Configuration Reference - TensorZero Docs

The configuration file is the backbone of TensorZero. It defines the behavior of the gateway, including the models and their providers, functions and their variants, tools, metrics, and more. Developers express the behavior of LLM calls by defining the relevant prompt templates, schemas, and other parameters in this configuration file. You can see an example configuration file here. The configuration file is a TOML file with a few major sections (TOML tables): gateway, clickhouse, models, model_providers, functions, variants, tools, and metrics.

`[gateway]`

The [gateway] section defines the behavior of the TensorZero Gateway.

`base_path`

Type: string
Required: no (default: /)

If set, the gateway will prefix its HTTP endpoints with this base path. For example, if base_path is set to /custom/prefix, the inference endpoint will become /custom/prefix/inference instead of /inference.

`bind_address`

Type: string
Required: no (default: [::]:3000)

Defines the socket address to bind the TensorZero Gateway to. You can bind the gateway to IPv4 and/or IPv6 addresses. To bind to an IPv6 address, you can set this field to a value like [::]:3000. Depending on the operating system, this value binds only to IPv6 (e.g. Windows) or to both (e.g. Linux by default).

// tensorzero.toml
[gateway]
# ...
bind_address = "0.0.0.0:3000"
# ...

`debug`

Type: boolean
Required: no (default: false)

Typically, TensorZero will not include inputs and outputs in logs or errors to avoid leaking sensitive data. It may be helpful during development to be able to see more information about requests and responses. When this field is set to true, the gateway will log more verbose errors to assist with debugging.

`enable_template_filesystem_access`

Type: boolean
Required: no (default: false)

Enabling this setting will allow MiniJinja templates to load sub-templates from the file system (using the include directive). Paths must be relative to tensorzero.toml, and can only access files in that directory or its sub-directories.

Make sure to sanitize all user-provided template data before using this setting. Otherwise, a malicious input could read unintended files in the file system.

`export.otlp.traces.enabled`

Type: boolean
Required: no (default: false)

Enable exporting traces to an external OpenTelemetry-compatible observability system.

Note that you will still need to set the OTEL_EXPORTER_OTLP_TRACES_ENDPOINT environment variable. See above linked guide for details.

`observability.async_writes`

Type: boolean
Required: no (default: true)

Enabling this setting will improve the latency of the gateway by offloading the responsibility of writing inference responses to ClickHouse to a background task, instead of waiting for ClickHouse to return the inference response.

If you enable this setting, make sure that the gateway lives long enough to complete the writes. This can be problematic in serverless environments that terminate the gateway instance after the response is returned but before the writes are completed.

`observability.enabled`

Type: boolean
Required: no (default: null)

Enable the observability features of the TensorZero Gateway. If true, the gateway will throw an error on startup if it fails to validate the ClickHouse connection. If null, the gateway will log a warning but continue if ClickHouse is not available, and it will use ClickHouse if available. If false, the gateway will not use ClickHouse.

// tensorzero.toml
[gateway]
# ...
observability.enabled = true
# ...

`[models.model_name]`

The [models.model_name] section defines the behavior of a model. You can define multiple models by including multiple [models.model_name] sections. A model is provider agnostic, and the relevant providers are defined in the providers sub-section (see below). If your model_name is not a basic string, it can be escaped with quotation marks. For example, periods are not allowed in basic strings, so you can define llama-3.1-8b-instruct as [models."llama-3.1-8b-instruct"].

// tensorzero.toml
[models.claude-3-haiku-20240307]
# fieldA = ...
# fieldB = ...
# ...

[models."llama-3.1-8b-instruct"]
# fieldA = ...
# fieldB = ...
# ...

`routing`

Type: array of strings
Required: yes

A list of provider names to route requests to. The providers must be defined in the providers sub-section (see below). The TensorZero Gateway will attempt to route a request to the first provider in the list, and fallback to subsequent providers in order if the request is not successful.

// tensorzero.toml
[models.gpt-4o]
# ...
routing = ["openai", "azure"]
# ...

[models.gpt-4o.providers.openai]
# ...

[models.gpt-4o.providers.azure]
# ...

`timeouts`

Type: object
Required: no

The timeouts object allows you to set granular timeouts for requests to this model. You can define timeouts for non-streaming and streaming requests separately: timeouts.non_streaming.total_ms corresponds to the total request duration and timeouts.streaming.ttft_ms corresponds to the time to first token (TTFT). For example, the following configuration sets a 15-second timeout for non-streaming requests and a 3-second timeout for streaming requests (TTFT):

[models.model_name]
# ...
timeouts = { non_streaming.total_ms = 15000, streaming.ttft_ms = 3000 }
# ...

The specified timeouts apply to the scope of an entire model inference request, including all retries and fallbacks across its providers. You can also set timeouts at the variant level and provider level. Multiple timeouts can be active simultaneously.

`[models.model_name.providers.provider_name]`

The providers sub-section defines the behavior of a specific provider for a model. You can define multiple providers by including multiple [models.model_name.providers.provider_name] sections. If your provider_name is not a basic string, it can be escaped with quotation marks. For example, periods are not allowed in basic strings, so you can define vllm.internal as [models.model_name.providers."vllm.internal"].

// tensorzero.toml
[models.gpt-4o]
# ...
routing = ["openai", "azure"]
# ...

[models.gpt-4o.providers.openai]
# ...

[models.gpt-4o.providers.azure]
# ...

