **Basic LLM Chain**: Simple prompting with structured output
* **Question & Answer Chain**: RAG-powered Q\&A over your documents
* **Summarization Chain**: Summarize long documents efficiently
* **Information Extractor**: Extract structured data from unstructured text
* **Text Classifier**: Classify text into categories
* **Sentiment Analysis**: Analyze sentiment in text

### Agents

Agents are autonomous AI systems that can use tools and make decisions:

* Execute multi-step reasoning
* Use tools like web search, calculators, and custom workflows
* Maintain conversation context with memory
* Handle complex tasks autonomously

[Learn more about AI Agents →](/ai/agents)

### Vector Stores & Embeddings

Store and retrieve information using semantic search:

* **Vector Stores**: Pinecone, Qdrant, Supabase, Weaviate, and more
* **Embeddings**: OpenAI, Cohere, Google, and others
* **Document Loaders**: Load data from various sources
* **Text Splitters**: Break documents into chunks for processing

[Learn more about Vector Stores →](/ai/vector-stores) | [Learn more about Embeddings →](/ai/embeddings)

## Getting Started

<Steps>
  <Step title="Choose Your Use Case">
    Decide what you want to build - a simple LLM chain, a RAG system, or an autonomous agent.
  </Step>

  <Step title="Add a Language Model">
    Add a language model node (e.g., OpenAI Chat Model) and configure your credentials.
  </Step>

  <Step title="Build Your Chain or Agent">
    Add a Chain or Agent node and connect it to your language model.
  </Step>

  <Step title="Add Tools or Memory (Optional)">
    Enhance your agent with tools, memory, or vector store retrieval.
  </Step>

  <Step title="Test and Deploy">
    Test your workflow and deploy it as a webhook, scheduled task, or integration.
  </Step>
</Steps>

## Quick Example: Simple Q\&A with Memory

Here's a basic example of building a conversational AI with memory:

```json theme={null}
{
  "nodes": [
    {
      "name": "When chat message received",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger"
    },
    {
      "name": "OpenAI Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi"
    },
    {
      "name": "Simple Memory",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow"
    },
    {
      "name": "AI Agent",
      "type": "@n8n/n8n-nodes-langchain.agent"
    }
  ]
}
```

<Note>
  This example uses the Chat Trigger node to receive messages, connects to OpenAI's chat model, adds simple memory for context, and processes everything through an AI Agent.
</Note>

## Architecture Patterns

### RAG (Retrieval Augmented Generation)

RAG combines vector search with LLMs to provide context-aware responses:

```
Documents → Embeddings → Vector Store → Retriever → Q&A Chain → Response
```

### Agent with Tools

Agents can use multiple tools to accomplish complex tasks:

```
User Input → Agent → Tool Selection → Tool Execution → Agent Reasoning → Response
```

### Multi-Agent Systems

Combine multiple specialized agents for complex workflows:

```
Coordinator Agent → Research Agent → Analysis Agent → Writing Agent → Final Output
```

## Best Practices

<Tip>
  **Start Simple**: Begin with a Basic LLM Chain before moving to more complex agents.
</Tip>

* **Use Structured Output**: Enable output parsers for reliable data extraction
* **Implement Memory Wisely**: Choose the right memory type for your use case
* **Optimize Token Usage**: Use text splitters and limit context window size
* **Handle Errors**: Enable "Continue on Fail" for production workflows
* **Monitor Costs**: Track API usage, especially with external LLM providers
* **Test Thoroughly**: Test with various inputs before deploying

## Advanced Features

### Output Parsers

Structure LLM responses into reliable JSON schemas:

* **Structured Output Parser**: Define custom schemas
* **Auto-fixing Parser**: Automatically fix malformed outputs
* **Item List Parser**: Extract lists from responses

### Memory Types

Choose from various memory implementations:

* **Simple Memory**: In-memory buffer (development)
* **Redis Memory**: Distributed memory (production)
* **Postgres Memory**: Persistent SQL-based memory
* **MongoDB Memory**: Document-based memory
* **Zep Memory**: Specialized memory with automatic summarization

### Tools & Integrations

Extend agent capabilities with built-in tools:

* **HTTP Request Tool**: Make API calls
* **Calculator Tool**: Perform calculations
* **Wikipedia Tool**: Search Wikipedia
* **Workflow Tool**: Call other n8n workflows
* **Code Tool**: Execute JavaScript/Python code
* **Vector Store Tool**: Query vector databases

## Next Steps

<CardGroup cols={2}>
  <Card title="Build Your First Agent" icon="rocket" href="/ai/agents">
    Learn how to create autonomous AI agents
  </Card>

  <Card title="LangChain Nodes Reference" icon="book" href="/ai/langchain-nodes">
    Explore all available LangChain nodes
  </Card>

  <Card title="Vector Stores Guide" icon="database" href="/ai/vector-stores">
    Set up semantic search with vector databases
  </Card>

  <Card title="Embeddings Guide" icon="layers" href="/ai/embeddings">
    Configure embedding models for your workflows
  </Card>
</CardGroup>

## Resources

* [LangChain Documentation](https://docs.langchain.com/)
* [n8n Community Forum](https://community.n8n.io/)
* [AI Workflow Templates](https://n8n.io/workflows?categories=AI)
* [Tutorial: Build a RAG Chatbot](https://docs.n8n.io/advanced-ai/intro-tutorial/)