DeepSeek Harness

DeepSeek Harness is a plugin based coding agent from DeepSeek. It runs in your terminal as dsh and in a local web UI. Using the Requesty integration, you can:

Access 300+ models from DeepSeek, OpenAI, Anthropic, Google, Mistral and many other providers through one API key.
Keep every request, its cost and its latency in one dashboard.
Apply fallback policies, load balancing and latency routing to your agent runs.
Switch models without reinstalling or reconfiguring the harness.

The Requesty CLI writes this configuration for you: run requesty, select DeepSeek Harness, pick a model, and it backs up your existing files before writing.

Prerequisites

DeepSeek Harness installed on your machine. npx @deepseek-ai/dsh web runs it without a global install, and Node 22.19 or later is required.
A Requesty API key from the API Keys page.

How the configuration works

DeepSeek Harness keeps user configuration in its harness home, which is ~/.dsh unless you set DSH_HOME:

File	Purpose
`~/.dsh/settings.yaml`	Providers, models and the default model.
`~/.dsh/.credentials.yaml`	API keys, referenced by name from `settings.yaml`.

Requesty is an OpenAI compatible endpoint, so it plugs into the harness as a custom provider on the llm-pi-ai plugin. No plugin needs to be installed for this.

Configuration

Install DeepSeek Harness

Run the web UI once to create the harness home:

npx @deepseek-ai/dsh web

You can also install from source. See the DeepSeek Harness README for that path.

Store your Requesty API key

Add your key to ~/.dsh/.credentials.yaml:

REQUESTY_API_KEY: rqsty-sk-....

Replace rqsty-sk-.... with your key from the API Keys page.The harness requires this file to be readable by you only. Set the permissions after creating it:

chmod 700 ~/.dsh && chmod 600 ~/.dsh/.credentials.yaml

Exporting REQUESTY_API_KEY in your shell works as well, and a value from the environment wins over the stored one. Use the file when you want the key to survive across terminals.

Add Requesty as a provider

Create or edit ~/.dsh/settings.yaml:

agent-default-model:
  provider: requesty
  model: deepseek/deepseek-v4-pro-0813

llm-pi-ai:
  providers:
    requesty:
      displayName: Requesty
      apiKeyEnv: REQUESTY_API_KEY
      api: openai-completions
      baseURL: https://router.requesty.ai/v1
      defaultContextWindow: 200000
      defaultMaxTokens: 8192
      headers:
        HTTP-Referer: https://requesty.ai
        X-Title: DeepSeek Harness
      models:
        - id: deepseek/deepseek-v4-pro-0813
          name: DeepSeek V4 Pro (Requesty)
        - id: anthropic/claude-sonnet-4-6
          name: Claude Sonnet 4.6 (Requesty)
          input: [text, image]
        - id: openai/gpt-5.4
          name: GPT-5.4 (Requesty)

What each field does:

Field	Value
`apiKeyEnv`	Name of the credential to use, not the key itself.
`api`	`openai-completions`, the Requesty Chat Completions format.
`baseURL`	`https://router.requesty.ai/v1`, including the `/v1` suffix.
`models`	The models you want in the model picker, each with a full Requesty model id.
`headers`	Optional analytics headers that tag these requests in your dashboard.

Model ids must carry the Requesty provider prefix, for example deepseek/deepseek-v4-pro-0813 rather than deepseek-v4-pro-0813. Ids without a prefix are rejected by the router.

A model that accepts images needs input: [text, image] on its entry. Text only is the default.

Start the harness

Run a task in your terminal:

dsh --profile headless "list the files in this directory"

Or open the web UI:

dsh web

From source, prefix the commands with pnpm, for example pnpm dsh web.

Configuring from the web UI

The web UI writes the same files, so you can skip the YAML:

Run dsh web and open the printed URL.
Go to Settings, then Models.
Choose Add a custom provider and fill in the form:
- Provider id: requesty
- Base URL: https://router.requesty.ai/v1
- Protocol: OpenAI Chat Completions
- API key: your Requesty key
Select Fetch available models to load the Requesty catalog, then pick the models you want.
Select a Requesty model as the default model for new sessions.

Selecting a model

The model picker lists every model on the requesty provider. Add or remove entries under models in settings.yaml to change that list, and set agent-default-model to the model new sessions should start with. Browse ids in the Requesty model library. Policies work here too: use the policy id as the model id, for example policy/coding-fallback.

Verifying the integration

Run one task, then open the Requesty analytics dashboard. The request appears within seconds with its model, token counts and cost. Requests configured with the headers above are tagged DeepSeek Harness, so you can filter your dashboard by this integration. You can also confirm that the harness reaches the router at all:

curl https://router.requesty.ai/v1/models \
  -H "Authorization: Bearer $REQUESTY_API_KEY"

This is the same endpoint the Fetch available models button calls.

Troubleshooting

MISSING_CREDENTIAL: no API key for provider route

The harness found the provider but no key. Check that the name in apiKeyEnv matches a key in ~/.dsh/.credentials.yaml exactly, or export that variable in the shell you start dsh from. The harness reads the environment at launch, so exporting a key after startup has no effect.

Credentials file rejected

The harness refuses to read ~/.dsh/.credentials.yaml if the file is readable by other users. Run chmod 600 ~/.dsh/.credentials.yaml. The file must be a plain mapping of credential name to key, with no wrapper level and no empty values.

404 page not found

The base URL does not match the protocol. Use https://router.requesty.ai/v1 with api: openai-completions. For api: anthropic-messages, use https://router.requesty.ai without the /v1 suffix, because the harness appends /v1/messages itself.

Model not found

Add the provider prefix to the id, for example openai/gpt-5.4. Confirm the id in the model library, and check approved models if your organization restricts which models a key may use.

Changes to settings.yaml do not apply

The harness watches its settings file, so most edits take effect immediately. If a run still uses the old provider, stop the process and start it again. Confirm you edited the file under the harness home in use, which is $DSH_HOME when that variable is set.

Requesty CLI

Coding Assistants

Desktop & Agents

Chat Platforms

Automation & Workflows

SDKs & Frameworks

Prerequisites

How the configuration works

Configuration

Configuring from the web UI

Selecting a model

Verifying the integration

Troubleshooting

References

​Prerequisites

​How the configuration works

​Configuration

​Configuring from the web UI

​Selecting a model

​Verifying the integration

​Troubleshooting

​References

Prerequisites

How the configuration works

Configuration

Configuring from the web UI

Selecting a model

Verifying the integration

Troubleshooting

References