`extra_body`

Type: array of objects (see below)
Required: no

The extra_body field allows you to modify the request body that TensorZero sends to a model provider. This advanced feature is an “escape hatch” that lets you use provider-specific functionality that TensorZero hasn’t implemented yet. Each object in the array must have two fields:

pointer: A JSON Pointer string specifying where to modify the request body
One of the following:
- value: The value to insert at that location; it can be of any type including nested types
- delete = true: Deletes the field at the specified location, if present.

You can also set extra_body for a variant entry. The model provider extra_body entries take priority over variant extra_body entries.Additionally, you can set extra_body at inference-time. The values provided at inference-time take priority over the values in the configuration file.

Example: `extra_body`

If TensorZero would normally send this request body to the provider…

{
  "project": "tensorzero",
  "safety_checks": {
    "no_internet": false,
    "no_agi": true
  }
}

…then the following extra_body…

extra_body = [
  { pointer = "/agi", value = true},
  { pointer = "/safety_checks/no_agi", value = { bypass = "on" }}
]

…overrides the request body to:

{
  "agi": true,
  "project": "tensorzero",
  "safety_checks": {
    "no_internet": false,
    "no_agi": {
      "bypass": "on"
    }
  }
}

`extra_headers`

Type: array of objects (see below)
Required: no

The extra_headers field allows you to set or overwrite the request headers that TensorZero sends to a model provider. This advanced feature is an “escape hatch” that lets you use provider-specific functionality that TensorZero hasn’t implemented yet. Each object in the array must have two fields:

name (string): The name of the header to modify (e.g. anthropic-beta)
One of the following:
- value (string): The value of the header (e.g. token-efficient-tools-2025-02-19)
- delete = true: Deletes the header from the request, if present

You can also set extra_headers for a variant entry. The model provider extra_headers entries take priority over variant extra_headers entries.

Example: `extra_headers`

If TensorZero would normally send the following request headers to the provider…

Safety-Checks: on

…then the following extra_headers…

extra_headers = [
  { name = "Safety-Checks", value = "off"},
  { name = "Intelligence-Level", value = "AGI"}
]

…overrides the request headers to:

Safety-Checks: off
Intelligence-Level: AGI

`timeouts`

Type: object
Required: no

The timeouts object allows you to set granular timeouts for individual requests to a model provider. You can define timeouts for non-streaming and streaming requests separately: timeouts.non_streaming.total_ms corresponds to the total request duration and timeouts.streaming.ttft_ms corresponds to the time to first token (TTFT). For example, the following configuration sets a 15-second timeout for non-streaming requests and a 3-second timeout for streaming requests (TTFT):

[models.model_name.providers.provider_name]
# ...
timeouts = { non_streaming.total_ms = 15000, streaming.ttft_ms = 3000 }
# ...

This setting applies to individual requests to the model provider. If you’re using an advanced variant type that performs multiple requests, the timeout will apply to each request separately. If you’ve defined retries and fallbacks, the timeout will apply to each retry and fallback separately. This setting is particularly useful if you’d like to retry or fallback on a request that’s taking too long. You can also set timeouts at the model level and provider level. Multiple timeouts can be active simultaneously. Separately, you can set a global timeout for the entire inference request using the TensorZero client’s timeout field (or simply killing the request if you’re using a different client).

`type`

Type: string
Required: yes

Defines the types of the provider. See Integrations » Model Providers for details. The supported provider types are anthropic, aws_bedrock, aws_sagemaker, azure, deepseek, fireworks, gcp_vertex_anthropic, gcp_vertex_gemini, google_ai_studio_gemini, groq, hyperbolic, mistral, openai, openrouter, sglang, tgi, together, vllm, and xai. The other fields in the provider sub-section depend on the provider type.

// tensorzero.toml
[models.gpt-4o.providers.azure]
# ...
type = "azure"
# ...

type: "anthropic"

`model_name`

Type: string
Required: yes

Defines the model name to use with the Anthropic API. See Anthropic’s documentation for the list of available model names.

// tensorzero.toml
[models.claude-3-haiku.providers.anthropic]
# ...
type = "anthropic"
model_name = "claude-3-haiku-20240307"
# ...

`api_key_location`

Type: string
Required: no (default: env::ANTHROPIC_API_KEY)

Defines the location of the API key for the Anthropic provider. The supported locations are env::ENVIRONMENT_VARIABLE and dynamic::ARGUMENT_NAME (see the API reference) for more details).

// tensorzero.toml
[models.claude-3-haiku.providers.anthropic]
# ...
type = "anthropic"
api_key_location = "dynamic::anthropic_api_key"
# api_key_location = "env::ALTERNATE_ANTHROPIC_API_KEY"
# ...

type: "aws_bedrock"

`allow_auto_detect_region`

Type: boolean
Required: no (default: false)

Defines whether to automatically detect the AWS region to use with the SageMaker API. Under the hood, the gateway will use the AWS SDK to try to detect the region. Alternatively, you can specify the region manually with the region field (recommended).

`model_id`

Type: string
Required: yes

Defines the model ID to use with the AWS Bedrock API. See AWS Bedrock’s documentation for the list of available model IDs.

// tensorzero.toml
[models.claude-3-haiku.providers.aws_bedrock]
# ...
type = "aws_bedrock"
model_id = "anthropic.claude-3-haiku-20240307-v1:0"
# ...

