Harness AI with OpenCheese

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Harness AI Desktop (DeepSeek Harness) adds OpenCheese as a custom provider beside its built-in DeepSeek one, speaking the OpenAI Chat Completions API at https://api.opencheese.dev/v1.

Harness keeps everything in one folder: %USERPROFILE%\.dsh on Windows, ~/.dsh on Linux and macOS, or wherever $DSH_HOME points.

01Install Harness AI

Download the build for your platform from the Harness AI repository and finish the installer.

02Add OpenCheese as a custom provider

1. Open Settings from the sidebar and select Models.
2. Click Add a custom provider and fill in:

3. Click Fetch available models, keep every model selected and click Create provider.

• The Provider ID must be exactly opencheese: Harness names the stored credential after it (OPENCHEESE_API_KEY), and any other spelling points the provider at a credential nothing sets.
• The Base URL keeps its /v1: Harness appends the endpoint path itself.
• The dashboard's Configuration page, Harness AI tab, shows the same values and reveals the key after you confirm its password.

OpenCheese now appears in the Models list with a Custom badge and a green dot, and its models can be picked in a chat.

03Add reasoning levels (optional)

The Settings page cannot set reasoning levels, so the level picker stays empty until settings.yaml declares them. Merge this block into %USERPROFILE%\.dsh\settings.yaml (~/.dsh/settings.yaml), keeping every other section the file already has, then restart Harness AI. The Harness AI tab generates it with every model your plan includes.

llm-pi-ai:
    providers:
        opencheese:
            apiKeyEnv: OPENCHEESE_API_KEY
            api: openai-completions
            baseURL: https://api.opencheese.dev/v1
            timeoutMs: 120000
            models:
                - id: MODEL_ID
                  name: Model name
                  contextWindow: 1000000
                  maxTokens: 131000
                  reasoningEfforts:
                      max: max
                      medium: medium
                      low: low

• Take contextWindow and maxTokens from the model's context_window and max_output_tokens in /v1/models, and the levels from its supported_reasoning_levels. Both numbers must be positive integers.
• Harness accepts only the levels off, minimal, low, medium, high, xhigh and max. One other name, or a missing number, makes Harness drop the whole llm-pi-ai section, and every OpenCheese model disappears at once.
• A model without levels gets no reasoningEfforts key: it reasons at its default.
• The key is not in this file: apiKeyEnv names the credential Harness stored in step 2.

Set up without the Settings page

To skip step 2, save the credential yourself as .credentials.yaml in the same folder as settings.yaml, next to the block above:

version: 1
refs:
    OPENCHEESE_API_KEY: YOUR_OPENCHEESE_API_KEY

• version: 1 is required.
• On Linux and macOS run chmod 600 ~/.dsh/.credentials.yaml: Harness refuses a credential file other users can read.
• Chrome, Edge and other Chromium browsers save a downloaded .credentials.yaml as credentials.yaml; rename it back.
• This file holds your key in plain text. Never commit it to a repository.

Usage and costs

Every request appears under Transactions in the dashboard, grouped as one session per conversation, with its token usage and cost.