LLM Node

The LLM node calls AI language models to analyze data, generate insights, and make decisions within your workflow. It supports 9 providers and 30+ models — from fast summarizers to deep reasoning engines.

Configuration

Available Models

Prompt Interpolation

Use double curly braces to inject live data from upstream nodes into your prompts.

Example: Market Analysis Prompt

System Prompt:

You are a crypto market analyst. Analyze the provided price data
and technical indicators.

Respond in this exact format:
ACTION: [BUY / SELL / HOLD]
CONFIDENCE: [0-100]%
RATIONALE: [2-3 sentence explanation]

Be conservative — only recommend BUY when multiple indicators align.

User Prompt:

Analyze BTC/USD right now.

Current price: {{price_data.data.prices[0].current}}
24h change: {{price_data.data.prices[0].changePercent24h}}%
RSI: {{price_data.data.prices[0].indicators.rsi}}

Based on these indicators, what is your recommendation?

Structured Output

Toggle Structured Output to force the model to return a specific JSON schema instead of free-form text. This is useful when you need to feed parsed data directly into Conditional or Function nodes without extra parsing.

Define fields with a name, type (string, number, boolean, array, or object), and whether they're required. Click the type badge to change it. Expand objects and arrays to add nested properties. Try it below — the output preview updates in real time.

{
  "signal": "buy",
  "confidence": 0.85,
  "reasoning": "RSI below 30 indicates oversold"
}

The demo above is pre-populated with a trading signal schema. In your workflow, the LLM will return JSON matching your schema exactly — you can then route it directly into a Conditional node (e.g., check if signal equals "buy" and confidence is greater than 0.7).

Visual Analysis

Connect a Chart Image node to the LLM to enable visual chart analysis. Vision-capable models (GPT-4o, Gemini, Kimi K2.5) can analyze candlestick patterns, support/resistance levels, and trend direction directly from the chart image.

The LLM automatically detects images in the interpolated data — no special configuration needed.

Parsing LLM Output Downstream

The LLM returns a text string by default. To use it in decisions:

• Simple routing: Connect LLM → Conditional. Set Field to llm.output, Operator to "Contains", Value to BUY. The true branch triggers the trade, false branch sends a notification.

• Structured parsing: Enable Structured Output on the LLM node itself, or connect LLM → Function node that parses the text into JSON → Conditional on the parsed fields.

• Multi-model consensus: Run the same data through multiple LLM nodes in parallel, then merge results in a Function node to vote on the final action.

Output

Next Steps

• Conditional Node — Route decisions based on LLM output.
• Function Node — Parse or transform LLM responses with custom code.
• Chart Image Node — Generate charts for visual LLM analysis.
• Credentials — Set up your own API keys for each provider.