n8n AI Features

Build AI-powered automation with n8n's AI nodes and LangChain integration.

AI Agents

Available Agent Types

Tools Agent

Most flexible - uses tools/functions to complete tasks.

{ "agent": "toolsAgent", "options": { "systemMessage": "You are a helpful assistant that can search and analyze data." } }

• Best for: General-purpose tasks, API integrations

• Works with: Any tool node (HTTP, database, code)

Conversational Agent

Memory-enabled for multi-turn conversations.

{ "agent": "conversationalAgent", "options": { "systemMessage": "You are a customer support agent.", "maxIterations": 10 } }

• Best for: Chatbots, support systems

• Works with: Memory nodes for context

ReAct Agent

Reasoning + Acting - thinks step-by-step.

{ "agent": "reAct", "options": { "maxIterations": 15, "returnIntermediateSteps": true } }

• Best for: Complex reasoning tasks

• Shows chain-of-thought

SQL Agent

Database-focused with schema awareness.

{ "agent": "sqlAgent", "options": { "topK": 10, "dialect": "postgresql" } }

• Best for: Natural language to SQL

• Works with: Database credentials

Plan and Execute Agent

Plans first, then executes steps.

{ "agent": "planAndExecute", "options": { "humanInputMode": "never" } }

• Best for: Multi-step complex tasks

• More deliberate than ReAct

LangChain Nodes

Chains

Basic LLM Chain

Simple prompt → response.

[Chat Trigger] → [Basic LLM Chain] → [Output] ↑ [OpenAI Chat Model]

Configuration:

{ "promptType": "define", "text": "Summarize this text: {{ $json.content }}" }

Question and Answer Chain

RAG-based Q&A with context retrieval.

[Input] → [Retriever] → [Q&A Chain] → [Answer] ↑ ↑ [Vector Store] [LLM Model]

Summarization Chain

Long document summarization.

{ "type": "map_reduce", "options": { "chunkSize": 4000 } }

Types:

• stuff

• Single pass (short docs)

• map_reduce

• Chunk and combine (long docs)

• refine

• Iterative refinement

Information Extractor

Structured data extraction from text.

{ "text": "={{ $json.document }}", "schema": { "type": "object", "properties": { "company": {"type": "string"}, "revenue": {"type": "number"}, "employees": {"type": "integer"} } } }

Text Classifier

Categorize text into predefined labels.

{ "categories": ["positive", "negative", "neutral"], "text": "={{ $json.review }}" }

Sentiment Analysis

Built-in sentiment detection.

{ "text": "={{ $json.feedback }}", "options": { "returnScore": true } }

LLM Providers

OpenAI

{ "model": "gpt-4-turbo", "options": { "temperature": 0.7, "maxTokens": 2000, "topP": 1 } }

Models: gpt-4-turbo , gpt-4 , gpt-3.5-turbo

Anthropic Claude

{ "model": "claude-3-opus-20240229", "options": { "temperature": 0.5, "maxTokens": 4096 } }

Models: claude-3-opus , claude-3-sonnet , claude-3-haiku

Google Gemini

{ "model": "gemini-pro", "options": { "temperature": 0.7 } }

Ollama (Distributed Cluster)

{ "baseUrl": "http://100.124.63.99:11434", "model": "qwen2.5:32b", "options": { "temperature": 0.8 } }

• Primary: Gaming PC (100.124.63.99 ) - RTX 4070 GPU

• Fallback: Studio (localhost:11434 ) - M2 Max

• Always-on: Tower (tower.local:11434 ) - CPU

• Models: qwen2.5, raz, deepseek-r1, llava, etc.

Groq

{ "model": "mixtral-8x7b-32768", "options": { "temperature": 0.5 } }

• Ultra-fast inference

• Good for high-volume

Mistral

{ "model": "mistral-large-latest", "options": { "temperature": 0.7 } }

Vector Stores

Supabase Vector

{ "tableName": "documents", "queryName": "match_documents", "options": { "matchThreshold": 0.8, "matchCount": 5 } }

Setup:

-- Enable pgvector extension create extension if not exists vector;

-- Create documents table create table documents ( id uuid primary key default gen_random_uuid(), content text, metadata jsonb, embedding vector(1536) );

-- Create matching function create function match_documents( query_embedding vector(1536), match_threshold float, match_count int ) returns table (id uuid, content text, similarity float) language sql stable as $$ select id, content, 1 - (embedding <=> query_embedding) as similarity from documents where 1 - (embedding <=> query_embedding) > match_threshold order by embedding <=> query_embedding limit match_count; $$;

Pinecone

{ "indexName": "my-index", "namespace": "documents", "options": { "topK": 5 } }

Qdrant

{ "collectionName": "documents", "url": "http://localhost:6333", "options": { "limit": 10, "scoreThreshold": 0.7 } }

PGVector

{ "tableName": "embeddings", "options": { "distanceStrategy": "cosine", "k": 5 } }

In-Memory Vector Store

{ "memoryKey": "document_store" }

• Good for prototyping

• Lost on restart

Embeddings

OpenAI Embeddings

{ "model": "text-embedding-3-small", "options": { "batchSize": 512, "stripNewLines": true } }

Models:

• text-embedding-3-small (1536 dims, cheaper)

• text-embedding-3-large (3072 dims, better)

• text-embedding-ada-002 (legacy)

Cohere Embeddings

{ "model": "embed-english-v3.0", "inputType": "search_document" }

Ollama Embeddings (Gaming PC)

{ "baseUrl": "http://100.124.63.99:11434", "model": "nomic-embed-text" }

Memory Nodes

Buffer Memory

Simple conversation history.

