Proposed node name
OllamaInference
What should the node do?
Calls a self-hosted Ollama instance via its OpenAI-compatible endpoint, as a local/no-cost alternative to LlmInference (which only supports OpenRouter). Self-contained, no separate config node, since there's no API key to externalise (Ollama needs no auth). Configurable base URL defaults to http://127.0.0.1:11434/v1 for the common localhost case.
Inputs and outputs
inputs:
- prompt: String (required)
- systemPrompt: String (optional)
config fields:
- model: String (required) — Ollama model tag, e.g. qwen2.5-coder:7b
- baseUrl: String (optional, default http://127.0.0.1:11434/v1)
- temperature, maxTokens, topP: Number (optional)
- parseJson: Boolean (optional)
outputs:
- response: MustOverride (String, or Dict when parseJson is set)
Use case
Running a self-hosted personal AI server (Ollama + local models) and want to use Weft for orchestration without routing every LLM call through a paid provider. Right now LlmInference only supports OpenRouter, so there's no way to wire a graph node directly to a local model, this closes that gap for anyone running Ollama, which is one of the most common local-LLM setups.
Current workaround (if any)
ExecPython with a manual Python requests call to Ollama's API works, but it's untyped, doesn't get the dashboard's node UI/validation, and has to be re-written per project instead of being a reusable typed node.
Proposed node name
OllamaInference
What should the node do?
Calls a self-hosted Ollama instance via its OpenAI-compatible endpoint, as a local/no-cost alternative to LlmInference (which only supports OpenRouter). Self-contained, no separate config node, since there's no API key to externalise (Ollama needs no auth). Configurable base URL defaults to http://127.0.0.1:11434/v1 for the common localhost case.
Inputs and outputs
Use case
Running a self-hosted personal AI server (Ollama + local models) and want to use Weft for orchestration without routing every LLM call through a paid provider. Right now LlmInference only supports OpenRouter, so there's no way to wire a graph node directly to a local model, this closes that gap for anyone running Ollama, which is one of the most common local-LLM setups.
Current workaround (if any)
ExecPython with a manual Python
requestscall to Ollama's API works, but it's untyped, doesn't get the dashboard's node UI/validation, and has to be re-written per project instead of being a reusable typed node.