Many AWS Bedrock models are only available through cross-region inference profiles. For those models, the model_id requires special prefix (e.g. the us. prefix in us.anthropic.claude-3-7-sonnet-20250219-v1:0). See the AWS documentation on inference profiles.

`region`

Type: string
Required: no (default: based on credentials if set, otherwise us-east-1)

Defines the AWS region to use with the AWS Bedrock API.

// tensorzero.toml
[models.claude-3-haiku.providers.aws_bedrock]
# ...
type = "aws_bedrock"
region = "us-east-2"
# ...

type: "aws_sagemaker"

`allow_auto_detect_region`

Type: boolean
Required: no (default: false)

`endpoint_name`

Type: string
Required: yes

Defines the endpoint name to use with the AWS SageMaker API.

`hosted_provider`

Type: string
Required: yes

Defines the underlying model provider to use with the SageMaker API. The aws_sagemaker provider is a wrapper on other providers.Currently, the only supported hosted_provider options are:

openai (including any OpenAI-compatible server e.g. Ollama)
tgi

For example, if you’re using Ollama, you can set:

// tensorzero.toml
[models.claude-3-haiku.providers.aws_sagemaker]
# ...
type = "aws_sagemaker"
hosted_provider = "openai"
# ...

`model_name`

Type: string
Required: yes

Defines the model name to use with the AWS SageMaker API.

// tensorzero.toml
[models.claude-3-haiku.providers.aws_sagemaker]
# ...
type = "aws_sagemaker"
model_name = "gemma3:1b"
# ...

`region`

Type: string
Required: no (default: based on credentials if set, otherwise us-east-1)

Defines the AWS region to use with the AWS Bedrock API.

// tensorzero.toml
[models.claude-3-haiku.providers.aws_sagemaker]
# ...
type = "aws_sagemaker"
region = "us-east-2"
# ...

type: "azure"

The TensorZero Gateway handles the API version under the hood (currently 2024-06-01). You only need to set the deployment_id and endpoint fields.

`deployment_id`

Type: string
Required: yes

Defines the deployment ID of the Azure OpenAI deployment.See Azure OpenAI’s documentation for the list of available models.

// tensorzero.toml
[models.gpt-4o-mini.providers.azure]
# ...
type = "azure"
deployment_id = "gpt4o-mini-20240718"
# ...

`endpoint`

Type: string
Required: yes

Defines the endpoint of the Azure OpenAI deployment (protocol and hostname).

// tensorzero.toml
[models.gpt-4o-mini.providers.azure]
# ...
type = "azure"
endpoint = "https://<your-endpoint>.openai.azure.com"
# ...

`api_key_location`

Type: string
Required: no (default: env::AZURE_OPENAI_API_KEY)

Defines the location of the API key for the Azure OpenAI provider. The supported locations are env::ENVIRONMENT_VARIABLE and dynamic::ARGUMENT_NAME (see the API reference) for more details).

// tensorzero.toml
[models.gpt-4o-mini.providers.azure]
# ...
type = "azure"
api_key_location = "dynamic::azure_openai_api_key"
# api_key_location = "env::ALTERNATE_AZURE_OPENAI_API_KEY"
# ...

type: "deepseek"

`model_name`

Type: string
Required: yes

Defines the model name to use with the DeepSeek API. Currently supported models are deepseek-chat (DeepSeek-v3) and deepseek-reasoner (R1).

// tensorzero.toml
[models.deepseek_chat.providers.deepseek]
# ...
type = "deepseek"
model_name = "deepseek-chat"
# ...

`api_key_location`

Type: string
Required: no (default: env::DEEPSEEK_API_KEY)

Defines the location of the API key for the DeepSeek provider. The supported locations are env::ENVIRONMENT_VARIABLE and dynamic::ARGUMENT_NAME (see the API reference) for more details).

// tensorzero.toml
[models.deepseek_chat.providers.deepseek]
# ...
type = "deepseek"
api_key_location = "dynamic::deepseek_api_key"
# api_key_location = "env::ALTERNATE_DEEPSEEK_API_KEY"
# ...

type: "fireworks"

`model_name`

Type: string
Required: yes

Defines the model name to use with the Fireworks API.See Fireworks’ documentation for the list of available model names. You can also deploy your own models on Fireworks AI.

// tensorzero.toml
[models."llama-3.1-8b-instruct".providers.fireworks]
# ...
type = "fireworks"
model_name = "accounts/fireworks/models/llama-v3p1-8b-instruct"
# ...

`api_key_location`

Type: string
Required: no (default: env::FIREWORKS_API_KEY)

Defines the location of the API key for the Fireworks provider. The supported locations are env::ENVIRONMENT_VARIABLE and dynamic::ARGUMENT_NAME (see the API reference) for more details).

// tensorzero.toml
[models."llama-3.1-8b-instruct".providers.fireworks]
# ...
type = "fireworks"
api_key_location = "dynamic::fireworks_api_key"
# api_key_location = "env::ALTERNATE_FIREWORKS_API_KEY"
# ...

type: "gcp_vertex_anthropic"

`endpoint_id`

Type: string
Required: no (exactly one of endpoint_id or model_id must be set)

Defines the endpoint ID of the GCP Vertex AI Anthropic model.Use model_id for off-the-shelf models and endpoint_id for fine-tuned models and custom endpoints.

`location`

Type: string
Required: yes

Defines the location (region) of the GCP Vertex AI Anthropic model.

// tensorzero.toml
[models.claude-3-haiku.providers.gcp_vertex]
# ...
type = "gcp_vertex_anthropic"
location = "us-central1"
# ...

