n8n LangChain

LangChain Concepts in n8n

n8n implements four key LangChain patterns:

Chains: A sequence of LLM calls where the output of one becomes the input of the next. Used for multi-step reasoning, document processing pipelines, and workflows where the AI needs to think in stages.

Agents: An AI model with access to tools that it can use autonomously to accomplish a goal. The model decides which tools to use and in what order. n8n's AI Agent node implements this pattern.

Memory: State that persists across multiple AI calls. Allows the AI to remember previous interactions — essential for conversational agents, multi-session assistants, and tasks that build on prior context.

Retrievers: Components that search a knowledge base (vector store) for relevant information based on a query. Used in RAG systems where the AI needs to answer questions about your specific data.

Building a RAG System in n8n

A RAG (Retrieval Augmented Generation) system answers questions using information from your documents — not just the AI model's training data. Building one in n8n:

Step 1: Ingest documents (one-time setup)

1. Add a "Document Loader" node — load PDFs, text files, or web pages
2. Add a "Text Splitter" node — split documents into chunks (e.g., 500 token chunks)
3. Add an "Embeddings" node — generate vector embeddings for each chunk
4. Add a "Vector Store" node — write the embeddings to Pinecone, Qdrant, or Supabase pgvector

Step 2: Query the RAG system (runtime workflow)

1. Receive a question (via chat, webhook, or trigger)
2. Add an "Embeddings" node — embed the question
3. Add a "Vector Store Retriever" node — find the top 5 most relevant chunks
4. Add an "Anthropic" node — pass the question + retrieved context: "Answer the question based on this context: [retrieved chunks]. Question: [question]"
5. Return the answer

The result: questions are answered using your specific documents, not the AI's general knowledge. A customer support bot that answers questions about your actual product documentation, not generic answers.

Memory Nodes

Memory allows AI agents to maintain context across multiple messages — essential for conversational applications.

n8n supports several memory types:

• Window Buffer Memory: Remembers the last N messages in a conversation
• Summary Memory: Summarises older messages to fit within context limits
• Postgres/Redis Chat Memory: Persists conversation history in a database across sessions

For a customer service chatbot: the AI Agent node + Window Buffer Memory means the agent remembers everything said earlier in the conversation. "As I mentioned earlier, I'm calling about my March invoice" doesn't require the customer to repeat themselves.

Multi-Agent Workflows

Complex tasks can be split across multiple specialised agents. n8n supports this through workflow-to-workflow calls and sub-workflow execution.

Example: a complex research report is broken into:

• Agent 1: Research the topic (web search + summarisation)
• Agent 2: Generate the outline based on research
• Agent 3: Write each section
• Agent 4: Edit and format the final report

Each agent is a separate n8n workflow. The orchestrating workflow calls them in sequence, passing outputs between them.

Tool Nodes for Agents

n8n has dedicated Tool nodes designed to be connected to AI Agent nodes:

• Tavily Search Tool: Web search capability
• Wikipedia Tool: Wikipedia article lookup
• Calculator Tool: Mathematical calculations
• HTTP Request Tool: Call any external API as a tool
• Code Tool: Execute JavaScript/Python as a tool
• n8n Workflow Tool: Call another n8n workflow as a tool

Connect these tool nodes to your AI Agent node, and the agent can use them autonomously based on what the task requires.

Recommended Tools

• n8n — Platform with native LangChain integration
• Claude API — Language model for agents and chains
• Pinecone — Vector store for RAG systems
• Supabase — PostgreSQL with pgvector, good n8n integration
• OpenAI — Embeddings model (OpenAI's text-embedding-3-small)
• Airtable — Data source for document ingestion

“n8n's LangChain nodes make sophisticated AI patterns accessible without Python. The same RAG system that would take a developer a week to build in code takes a day in n8n.”