{ "sessionKey": "={{ $json.userId }}", "contextWindowLength": 10 }

Buffer Window Memory

Limited window of recent messages.

{ "sessionKey": "chat_{{ $json.sessionId }}", "windowSize": 5 }

Motorhead Memory

External memory service.

{ "url": "http://localhost:8080", "sessionId": "={{ $json.userId }}" }

Zep Memory

Advanced memory with search.

{ "baseUrl": "http://localhost:8000", "sessionId": "user_123" }

RAG (Retrieval-Augmented Generation)

Basic RAG Pattern

[Document Loader] → [Text Splitter] → [Embeddings] → [Vector Store] ↓ [User Query] → [Retriever] → [Context + Query] → [LLM] → [Response]

Document Loading

[HTTP Request] → [Extract Text] → [Text Splitter] [Read File] → [Extract PDF] → [Text Splitter] [Google Drive] → [Download] → [Text Splitter]

Text Splitters

Character Text Splitter

{ "chunkSize": 1000, "chunkOverlap": 200, "separator": "\n\n" }

Recursive Character Splitter

{ "chunkSize": 1000, "chunkOverlap": 200, "separators": ["\n\n", "\n", ". ", " "] }

• Better for preserving context

• Tries larger separators first

Token Splitter

{ "chunkSize": 500, "chunkOverlap": 50, "encodingName": "cl100k_base" }

• Token-accurate for LLM context

Retrieval Configuration

{ "topK": 5, "scoreThreshold": 0.7, "searchType": "similarity" }

Search types:

• similarity

• Cosine similarity

• mmr

• Maximum Marginal Relevance (diversity)

AI Workflow Patterns

Chatbot with Memory

[Chat Trigger] ↓ [Buffer Memory] ←→ [Conversational Agent] ↓ [OpenAI Model] ↓ [Response]

Document Q&A System

[Webhook: /upload] [Webhook: /query] ↓ ↓ [PDF Extract] [Supabase Retriever] ↓ ↓ [Text Splitter] [Q&A Chain] ↓ ↓ [Embeddings] [OpenAI Model] ↓ ↓ [Supabase Store] [Response]

Content Generation Pipeline

[Schedule: Daily] ↓ [HTTP: Get Topics] → [For Each Topic] ↓ [Basic LLM Chain: Generate Article] ↓ [Basic LLM Chain: Edit/Refine] ↓ [HTTP: Post to CMS]

AI-Powered Data Processing

[Webhook: Data Input] ↓ [Information Extractor] ↓ [Code: Validate/Transform] ↓ [If: Confidence > 0.8] ↓ [Database: Insert]

Multi-Model Routing

[Input] ↓ [Text Classifier: Complexity] ↓ [Switch] ├→ simple → [GPT-3.5] ├→ medium → [GPT-4-Turbo] └→ complex → [Claude Opus] ↓ [Merge] ↓ [Output]

MCP (Model Context Protocol) Integration

MCP Client Node

Connect to MCP servers for extended capabilities.

{ "serverUrl": "http://localhost:3000/mcp", "tools": ["web_search", "calculator", "code_interpreter"] }

Best Practices

1. Prompt Engineering

// Use structured prompts const systemPrompt = `You are a helpful assistant.

RULES:

1. Be concise
2. Use bullet points
3. Cite sources

FORMAT:

• Summary: [brief answer]
• Details: [expanded explanation]
• Sources: [references]`;

2. Temperature Settings

Use Case Temperature

Factual Q&A 0.0 - 0.3

Summarization 0.3 - 0.5

Creative writing 0.7 - 0.9

Brainstorming 0.9 - 1.0

3. Token Management

• Monitor token usage per execution

• Use summarization for long contexts

• Implement chunking for large documents

• Cache embeddings to reduce API calls

4. Error Handling

[AI Node] ↓ (on error) [Error Trigger] ↓ [Fallback Response] ↓ [Log Error]

5. Cost Optimization

• Use smaller models for simple tasks

• Cache frequent queries

• Batch similar requests

• Use local models (Ollama) for development

Output Format

AI WORKFLOW: Purpose: [What it automates] LLM: [Model choice and why] Vector Store: [If RAG, which store]

NODES:

1. [Trigger] - [Configuration]
2. [AI Node] - [Model, temperature, prompt] ...

PROMPT TEMPLATE: System: [System message] User: [User message template]

CONSIDERATIONS:

• Tokens: [Estimated usage]
• Cost: [Per execution estimate]
• Latency: [Expected response time]