AI Workflows

Overview

Codika workflows use n8n’s LangChain integration nodes to run AI operations. The two main node types for LLM processing are:

Node	Use when	Why
`chainLlm`	Structured output (classification, extraction, JSON)	Direct response, no reasoning noise
`agent`	Multi-step reasoning, tool usage	Planning and iteration capability

Critical rule: Use chainLlm when you need structured JSON output. The agent node adds verbose reasoning before the final answer, which breaks structured output parsers.

Architecture

Both node types follow the same wiring pattern:

lmChatAnthropic (model + credentials) ──ai_languageModel──┐
                                                           ├──→ chainLlm or agent
outputParserStructured ────────────ai_outputParser─────────┘

Credentials go on the model node, not on the chain/agent node.

Basic chainLlm example

This classifies an email as “newsletter”, “action_item”, or “spam”:

LLM Model node

{
  "type": "@n8n/n8n-nodes-langchain.lmChatAnthropic",
  "typeVersion": 1.3,
  "position": [600, 500],
  "id": "model-1",
  "name": "Claude Model",
  "parameters": {
    "model": {
      "__rl": true,
      "value": "claude-haiku-4-5-20251001",
      "mode": "list"
    },
    "options": {
      "maxTokensToSample": 1024,
      "temperature": 0.3
    }
  },
  "credentials": {
    "anthropicApi": {
      "id": "{{FLEXCRED_ANTHROPIC_ID_DERCXELF}}",
      "name": "{{FLEXCRED_ANTHROPIC_NAME_DERCXELF}}"
    }
  }
}

Output Parser node

{
  "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
  "typeVersion": 1.3,
  "position": [600, 600],
  "id": "parser-1",
  "name": "Classification Parser",
  "parameters": {
    "jsonSchemaExample": "{\"category\": \"newsletter\", \"confidence\": \"high\", \"reason\": \"Contains subscription links\"}"
  }
}

Chain LLM node

{
  "type": "@n8n/n8n-nodes-langchain.chainLlm",
  "typeVersion": 1.7,
  "position": [800, 400],
  "id": "chain-1",
  "name": "Classify Email",
  "parameters": {
    "promptType": "define",
    "text": "Classify the following email into one of these categories: newsletter, action_item, spam.\n\nEmail subject: {{ $json.subject }}\nEmail body: {{ $json.body }}\n\nRespond with the category, confidence (high/medium/low), and a brief reason.",
    "hasOutputParser": true
  }
}

Connections

{
  "Claude Model": {
    "ai_languageModel": [[{ "node": "Classify Email", "type": "ai_languageModel", "index": 0 }]]
  },
  "Classification Parser": {
    "ai_outputParser": [[{ "node": "Classify Email", "type": "ai_outputParser", "index": 0 }]]
  }
}

Accessing LLM output

After the chainLlm node executes, the parsed output is available at:

// In a Code node after chainLlm
const result = $('Classify Email').first().json;
const category = result.output.category;
const confidence = result.output.confidence;

For outputParserStructured, the parsed JSON is in the output field of the chain’s result.

Available Claude models

Model	ID	Best for
Claude Haiku 4.5	`claude-haiku-4-5-20251001`	Fast classification, simple extraction
Claude Sonnet 4.6	`claude-sonnet-4-6-20250514`	Complex analysis, generation
Claude Opus 4.6	`claude-opus-4-6-20250527`	Most capable, multi-step reasoning

Use FLEXCRED placeholders for AI provider credentials — they automatically handle org-owned vs. Codika-provided API keys.

Multi-step processing pattern

For workflows that need to process multiple items (e.g., classify each email in a batch):

Fetch Items → Loop Over Items → chainLlm (classify each) → Aggregate → Submit Result

Use n8n’s SplitInBatches or Loop Over Items node to iterate, with the chainLlm inside the loop.

Temperature guidelines

Task	Temperature	Why
Classification	0.0 - 0.3	Deterministic, consistent results
Extraction	0.0 - 0.2	Accurate data extraction
Summarization	0.3 - 0.5	Some creative flexibility
Generation	0.5 - 0.8	Creative, varied output

Common mistakes

Credentials on chainLlm instead of lmChatAnthropic — credentials must be on the model node
Using agent for JSON output — agent adds reasoning text that breaks structured parsers
Missing hasOutputParser: true on chainLlm — required when using outputParserStructured
Accessing output incorrectly — use $('Node Name').first().json.output, not .json directly

Getting Started

Why Codika

Core Concepts

Guides

Builder System

Examples

Overview

Architecture

Basic chainLlm example

LLM Model node

Output Parser node

Chain LLM node

Connections

Accessing LLM output

Available Claude models

Multi-step processing pattern

Temperature guidelines

Common mistakes

​Overview

​Architecture

​Basic chainLlm example

​LLM Model node

​Output Parser node

​Chain LLM node

​Connections

​Accessing LLM output

​Available Claude models

​Multi-step processing pattern

​Temperature guidelines

​Common mistakes

Overview

Architecture

Basic chainLlm example

LLM Model node

Output Parser node

Chain LLM node

Connections

Accessing LLM output

Available Claude models

Multi-step processing pattern

Temperature guidelines

Common mistakes