Document updated on Sep 17, 2025

Mistral Integration

The Mistral integration enables KrakenD to talk to Mistral’s AI services, allowing you to embed powerful language model capabilities into your API workflows without custom coding. Use this interface when you want to connect Mistral for tasks like intelligent automation, conversational AI, content generation, or other LLM-driven functionalities inside your existing API infrastructure.

This component abstracts the complexities of communicating with Mistral’s AI API. When an API request hits your KrakenD endpoint configured with the Mistral interface, KrakenD automatically constructs the necessary Mistral API payload, handles authentication, and processes the responses uniformly. This consistent integration also allows if needed to switch between different LLM vendors without changing client-facing interfaces.

In essence, the user sends the textual content or instructions, and KrakenD manages the complete interaction with Mistral behind the scenes.

Configuration of the Mistral integration

To enable the Mistral interface, you add a backend configuration in KrakenD with the extra_config key under the ai/llm namespace. Example:

{
  "endpoint": "/mistral",
  "method": "POST",
  "backend": [
    {
      "host": [
        "https://api.mistral.ai"
      ],
      "url_pattern": "/v1/chat/completions",
      "method": "POST",
      "extra_config": {
        "ai/llm": {
          "mistral": {
            "v1": {
              "credentials": "xxxxxxxxx",
              "debug": false,
              "variables": {
                "model": "mistral-small-latest"
              }
            }
          }
        }
      }
    }
  ]
}

To interact with the LLM, the user can send in the request:

• instructions (optional): If you want to add a system prompt
• contents: The content you want to send to the template

Like this:

$curl -XPOST --json '{"instructions": "Act as a 1000 dollar consultant", "contents": "Tell me a consultant joke"}' http://localhost:8080/mistral

The configuration options are:

This design keeps user input clean and focused only on content, while KrakenD handles building the full API request.

Customizing the payload sent and received from Mistral

As it happens with all LLM interfaces of KrakenD, you can completely replace the request and the response so you have a custom interaction with the LLM. While the default template should allow you to accomplish any day to day job, you might need to extend it using your own template.

You may override the input and output Go templates by specifying:

• input_template: Path to a custom template controlling how the request data is formatted before sending to Mistral.
• output_template: Path to a custom template to transform and extract the desired pieces from Mistral’s response.

See below how to change this interaction

Default input_template for Mistral v1

When you don’t set any input_template, KrakenD will create the JSON payload sent to Mistral using the following template:

{
	"model": {{ .variables.model | toJson }},
	{{ $temperature :=  .variables.temperature }}{{ if ge $temperature 0.0 }}"temperature": {{ $temperature }},{{ end }}
	{{ $top_p := .variables.top_p }}{{ if ge $top_p 0.0 }}"top_p": {{ $top_p }},{{ end }}
	{{ $max_tokens := .variables.max_tokens }}{{ if ge $max_tokens 0 }}"max_tokens": {{ $max_tokens }},{{ end }}
	{{ $random_seed := .variables.random_seed }}{{ if ge $random_seed 0 }}"random_seed": {{ $random_seed }},{{ end }}
	"n":  {{ .variables.n }},
	"stream": false,
	"stop":  {{ .variables.stop | toJson }},
	"safe_prompt":  {{ .variables.safe_prompt }},
	"messages": [
	{{- if hasKey .req_body "instructions" }}
		{
			"role": "system",
			"content": {{ .req_body.instructions | toJson }}
		},
	{{- end }}
		{
			"role": "user",
			"content": {{ .req_body.contents | toJson }}
		}
	]
}

Default output_template for Mistral v1

When you don’t declare an output_template, the response from the AI is transformed to have the following format:

{
	"ai_gateway_response":
	[
		{{ if gt (len .resp_body.choices) 0 }}
		{
			"contents": [
				{{- range $index, $choice := .resp_body.choices }}
				{{- if $index }},{{ end }}
				{{ $choice.message.content | toJson }}
				{{- end }}
			]
		}
		{{ end }}
	],
	"usage": "{{ .resp_body.usage.total_tokens }}"
}

If you are not confortable receiveing the response in the ai_gateway_response field, you can add to the backend a mapping attribute to use another name, without changing the template:

{
	"url_pattern": "...",
	"mapping": {
		"ai_gateway_response": "my_response"
	}
}