`model_id`

Type: string
Required: no (exactly one of model_id or endpoint_id must be set)

Defines the model ID of the GCP Vertex AI model.See Anthropic’s GCP documentation for the list of available model IDs.

// tensorzero.toml
[models.claude-3-haiku.providers.gcp_vertex]
# ...
type = "gcp_vertex_anthropic"
model_id = "claude-3-haiku@20240307"
# ...

Use model_id for off-the-shelf models and endpoint_id for fine-tuned models and custom endpoints.

`project_id`

Type: string
Required: yes

Defines the project ID of the GCP Vertex AI model.

// tensorzero.toml
[models.claude-3-haiku-2024030.providers.gcp_vertex]
# ...
type = "gcp_vertex"
project_id = "your-project-id"
# ...

`credential_location`

Type: string
Required: no (default: env::GCP_CREDENTIALS_PATH)

Defines the location of the credentials for the GCP Vertex Anthropic provider. The supported locations are env::PATH_TO_CREDENTIALS_FILE, dynamic::CREDENTIALS_ARGUMENT_NAME (see the API reference) for more details), and file::PATH_TO_CREDENTIALS_FILE.

// tensorzero.toml
[models.claude-3-haiku.providers.gcp_vertex]
# ...
type = "gcp_vertex_anthropic"
credential_location = "dynamic::gcp_credentials_path"
# credential_location = "env::ALTERNATE_GCP_CREDENTIALS_PATH"
# credential_location = "file::PATH_TO_CREDENTIALS_FILE"
# ...

type: "gcp_vertex_gemini"

`endpoint_id`

Type: string
Required: no (exactly one of endpoint_id or model_id must be set)

Defines the endpoint ID of the GCP Vertex AI Gemini model.Use model_id for off-the-shelf models and endpoint_id for fine-tuned models and custom endpoints.

`location`

Type: string
Required: yes

Defines the location (region) of the GCP Vertex Gemini model.

// tensorzero.toml
[models."gemini-1.5-flash".providers.gcp_vertex]
# ...
type = "gcp_vertex_gemini"
location = "us-central1"
# ...

`model_id`

Type: string
Required: no (exactly one of model_id or endpoint_id must be set)

Defines the model ID of the GCP Vertex AI model.See GCP Vertex AI’s documentation for the list of available model IDs.

// tensorzero.toml
[models."gemini-1.5-flash".providers.gcp_vertex]
# ...
type = "gcp_vertex_gemini"
model_id = "gemini-1.5-flash-001"
# ...

`project_id`

Type: string
Required: yes

Defines the project ID of the GCP Vertex AI model.

// tensorzero.toml
[models."gemini-1.5-flash".providers.gcp_vertex]
# ...
type = "gcp_vertex_gemini"
project_id = "your-project-id"
# ...

`credential_location`

Type: string
Required: no (default: env::GCP_CREDENTIALS_PATH)

Defines the location of the credentials for the GCP Vertex Gemini provider. The supported locations are env::PATH_TO_CREDENTIALS_FILE, dynamic::CREDENTIALS_ARGUMENT_NAME (see the API reference) for more details), and file::PATH_TO_CREDENTIALS_FILE.

// tensorzero.toml
[models."gemini-1.5-flash".providers.gcp_vertex]
# ...
type = "gcp_vertex_gemini"
credential_location = "dynamic::gcp_credentials_path"
# credential_location = "env::ALTERNATE_GCP_CREDENTIALS_PATH"
# credential_location = "file::PATH_TO_CREDENTIALS_FILE"
# ...

type: "google_ai_studio_gemini"

`model_name`

Type: string
Required: yes

Defines the model name to use with the Google AI Studio Gemini API. See Google AI Studio’s documentation for the list of available model names.

// tensorzero.toml
[models."gemini-1.5-flash".providers.google_ai_studio_gemini]
# ...
type = "google_ai_studio_gemini"
model_name = "gemini-1.5-flash-001"
# ...

`api_key_location`

Type: string
Required: no (default: env::GOOGLE_AI_STUDIO_API_KEY)

Defines the location of the API key for the Google AI Studio Gemini provider. The supported locations are env::ENVIRONMENT_VARIABLE and dynamic::ARGUMENT_NAME (see the API reference) for more details).

// tensorzero.toml
[models."gemini-1.5-flash".providers.google_ai_studio_gemini]
# ...
type = "google_ai_studio_gemini"
api_key_location = "dynamic::google_ai_studio_api_key"
# api_key_location = "env::ALTERNATE_GOOGLE_AI_STUDIO_API_KEY"
# ...

type: "groq"

`model_name`

Type: string
Required: yes

Defines the model name to use with the Groq API.See Groq’s documentation for the list of available model names.

// tensorzero.toml
[models.llama4_scout_17b_16e_instruct.providers.groq]
# ...
type = "groq"
model_name = "meta-llama/llama-4-scout-17b-16e-instruct"
# ...

`api_key_location`

Type: string
Required: no (default: env::GROQ_API_KEY)

Defines the location of the API key for the Groq provider. The supported locations are env::ENVIRONMENT_VARIABLE and dynamic::ARGUMENT_NAME (see the API reference) for more details).

