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The Custom LLM node lets you connect your workflows to any model provider that supports the OpenAI Chat API format, or to a locally hosted LLM. Use it to access specialized models from providers like Together AI, Replicate, or Fireworks — or connect to a local model running via LM Studio or Ollama — enabling use cases such as running proprietary fine-tuned models for financial analysis, evaluating open-source models for cost optimization, or prototyping with local models before scaling to production.

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Core Functionality

  • Connect to any LLM provider compatible with the OpenAI Chat API format
  • Access local models via LM Studio, Ollama, or other local serving frameworks
  • Specify a custom base URL, model name, and API key
  • Process system instructions and dynamic prompts with variable interpolation
  • Stream responses in real time for long-running generations
  • Track token usage and credit consumption per run
  • Apply content moderation, PII detection, and safety guardrails
  • Retry failed executions automatically with configurable intervals

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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 * — (String (Text input)) The model identifier to use. This is a free-text field — enter the exact model name as specified by your provider
  • Use Personal Api Key — (Boolean, default: No) Toggle to provide your own API key
  • Base URL * — (String) The base URL of your model provider (e.g., https://api.together.xyz or http://localhost:1234/v1)
  • Api Key — (String) Your API key for the model provider. Required when Use Personal Api Key is enabled
* indicates a required field

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Tool Outputs

  • response — (String (or Stream<String> when streaming)) The generated text response from the model
  • 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

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Overview

The Custom LLM node in workflows lets you connect to any OpenAI-compatible model endpoint by specifying a base URL, model name, and API key. This provides maximum flexibility — you can use commercial API providers, privately hosted models, or local development servers, all within the same workflow canvas.

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Use Cases

  • Fine-tuned model deployment — Connect to a custom fine-tuned model hosted on Together AI or Replicate that’s been trained on your organization’s financial documents and terminology.
  • Local model prototyping — Test and iterate with locally hosted models via LM Studio or Ollama before committing to a cloud provider for production workloads.
  • Cost-optimized batch processing — Route high-volume, low-complexity tasks (like transaction tagging) to cost-effective open-source models while reserving premium models for complex analysis.
  • Multi-provider evaluation — Compare outputs from different model providers by swapping the base URL and model name to find the best quality-to-cost ratio for your financial workflows.
  • Private infrastructure compliance — Connect to models hosted within your organization’s private infrastructure to meet data residency and security requirements.

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How It Works

  1. Add the node to your workflow. From the toolbar, open the AI category and drag the Custom node onto the canvas.
Custom node being dragged onto the canvas
  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. Enter the model name. In the Model field, type the exact model identifier as specified by your provider (e.g., meta-llama/Llama-3-70b-chat-hf for Together AI, or local-model for LM Studio).
  4. Set the Base URL. Enter the base URL for your model provider in the Base URL field. Examples:
    • Together AI: https://api.together.xyz
    • Replicate: https://api.replicate.com
    • Local LM Studio: http://localhost:1234/v1
  5. Provide an API key (optional). Toggle Use Personal Api Key to Yes and enter your provider’s API key. For local models, you may use a placeholder key (e.g., lm-studio).
  6. Open settings. Click the gear icon (⚙) on the node to configure token limits, temperature, retry behavior, and more.
Custom node settings panel
  1. Connect outputs and run. Wire the response output to downstream nodes. Execute the pipeline to process inputs through your custom model endpoint.

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Settings

All settings below are accessed via the gear icon (⚙) on the node.
SettingTypeDefaultDescription
ProviderDropdownCustomThe LLM provider.
Max TokensInteger128000Maximum number of input + output tokens the model will process per run.
Reasoning EffortDropdownDefaultControls the depth of reasoning the model applies. Options: Default, Low, Medium, High.
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 instead of returning the full response at once.
Show SourcesBooleanOffDisplay source documents used for the response. Useful when combining with knowledge base inputs.
Toxic Input FiltrationBooleanOffFilter toxic input content. If the model receives toxic content, it responds with a respectful message instead.
Safe Context Token WindowBooleanOffAutomatically reduce context to fit within the model’s maximum context window.
Retry On FailureBooleanOffEnable automatic retries when execution fails.
Max # of re-tryInteger—Maximum number of 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 before sending to the model.
EmailBooleanOffDetect and redact email addresses from input.
PhoneBooleanOffDetect and redact phone numbers from input.
Credit Card InfoBooleanOffDetect and redact credit card numbers from input.
Show Guardrail StatusDropdown—Controls whether guardrail status is included in the output.

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Best Practices

  • Verify base URL format. Ensure your base URL does not include a trailing slash and matches the provider’s API documentation exactly. A common mistake is including extra path segments.
  • Match model names precisely. The model identifier must exactly match what the provider expects. Check your provider’s model catalog for the correct string.
  • Start with local models for prototyping. Use LM Studio or Ollama during development to iterate quickly without incurring API costs, then swap to a cloud provider for production.
  • Set appropriate Max Tokens. Different custom models have vastly different context windows. Set Max Tokens to match your model’s actual limit to avoid errors.
  • Enable retry for unreliable endpoints. If connecting to a self-hosted or development server, enable Retry On Failure with reasonable intervals to handle transient failures.
  • Apply PII detection for client data. Even when using private infrastructure, enable PII toggles as a defense-in-depth measure for workflows processing sensitive financial information.

Custom API Chatbot

A configurable chatbot that connects to custom APIs to retrieve and present dynamic data.

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Common Issues

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