API Access¶

uniGPT provides an OpenAI-compatible API that allows researchers and developers to integrate AI models into their own applications, scripts, and workflows.

The API is available at:

https://gpt.uni-muenster.de/v1

Getting an API Key¶

To use the API, you need an API key (starting with sk-...). API keys are currently available for two use cases:

Research Projects¶

API keys for research projects are issued upon request. Contact the uniGPT team via our support channel:

uniGPT Support on Mattermost

Please briefly describe your project and intended usage so we can set appropriate quotas.

AI-Assisted Coding (OpenCode, Roo Code, etc.)¶

API keys for use with coding assistants are available through a self-service portal at https://gpt-api.uni-muenster.de, where you can generate your own key without manual approval. The portal is open to all employees. A small guide for OpenCode and VSCode is shown below.

Currently the following models are enabled for all employees via the portal:

• DeepSeek-v4-flash
• Qwen3.8-27B

Additional models are enabled on request — contact the team via the support channel. If you generated your key before a model was added, you may need to regenerate your key to pick it up.

Using the Python OpenAI Client¶

Install the OpenAI Python library:

pip install openai

Basic Example¶

from openai import OpenAI

client = OpenAI(
    api_key="sk-YOUR_API_KEY",
    base_url="https://gpt.uni-muenster.de/v1"
)

completion = client.chat.completions.create(
    model="gemma-4-31B-it",
    messages=[
        {"role": "user", "content": "What is the capital of France?"}
    ]
)

print(completion.choices[0].message.content)

Streaming Example¶

stream = client.chat.completions.create(
    model="gemma-4-31B-it",
    messages=[{"role": "user", "content": "Explain quantum computing briefly."}],
    stream=True,
)

for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

Using curl¶

curl https://gpt.uni-muenster.de/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-YOUR_API_KEY" \
  -d '{
    "model": "gemma-4-31B-it",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Checking Your Key & Quotas¶

You can inspect your API key and its quotas using the /key/info endpoint:

curl https://gpt.uni-muenster.de/key/info \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-ENTER-KEY-HERE"

This returns information about your key and the configured quotas/limits.

Limits, Concurrency & Caching¶

• Parallel requests: By default, each API key allows 4 parallel requests. Contact the team if you need more.
• Input caching: Prompt caching is enabled, but the discount is not yet billed — you currently pay the full input price even for cached tokens.
• Reasoning: Reasoning is returned via the OpenAI Chat Completions format as the reasoning_content field alongside content.

Available Models via API¶

You can use any of the internal models listed on the Available Models page by specifying their name in the model parameter, including specialized models such as medgemma-1.5-4b-it for medical text and image tasks. Through the self-service portal, DeepSeek-v4-flash and Qwen3.8-27B are currently enabled for all employees; additional models are enabled on request.

Using Coding Assistants¶

You can connect uniGPT to your ide and coding agents via the OpenAI-compatible API. You will need an API key from the self-service portal.

Visual Studio Code¶

Since version 1.104, Visual Studio Code can connect to uniGPT in the built-in Chat view via a custom provider. No extra extension is required.

This uses VS Code's custom endpoint provider. Chat works with your uniGPT API key and does not require a GitHub Copilot plan.

1. Open the Command Palette (Ctrl+Shift+P / Cmd+Shift+P) and run Chat: Manage Language Models.
2. Select Add Models, then Custom Endpoint.
3. Enter a group name such as uniGPT and paste your uniGPT API key when prompted.
4. Select Chat Completions as the API type.
5. VS Code opens the chatLanguageModels.json file. Fill in the model fields as shown below and save it. Do not replace the apiKey value. Keep the ${input:...} variable that VS Code generated.

[
  {
    "name": "uniGPT",
    "vendor": "customendpoint",
    "apiKey": "${input:chat.lm.secret.xxxxxxxxx}",
    "apiType": "chat-completions",
    "models": [
      {
        "id": "DeepSeek-V4-Flash",
        "name": "uniGPT DeepSeek V4 Flash",
        "url": "https://gpt.uni-muenster.de/v1/chat/completions",
        "toolCalling": true,
        "vision": true,
        "maxInputTokens": 128000,
        "maxOutputTokens": 8000
      },
      {
        "id": "Qwen3.8-27B",
        "name": "uniGPT Qwen3.8 27B",
        "url": "https://gpt.uni-muenster.de/v1/chat/completions",
        "toolCalling": true,
        "vision": false,
        "maxInputTokens": 128000,
        "maxOutputTokens": 8000
      }
    ]
  }
]

Set id to an internal model name from the Available Models page. You can add further objects to the models array to switch between them in the picker.

Use maxInputTokens 128000 and maxOutputTokens 8000. For a multimodal model such as DeepSeek V4 Flash, set "vision": true.

If the model does not appear in the picker immediately, restart VS Code.

OpenCode¶

OpenCode can use uniGPT as a custom OpenAI-compatible provider. You need an API key from the self-service portal.

1. Start OpenCode and run /connect.
2. Select Other, then enter unigpt as the provider ID.
3. Paste your uniGPT API key when prompted. OpenCode stores it in its auth file. Do not put the key in opencode.json.
4. Create or update your OpenCode config (~/.config/opencode/opencode.json for all projects, or opencode.json in a project root):

{
  "$schema": "https://opencode.ai/config.json",
  "model": "unigpt/DeepSeek-V4-Flash",
  "provider": {
    "unigpt": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "uniGPT",
      "options": {
        "baseURL": "https://gpt.uni-muenster.de/v1"
      },
      "models": {
        "DeepSeek-V4-Flash": {
          "name": "uniGPT DeepSeek V4 Flash",
          "limit": {
            "context": 128000,
            "output": 8000
          }
        },
        "Qwen3.8-27B": {
          "name": "uniGPT Qwen3.8 27B",
          "limit": {
            "context": 128000,
            "output": 8000
          }
        }
      }
    }
  }
}

The provider ID in /connect must match the key in provider (unigpt). Use @ai-sdk/openai-compatible because uniGPT speaks the Chat Completions API.

If you already have an opencode.json, merge the provider.unigpt block into it rather than replacing the whole file.

1. Run /models and select uniGPT DeepSeek V4 Flash.