// tensorzero.toml
[models.llama4_scout_17b_16e_instruct.providers.groq]
# ...
type = "groq"
api_key_location = "dynamic::groq_api_key"
# api_key_location = "env::ALTERNATE_GROQ_API_KEY"
# ...

type: "hyperbolic"

`model_name`

Type: string
Required: yes

Defines the model name to use with the Hyperbolic API.See Hyperbolic’s documentation for the list of available model names.

// tensorzero.toml
[models."meta-llama/Meta-Llama-3-70B-Instruct".providers.hyperbolic]
# ...
type = "hyperbolic"
model_name = "meta-llama/Meta-Llama-3-70B-Instruct"
# ...

`api_key_location`

Type: string
Required: no (default: env::HYPERBOLIC_API_KEY)

Defines the location of the API key for the Hyperbolic provider. The supported locations are env::ENVIRONMENT_VARIABLE and dynamic::ARGUMENT_NAME (see the API reference) for more details).

// tensorzero.toml
[models."meta-llama/Meta-Llama-3-70B-Instruct".providers.hyperbolic]
# ...
type = "hyperbolic"
api_key_location = "dynamic::hyperbolic_api_key"
# api_key_location = "env::ALTERNATE_HYPERBOLIC_API_KEY"
# ...

type: "mistral"

`model_name`

Type: string
Required: yes

Defines the model name to use with the Mistral API.See Mistral’s documentation for the list of available model names.

// tensorzero.toml
[models."open-mistral-nemo".providers.mistral]
# ...
type = "mistral"
model_name = "open-mistral-nemo-2407"
# ...

`api_key_location`

Type: string
Required: no (default: env::MISTRAL_API_KEY)

Defines the location of the API key for the Mistral provider. The supported locations are env::ENVIRONMENT_VARIABLE and dynamic::ARGUMENT_NAME (see the API reference) for more details).

// tensorzero.toml
[models."open-mistral-nemo".providers.mistral]
# ...
type = "mistral"
api_key_location = "dynamic::mistral_api_key"
# api_key_location = "env::ALTERNATE_MISTRAL_API_KEY"
# ...

type: "openai"

`api_base`

Type: string
Required: no (default: https://api.openai.com/v1/)

Defines the base URL of the OpenAI API.You can use the api_base field to use an API provider that is compatible with the OpenAI API. However, many providers are only “approximately compatible” with the OpenAI API, so you might need to use a specialized model provider in those cases.

// tensorzero.toml
[models."gpt-4o".providers.openai]
# ...
type = "openai"
api_base = "https://api.openai.com/v1/"
# ...

`model_name`

Type: string
Required: yes

Defines the model name to use with the OpenAI API.See OpenAI’s documentation for the list of available model names.

// tensorzero.toml
[models.gpt-4o-mini.providers.openai]
# ...
type = "openai"
model_name = "gpt-4o-mini-2024-07-18"
# ...

`api_key_location`

Type: string
Required: no (default: env::OPENAI_API_KEY)

Defines the location of the API key for the OpenAI provider. The supported locations are env::ENVIRONMENT_VARIABLE, dynamic::ARGUMENT_NAME, and none (see the API reference) for more details).

// tensorzero.toml
[models.gpt-4o-mini.providers.openai]
# ...
type = "openai"
api_key_location = "dynamic::openai_api_key"
# api_key_location = "env::ALTERNATE_OPENAI_API_KEY"
# api_key_location = "none"
# ...

type: "openrouter"

`model_name`

Type: string
Required: yes

Defines the model name to use with the OpenRouter API.See OpenRouter’s documentation for the list of available model names.

// tensorzero.toml
[models.gpt4_turbo.providers.openrouter]
# ...
type = "openrouter"
model_name = "openai/gpt4.1"
# ...

`api_key_location`

Type: string
Required: no (default: env::OPENROUTER_API_KEY)

Defines the location of the API key for the OpenRouter provider. The supported locations are env::ENVIRONMENT_VARIABLE and dynamic::ARGUMENT_NAME (see the API reference) for more details).

// tensorzero.toml
[models.gpt4_turbo.providers.openrouter]
# ...
type = "openrouter"
api_key_location = "dynamic::openrouter_api_key"
# api_key_location = "env::ALTERNATE_OPENROUTER_API_KEY"
# ...

type: "sglang"

`api_base`

Type: string
Required: yes

Defines the base URL of the SGLang API.

// tensorzero.toml
[models.llama.providers.sglang]
# ...
type = "sglang"
api_base = "http://localhost:8080/v1/"
# ...

`api_key_location`

Type: string
Required: no (default: none)

Defines the location of the API key for the SGLang provider. The supported locations are env::ENVIRONMENT_VARIABLE, dynamic::ARGUMENT_NAME, and none (see the API reference) for more details).

// tensorzero.toml
[models.llama.providers.sglang]
# ...
type = "sglang"
api_key_location = "dynamic::sglang_api_key"
# api_key_location = "env::ALTERNATE_SGLANG_API_KEY"
# api_key_location = "none"  # if authentication is disabled
# ...

type: "together"

`model_name`

Type: string
Required: yes

Defines the model name to use with the Together API.See Together’s documentation for the list of available model names. You can also deploy your own models on Together AI.

// tensorzero.toml
[models.llama3_1_8b_instruct_turbo.providers.together]
# ...
type = "together"
model_name = "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo"
# ...

`api_key_location`

Type: string
Required: no (default: env::TOGETHER_API_KEY)

Defines the location of the API key for the Together AI provider. The supported locations are env::ENVIRONMENT_VARIABLE and dynamic::ARGUMENT_NAME (see the API reference) for more details).

