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DeepSeek Harness Integration

Use NanoGPT with DeepSeek Harness (dsh), DeepSeek’s open source agent harness. Add NanoGPT as a custom model API to run DeepSeek V4 Pro, V4 Flash, V4.1 Flash, or any other NanoGPT text model, billed to your NanoGPT balance or subscription.
DeepSeek Harness is in developer preview and its settings can change between releases. This guide was tested with @deepseek-ai/dsh 0.2.1-rc.2. Read DeepSeek’s safety notice before you let it run commands on your machine.

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Requirements

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Start DeepSeek Harness

This starts the web UI at http://127.0.0.1:3080 and opens it in your browser.

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Add NanoGPT as a Model Provider

1

Open the custom provider form

Go to Settings → Models, choose Add model provider, and switch the card to Custom model API. NanoGPT is not in DeepSeek Harness’s built-in provider list, so do not look for it under Third-party model provider.
2

Enter the connection details

The Provider ID cannot be changed later, because saved sessions refer to it.Use the v1legacy base URL. It returns DeepSeek’s reasoning in the reasoning_content field, which is DeepSeek’s own field name, and DeepSeek Harness can send it back in that field on the next turn. When a response also carries structured reasoning_details, DeepSeek Harness may send those back instead. More of NanoGPT’s DeepSeek routes pass reasoning_content back to the model than the reasoning field that the standard /api/v1 endpoint returns, so earlier thinking is kept across tool calls more often. Whether it is kept still depends on the route that serves the request.
3

Add the DeepSeek models

Under the model catalog, choose Fetch available models, search for deepseek, tick the models you want, and add them. You can also type the model IDs by hand:
  • deepseek/deepseek-v4-pro
  • deepseek/deepseek-v4-flash
  • deepseek/deepseek-v4.1-flash
Use these base IDs rather than the :thinking variants. The Effort menu in DeepSeek Harness turns thinking on and off.
4

Set each model's options

In the model list, expand each model’s Model options and enter:Turn on Reasoning levels to edit the levels. Delete the rows the model does not list, and use Add level to add Max. Type none as the API value for Off, so that choosing Off sends reasoning_effort: "none" and switches thinking off on every route.Choose Create provider. To change these settings later, edit the provider and open Customized settings.
5

Pick a model

Select a NanoGPT model in the model picker and choose an Effort level. The model you pick becomes the default for new sessions.

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Why these values

  • Max output tokens: DeepSeek Harness sends this value as max_completion_tokens on every request. For pay-as-you-go requests, NanoGPT reserves enough balance to cover it, so a smaller limit keeps more of your balance available for parallel requests and subagents. 65,536 tokens covers almost every agent step. The models allow up to 384,000 output tokens, so raise it if long answers stop early. Thinking tokens count toward this limit.
  • Context window: Fetch available models reads NanoGPT’s plain model list, which does not include context sizes, so enter them yourself. Otherwise this release of DeepSeek Harness assumes 262,144 context tokens and 32,768 output tokens, and starts compacting the conversation long before DeepSeek’s 1M-token limit.
  • Image input: DeepSeek V4.1 Flash accepts images on NanoGPT, so DeepSeek Harness’s image reading works with it. V4 Pro and V4 Flash are text only.

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Configure with a File

You can make the same settings in ~/.dsh/profiles/web/cordis.patch.yml instead of the web form (use $DSH_HOME instead of ~/.dsh if you set it). The web UI reads this file on the next request. This example takes the key from the NANOGPT_API_KEY environment variable:
Start dsh web from a shell where NANOGPT_API_KEY is set, or leave out apiKeyEnv and save the key on the Models page. Quote "off" as shown. Thinking stays off until you choose an effort in the model picker. If the file already has an llm-pi-ai entry, add nanogpt under its existing providers. The configuration you write for llm-pi-ai replaces that plugin’s whole configuration, so keep your other providers in the same list.

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Headless runs

dsh headless answers one task and exits. It uses its own profile, so put the same llm-pi-ai entry in ~/.dsh/profiles/headless/cordis.patch.yml and add the default model below it:
reasoningEffort must be one of the model’s reasoning levels. Leave it out to run with thinking off. Then run:

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Other NanoGPT Models

Any NanoGPT text model with tool calling can be added to the same provider. Find model IDs, context sizes, output limits, and supported reasoning levels with:
Use each model’s context_length and max_output_tokens for the model options, and its reasoning_efforts list for the reasoning levels. NanoGPT’s none effort maps to the Off level. Leave reasoning levels off for models that do not list any. Do not add a provider-wide reasoning setting to the nanogpt provider when it includes models without reasoning levels. DeepSeek Harness applies it to every model on the provider and rejects requests to the others with UNSUPPORTED_REASONING_EFFORT.

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Do Not Point the DeepSeek Card at NanoGPT

The built-in DeepSeek card on the Models page is made for DeepSeek’s own API, so changing its base URL to NanoGPT does not work:
  • It uses DeepSeek’s model IDs, such as deepseek-flash, which NanoGPT rejects with model_not_supported.
  • It requests thinking without a token budget, which NanoGPT’s Anthropic-compatible endpoint rejects with Invalid thinking.budget_tokens.
Use the custom model API described above.

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Troubleshooting