n8n Agents

The n8n Agent node (@n8n/n8n-nodes-langchain.agent) is a multi-turn LLM driver with sub-nodes for the model, memory, tools, and optional output parser.

When to use the Agent node vs raw chat completion

Decision:

• Need tool calls, multi-turn reasoning, or memory? Agent. Also a fine default when you don't want to think about it: standardizing on Agent across the workflow is reasonable and makes the path to upgrade simpler.
• Want the lightest possible single-shot text-out call? Basic LLM Chain (@n8n/n8n-nodes-langchain.chainLlm) with a chat-model sub-node (OpenRouter Chat Model, OpenAI Chat Model, Anthropic Chat Model, etc.). No agent loop, no tool/memory/parser slots, easier to debug. Note: chat-model nodes are sub-nodes, and they don't run standalone. They wire into a chain or agent. Agent works here too if you'd rather standardize.
• Routing to one of N output branches based on natural-language input (the AI's job is to pick the branch)? Use the Text Classifier node (@n8n/n8n-nodes-langchain.textClassifier). N output handles, one per category, and downstream paths wire directly into each. Every category needs both a name AND a description (the description is what the model picks against, names alone aren't enough). Set options.enableAutoFixing: true for robustness on edge inputs. Pair with a chat-model sub-node (OpenRouter Chat Model, OpenAI Chat Model, etc.). Don't reach for Agent + Switch for this. Text Classifier is one node and purpose-built.
• Structured output but no tools? Agent is the easier default with future expansion in mind. Basic LLM Chain also accepts an outputParserStructured sub-node and works fine where you want the lighter node.
• Image / audio / video generation? The native single-call node for that provider when calling them directly (OpenAI Image, Gemini Image, ElevenLabs, etc.). HTTP Request when routing through an aggregator (OpenRouter, Together, etc.), because no native aggregator node exists and the native nodes hardcode their provider's base URL on the media operation. Don't wrap media generation in an Agent, see "Binary and the agent boundary" below.

There are other LangChain "chain" / utility nodes for narrow tasks: Information Extractor (pull structured fields from text), Sentiment Analysis (3-way branch), Summarization Chain, Basic LLM Chain.

Agent is a reasonable default for most LLM steps. Reach for Basic LLM Chain when you specifically want the leaner node for a one-shot text call with no tools, memory, or iteration. Reach for Information Extractor / Sentiment Analysis / Summarization Chain / Text Classifier when one of those purpose-built nodes matches the task exactly.

Non-negotiables