// tensorzero.toml
[models.llama3_1_8b_instruct_turbo.providers.together]
# ...
type = "together"
api_key_location = "dynamic::together_api_key"
# api_key_location = "env::ALTERNATE_TOGETHER_API_KEY"
# ...

type: "vllm"

`api_base`

Type: string
Required: yes (default: http://localhost:8000/v1/)

Defines the base URL of the VLLM API.

// tensorzero.toml
[models."phi-3.5-mini-instruct".providers.vllm]
# ...
type = "vllm"
api_base = "http://localhost:8000/v1/"
# ...

`model_name`

Type: string
Required: yes

Defines the model name to use with the vLLM API.

// tensorzero.toml
[models."phi-3.5-mini-instruct".providers.vllm]
# ...
type = "vllm"
model_name = "microsoft/Phi-3.5-mini-instruct"
# ...

`api_key_location`

Type: string
Required: no (default: env::VLLM_API_KEY)

Defines the location of the API key for the vLLM provider. The supported locations are env::ENVIRONMENT_VARIABLE, dynamic::ARGUMENT_NAME, and none (see the API reference) for more details).

// tensorzero.toml
[models."phi-3.5-mini-instruct".providers.vllm]
# ...
type = "vllm"
api_key_location = "dynamic::vllm_api_key"
# api_key_location = "env::ALTERNATE_VLLM_API_KEY"
# api_key_location = "none"
# ...

type: "xai"

`model_name`

Type: string
Required: yes

Defines the model name to use with the xAI API.See xAI’s documentation for the list of available model names.

// tensorzero.toml
[models.grok_2_1212.providers.xai]
# ...
type = "xai"
model_name = "grok-2-1212"
# ...

`api_key_location`

Type: string
Required: no (default: env::XAI_API_KEY)

Defines the location of the API key for the xAI provider. The supported locations are env::ENVIRONMENT_VARIABLE and dynamic::ARGUMENT_NAME (see the API reference) for more details).

// tensorzero.toml
[models.grok_2_1212.providers.xai]
# ...
type = "xai"
api_key_location = "dynamic::xai_api_key"
# api_key_location = "env::ALTERNATE_XAI_API_KEY"
# ...

type: "tgi"

`api_base`

Type: string
Required: yes

Defines the base URL of the TGI API.

// tensorzero.toml
[models.phi_4.providers.tgi]
# ...
type = "tgi"
api_base = "http://localhost:8080/v1/"
# ...

`api_key_location`

Type: string
Required: no (default: none)

Defines the location of the API key for the TGI provider. The supported locations are env::ENVIRONMENT_VARIABLE, dynamic::ARGUMENT_NAME, and none (see the API reference) for more details).

// tensorzero.toml
[models.phi_4.providers.tgi]
# ...
type = "tgi"
api_key_location = "dynamic::tgi_api_key"
# api_key_location = "env::ALTERNATE_TGI_API_KEY"
# api_key_location = "none"  # if authentication is disabled
# ...

`[embedding_models.model_name]`

The [embedding_models.model_name] section defines the behavior of an embedding model. You can define multiple models by including multiple [embedding_models.model_name] sections. A model is provider agnostic, and the relevant providers are defined in the providers sub-section (see below). If your model_name is not a basic string, it can be escaped with quotation marks. For example, periods are not allowed in basic strings, so you can define embedding-0.1 as [embedding_models."embedding-0.1"].

// tensorzero.toml
[embedding_models.openai-text-embedding-3-small]
# fieldA = ...
# fieldB = ...
# ...

[embedding_models."t0-text-embedding-3.5-massive"]
# fieldA = ...
# fieldB = ...
# ...

`routing`

Type: array of strings
Required: yes

// tensorzero.toml
[embedding_models.model-name]
# ...
routing = ["openai", "alternative-provider"]
# ...

[embedding_models.model-name.providers.openai]
# ...

[embedding_models.model-name.providers.alternative-provider]
# ...

`[embedding_models.model_name.providers.provider_name]`

The providers sub-section defines the behavior of a specific provider for a model. You can define multiple providers by including multiple [embedding_models.model_name.providers.provider_name] sections. If your provider_name is not a basic string, it can be escaped with quotation marks. For example, periods are not allowed in basic strings, so you can define vllm.internal as [embedding_models.model_name.providers."vllm.internal"].

// tensorzero.toml
[embedding_models.model-name]
# ...
routing = ["openai", "alternative-provider"]
# ...

[embedding_models.model-name.providers.openai]
# ...

[embedding_models.model-name.providers.alternative-provider]
# ...

`type`

Type: string
Required: yes

Defines the types of the provider. See Integrations » Model Providers for details. TensorZero currently only supports openai as a provider for embedding models. More integrations are on the way. The other fields in the provider sub-section depend on the provider type.

// tensorzero.toml
[embedding_models.model-name.providers.openai]
# ...
type = "openai"
# ...

type: "openai"

`api_base`

Type: string
Required: no (default: https://api.openai.com/v1/)

// tensorzero.toml
[embedding_models.openai-text-embedding-3-small.providers.openai]
# ...
type = "openai"
api_base = "https://api.openai.com/v1/"
# ...

`model_name`

Type: string
Required: yes

Defines the model name to use with the OpenAI API.See OpenAI’s documentation for the list of available model names.

