Skip to main content
The OpenAI LLM node connects your workflows to OpenAI’s GPT family of language models. Use it to generate text responses, analyze documents, answer questions grounded in knowledge bases, or produce structured JSON outputs — for example, drafting investment memos from research notes, classifying financial transactions, or extracting key terms from contracts using the latest GPT models.

​
Core Functionality

  • Generate text completions and conversational responses using GPT models
  • Process system instructions and dynamic prompts with variable interpolation
  • Stream responses in real time for long-running generations
  • Return structured JSON output with optional schema enforcement
  • Track token usage and credit consumption per run
  • Apply content moderation, PII detection, and safety guardrails
  • Retry failed executions automatically with configurable intervals

​
Tool Inputs

  • System Instructions — (String) Instructions that guide the model’s behavior, tone, and how it should use data provided in the prompt
  • Prompt — (String) The data sent to the model. Type {{ to open the variable builder and reference outputs from other nodes
  • Model * — (Enum (Dropdown), default: gpt-5.1) Select from available GPT models. Click Dropdown to view all options
  • Use Personal Api Key — (Boolean, default: No) Toggle to use your own OpenAI API key instead of VectorShift’s shared key
  • Api Key — (String) Your OpenAI API key. Only visible when Use Personal Api Key is enabled
  • JSON Schema — (String) JSON schema to enforce structured output format. Only visible when JSON Response is enabled
* indicates a required field

​
Tool Outputs

  • response — (String (or Stream<String> when streaming)) The generated text response from the model
  • prompt_response — (String) The combined prompt and response content
  • tokens_used — (Integer) Total number of tokens consumed (input + output)
  • input_tokens — (Integer) Number of input tokens sent to the model
  • output_tokens — (Integer) Number of output tokens generated by the model
  • credits_used — (Decimal) VectorShift AI credits consumed for this run

​
Overview

The OpenAI LLM node in workflows lets you place a GPT model directly on the canvas, wire inputs and outputs to other nodes, and configure model behavior through the settings panel. OpenAI models are widely adopted and offer strong general-purpose performance across a range of financial and analytical tasks.

​
Use Cases

  • Investment memo drafting — Generate structured investment memos by combining analyst research notes, market data, and portfolio context into a coherent summary.
  • Financial transaction classification — Categorize transactions by type, risk level, or regulatory relevance using natural language understanding.
  • Contract term extraction — Extract key terms, obligations, and dates from legal and financial contracts using JSON mode for structured output.
  • Client communication generation — Draft personalized portfolio update emails by combining market data with client-specific holdings and preferences.
  • Regulatory Q&A — Build chatbots that answer compliance questions grounded in your organization’s policy knowledge base.

​
How It Works

  1. Add the node to your workflow. From the toolbar, open the AI category and drag the OpenAI node onto the canvas.
OpenAI node being dragged onto the canvas from the AI category
  1. Write your System Instructions. Enter instructions in the System Instructions field to define the model’s behavior, tone, and how it should use any data provided in the prompt.
  2. Configure the Prompt. In the Prompt field, type {{ to open the variable builder and reference outputs from upstream nodes.
  3. Select a model. Use the Model dropdown to choose a GPT model. Available options include gpt-5.1, gpt-5, gpt-5-mini, gpt-5-nano, gpt-5.1-codes, o4-mini, gpt-4.1, gpt-4.1-mini, chatgpt-4o-latest, gpt-4o, gpt-4o-mini, and others.
OpenAI node showing the Model dropdown with available GPT models
  1. Open settings. Click the gear icon (⚙) to configure token limits, temperature, reasoning effort, retry behavior, and more.
OpenAI node settings panel showing token limits, temperature, and other configuration options
  1. Connect outputs. Click the Outputs button to open the outputs panel. Wire the response output to downstream nodes.
OpenAI node connected to upstream and downstream nodes on the canvas
  1. Run your workflow. Execute the pipeline to process inputs and return the generated response along with usage metrics.

​
Settings

All settings below are accessed via the gear icon (⚙) on the node.
SettingTypeDefaultDescription
ProviderDropdownOpenAIThe LLM provider.
Max TokensInteger128000Maximum number of input + output tokens the model will process per run.
Reasoning EffortDropdownDefaultControls the depth of reasoning the model applies to its response.
VerbosityDropdownDefaultControls the verbosity of model responses.
TemperatureFloat0.5Controls response creativity. Higher values produce more diverse outputs; lower values produce more deterministic responses. Range: 0–1.
Top PFloat0.5Controls token sampling diversity. Higher values consider more tokens at each generation step. Range: 0–1.
Stream ResponseBooleanOffStream responses token-by-token.
JSON ResponseBooleanOffReturn output as structured JSON. When enabled, a JSON Schema input appears.
Show SourcesBooleanOffDisplay source documents used for the response.
Toxic Input FiltrationBooleanOffFilter toxic input content.
Safe Context Token WindowBooleanOffAutomatically reduce context to fit within the model’s maximum context window.
Retry On FailureBooleanOffEnable automatic retries when execution fails.
Max RetriesInteger—Maximum retry attempts. Visible when Retry On Failure is enabled.
Max Interval b/w re-tryInteger—Interval in milliseconds between retry attempts.
PII Detection
NameBooleanOffDetect and redact personal names from input.
EmailBooleanOffDetect and redact email addresses from input.
PhoneBooleanOffDetect and redact phone numbers from input.
SSNBooleanOffDetect and redact Social Security numbers from input.
Credit CardBooleanOffDetect and redact credit card numbers from input.
Show Success/Failure OutputsBoolean—Display additional success and failure output ports on the node.

​
Best Practices

  • Select the right model for your task. Use gpt-5.1 or gpt-5 for complex reasoning; gpt-5-mini or gpt-4o-mini for cost-effective batch processing of simpler tasks.
  • Use JSON mode for structured extraction. Enable JSON Response and provide a schema for consistent, machine-readable output when extracting financial data.
  • Monitor token usage. Connect tokens_used and credits_used outputs to tracking nodes, especially for high-volume batch processing.
  • Enable Safe Context Token Window for variable-length inputs. Prevents token-limit errors when processing documents of unpredictable size.
  • Use streaming for interactive interfaces. Enable streaming when powering client-facing chatbots for responsive user experience.
  • Apply PII detection for regulated data. Enable SSN and other PII toggles when processing client financial records.

Document Classification Agent

Automatically categorizes and tags incoming documents based on content and type.

Contract AI Analyst

Analyzes contracts to extract key terms, flag risks, and summarize obligations.

Refund/Expense Approval AI Agent

Reviews and routes refund or expense requests based on policy rules and approval thresholds.

Customer Support Chatbot

Handles common customer inquiries and support tickets through conversational AI.

​
Common Issues

For troubleshooting common issues with this node, see the Common Issues documentation.