Use LithosAI with DeepSeek Harness

Before you begin

Install DeepSeek Harness, create a key on the API Keys page, and choose a model on the Models page.

DeepSeek Harness is in developer preview, and its configuration format can change between releases. These steps were tested with versions 0.2.0-rc.2 and 0.2.1-rc.2.

Set your API key

Export your key in the shell where you start DeepSeek Harness:

export LITHOSAI_API_KEY="your-key"

The desktop app reads your login shell’s environment, so add this line to ~/.zshrc and restart the app. After you configure LithosAI in the next step, you can instead enter the key in Settings → Models: edit LithosAI, enter the key, and select Apply. DeepSeek Harness stores it in ~/.dsh/.credentials.yaml.

Configure LithosAI

The configuration uses moonshotai/Kimi-K3-fast as an example.Add the configuration to the profile for the way you run DeepSeek Harness:

dsh web: ~/.dsh/profiles/web/cordis.patch.yml
Desktop app: ~/.dsh/profiles/desktop/cordis.patch.yml
dsh headless: ~/.dsh/profiles/headless/cordis.patch.yml

llm-pi-ai is the plugin DeepSeek Harness uses for third-party model providers. DeepSeek Harness also writes to this file, for example when you change the model or effort. If the file already has an llm-pi-ai entry, don’t add a second one. Copy only the lithosai: block into that entry’s providers. If it already has an agent-default-model entry, change that entry instead. Set the token limits to match your model.

DeepSeek Harness configuration

cordis.patch.yml

- id: llm-pi-ai
  config:
    providers:
      lithosai:
        displayName: LithosAI
        apiKeyEnv: LITHOSAI_API_KEY
        api: openai-completions
        baseURL: https://api.lithosai.cloud/v1
        compat:
          supportsReasoningEffort: true
          maxTokensField: max_tokens
        models:
          - id: moonshotai/Kimi-K3-fast
            name: moonshotai/Kimi-K3-fast
            contextWindow: 1048576
            maxTokens: 32768
            reasoningEfforts:
              low: low
              high: high
              max: max
- id: agent-default-model
  config:
    provider: lithosai
    model: moonshotai/Kimi-K3-fast
    reasoningEffort: high

To add another model or tier, copy the entry under providers.lithosai.models. Set id and name to the ID from the model catalog, and adjust contextWindow and maxTokens to match the model. Restart DeepSeek Harness to load the changes.

Start a conversation

Start DeepSeek Harness from the profile you configured.Web UI: Run the following command:

npx @deepseek-ai/dsh web

The Web UI opens in your browser. If it doesn’t, open the URL that the command prints. The URL includes an access token.Desktop app: Open the app. If it shows a welcome screen, select Add API Key, then Configure later. You don’t need a DeepSeek account.Headless: Run DeepSeek Harness in your project directory with the task as an argument. It prints the answer and exits.

npx @deepseek-ai/dsh headless "Summarize this repository"

Model and reasoning effort

Model and tier: Open the model menu in the message box and choose Model.
Reasoning effort: In the same menu, choose Effort and select Low, High, or Max. The configuration starts at High. The level you select becomes the new default.
Headless: There is no menu. Change model and reasoningEffort in the agent-default-model entry of the headless profile.

Issues common to every agent are listed under Common issues.

Getting Started

Inference APIs

Account

Use LithosAI with DeepSeek Harness

Before you begin

Model and reasoning effort

Resources

​Before you begin

​Model and reasoning effort

​Resources

Before you begin

Model and reasoning effort

Resources