// tensorzero.toml
[embedding_models.openai-text-embedding-3-small.providers.openai]
# ...
type = "openai"
model_name = "text-embedding-3-small"
# ...

`api_key_location`

Type: string
Required: no (default: env::OPENAI_API_KEY)

Defines the location of the API key for the OpenAI provider. The supported locations are env::ENVIRONMENT_VARIABLE, dynamic::ARGUMENT_NAME, and none (see the API reference) for more details).

// tensorzero.toml
[embedding_models.openai-text-embedding-3-small.providers.openai]
# ...
type = "openai"
api_key_location = "dynamic::openai_api_key"
# api_key_location = "env::ALTERNATE_OPENAI_API_KEY"
# api_key_location = "none"
# ...

`[functions.function_name]`

The [functions.function_name] section defines the behavior of a function. You can define multiple functions by including multiple [functions.function_name] sections. A function can have multiple variants, and each variant is defined in the variants sub-section (see below). A function expresses the abstract behavior of an LLM call (e.g. the schemas for the messages), and its variants express concrete instantiations of that LLM call (e.g. specific templates and models). If your function_name is not a basic string, it can be escaped with quotation marks. For example, periods are not allowed in basic strings, so you can define summarize-2.0 as [functions."summarize-2.0"].

// tensorzero.toml
[functions.draft-email]
# fieldA = ...
# fieldB = ...
# ...

[functions.summarize-email]
# fieldA = ...
# fieldB = ...
# ...

`assistant_schema`

Type: string (path)
Required: no

Defines the path to the assistant schema file. The path is relative to the configuration file. If provided, the assistant schema file should contain a JSON Schema for the assistant messages. The variables in the schema are used for templating the assistant messages. If a schema is provided, all function variants must also provide an assistant template (see below).

// tensorzero.toml
[functions.draft-email]
# ...
assistant_schema = "./functions/draft-email/assistant_schema.json"
# ...

[functions.draft-email.variants.prompt-v1]
# ...
assistant_template = "./functions/draft-email/prompt-v1/assistant_template.minijinja"
# ...

`description`

Type: string
Required: no

Defines a description of the function. In the future, this description will inform automated optimization recipes.

// tensorzero.toml
[functions.extract_data]
# ...
description = "Extract the sender's name (e.g. 'John Doe'), email address (e.g. '[email protected]'), and phone number (e.g. '+1234567890') from a customer's email."
# ...

`system_schema`

Type: string (path)
Required: no

Defines the path to the system schema file. The path is relative to the configuration file. If provided, the system schema file should contain a JSON Schema for the system message. The variables in the schema are used for templating the system message. If a schema is provided, all function variants must also provide a system template (see below).

// tensorzero.toml
[functions.draft-email]
# ...
system_schema = "./functions/draft-email/system_schema.json"
# ...

[functions.draft-email.variants.prompt-v1]
# ...
system_template = "./functions/draft-email/prompt-v1/system_template.minijinja"
# ...

`type`

Type: string
Required: yes

Defines the type of the function. The supported function types are chat and json. Most other fields in the function section depend on the function type.

// tensorzero.toml
[functions.draft-email]
# ...
type = "chat"
# ...

type: "chat"

`parallel_tool_calls`

Type: boolean
Required: no

Determines whether the function should be allowed to call multiple tools in a single conversation turn.If not set, TensorZero will default to the model provider’s default behavior.Most model providers do not support this feature. In those cases, this field will be ignored.

// tensorzero.toml
[functions.draft-email]
# ...
type = "chat"
parallel_tool_calls = true
# ...

`tool_choice`

Type: string
Required: no (default: auto)

Determines the tool choice strategy for the function.The supported tool choice strategies are:

none: The function should not use any tools.
auto: The model decides whether or not to use a tool. If it decides to use a tool, it also decides which tools to use.
required: The model should use a tool. If multiple tools are available, the model decides which tool to use.
{ specific = "tool_name" }: The model should use a specific tool. The tool must be defined in the tools field (see below).

// tensorzero.toml
[functions.solve-math-problem]
# ...
type = "chat"
tool_choice = "auto"
tools = [
  # ...
  "run-python"
  # ...
]
# ...

[tools.run-python]
# ...

// tensorzero.toml
[functions.generate-query]
# ...
type = "chat"
tool_choice = { specific = "query-database" }
tools = [
  # ...
  "query-database"
  # ...
]
# ...

[tools.query-database]
# ...

`tools`

Type: array of strings
Required: no (default: [])

Determines the tools that the function can use.The supported tools are defined in [tools.tool_name] sections (see below).

// tensorzero.toml
[functions.draft-email]
# ...
type = "chat"
tools = [
  # ...
  "query-database"
  # ...
]
# ...

[tools.query-database]
# ...

type: "json"

`output_schema`

Type: string (path)
Required: no (default: {}, the empty JSON schema that accepts any valid JSON output)

Defines the path to the output schema file, which should contain a JSON Schema for the output of the function. The path is relative to the configuration file.This schema is used for validating the output of the function.

// tensorzero.toml
[functions.extract-customer-info]
# ...
type = "json"
output_schema = "./functions/extract-customer-info/output_schema.json"
# ...

`user_schema`

Type: string (path)
Required: no

Defines the path to the user schema file. The path is relative to the configuration file. If provided, the user schema file should contain a JSON Schema for the user messages. The variables in the schema are used for templating the user messages. If a schema is provided, all function variants must also provide a user template (see below).

// tensorzero.toml
[functions.draft-email]
# ...
user_schema = "./functions/draft-email/user_schema.json"
# ...

[functions.draft-email.variants.prompt-v1]
# ...
user_template = "./functions/draft-email/prompt-v1/user_template.minijinja"
# ...

`[functions.function_name.variants.variant_name]`

The variants sub-section defines the behavior of a specific variant of a function. You can define multiple variants by including multiple [functions.function_name.variants.variant_name] sections. If your variant_name is not a basic string, it can be escaped with quotation marks. For example, periods are not allowed in basic strings, so you can define llama-3.1-8b-instruct as [functions.function_name.variants."llama-3.1-8b-instruct"].

// tensorzero.toml
[functions.draft-email]
# ...

[functions.draft-email.variants."llama-3.1-8b-instruct"]
# ...

[functions.draft-email.variants.claude-3-haiku]
# ...

`type`

Type: string
Required: yes

Defines the type of the variant. TensorZero currently supports the following variant types:

Type	Description
`chat_completion`	Uses a chat completion model to generate responses by processing a series of messages in a conversational format. This is typically what you use out of the box with most LLMs.
`experimental_best_of_n`	Generates multiple response candidates with other variants, and selects the best one using an evaluator model.
`experimental_chain_of_thought`	Encourages the model to reason step by step using a chain-of-thought prompting strategy, which is particularly useful for tasks requiring logical reasoning or multi-step problem-solving. Only available for non-streaming requests to JSON functions.
`experimental_dynamic_in_context_learning`	Selects similar high-quality examples using an embedding of the input, and incorporates them into the prompt to enhance context and improve response quality.
`experimental_mixture_of_n`	Generates multiple response candidates with other variants, and combines the responses using a fuser model.

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
type = "chat_completion"
# ...

type: "chat_completion"

`assistant_template`

Type: string (path)
Required: no

Defines the path to the assistant template file. The path is relative to the configuration file.This file should contain a MiniJinja template for the assistant messages. If the template uses any variables, the variables should be defined in the function’s assistant_schema field.

// tensorzero.toml
[functions.draft-email]
# ...
assistant_schema = "./functions/draft-email/assistant_schema.json"
# ...

[functions.draft-email.variants.prompt-v1]
# ...
assistant_template = "./functions/draft-email/prompt-v1/assistant_template.minijinja"
# ...

`extra_body`

Type: array of objects (see below)
Required: no

The extra_body field allows you to modify the request body that TensorZero sends to a variant’s model provider. This advanced feature is an “escape hatch” that lets you use provider-specific functionality that TensorZero hasn’t implemented yet.Each object in the array must have two fields:

pointer: A JSON Pointer string specifying where to modify the request body
One of the following:
- value: The value to insert at that location; it can be of any type including nested types
- delete = true: Deletes the field at the specified location, if present.

You can also set extra_body for a model provider entry. The model provider extra_body entries take priority over variant extra_body entries.Additionally, you can set extra_body at inference-time. The values provided at inference-time take priority over the values in the configuration file.

Example: `extra_body`

If TensorZero would normally send this request body to the provider…

{
  "project": "tensorzero",
  "safety_checks": {
    "no_internet": false,
    "no_agi": true
  }
}

…then the following extra_body…

extra_body = [
  { pointer = "/agi", value = true},
  { pointer = "/safety_checks/no_agi", value = { bypass = "on" }}
]

…overrides the request body to:

{
  "agi": true,
  "project": "tensorzero",
  "safety_checks": {
    "no_internet": false,
    "no_agi": {
      "bypass": "on"
    }
  }
}

`extra_headers`

Type: array of objects (see below)
Required: no

name (string): The name of the header to modify (e.g. anthropic-beta)
One of the following:
- value (string): The value of the header (e.g. token-efficient-tools-2025-02-19)
- delete = true: Deletes the header from the request, if present

You can also set extra_headers for a model provider entry. The model provider extra_headers entries take priority over variant extra_headers entries.

Example: `extra_headers`

If TensorZero would normally send the following request headers to the provider…

Safety-Checks: on

…then the following extra_headers…

extra_headers = [
  { name = "Safety-Checks", value = "off"},
  { name = "Intelligence-Level", value = "AGI"}
]

…overrides the request headers to:

Safety-Checks: off
Intelligence-Level: AGI

`frequency_penalty`

Type: float
Required: no (default: null)

Penalizes new tokens based on their frequency in the text so far if positive, encourages them if negative.

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
frequency_penalty = 0.2
# ...

`json_mode`

Type: string
Required: no (default: strict)

Defines the strategy for generating JSON outputs.This parameter is only supported for variants of functions with type = "json".The supported modes are:

off: Make a chat completion request without any special JSON handling (not recommended).
on: Make a chat completion request with JSON mode (if supported by the provider).
strict: Make a chat completion request with strict JSON mode (if supported by the provider). For example, the TensorZero Gateway uses Structured Outputs for OpenAI.
implicit_tool: Make a special-purpose tool use request under the hood, and convert the tool call into a JSON response.

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
json_mode = "strict"
# ...

`max_tokens`

Type: integer
Required: no (default: null)

Defines the maximum number of tokens to generate.

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
max_tokens = 100
# ...

`model`

Type: string
Required: yes

The name of the model to call.

To call…	Use this format…
A model defined as `[models.my_model]` in your `tensorzero.toml` configuration file	`model_name=“my_model”`
A model offered by a model provider, without defining it in your `tensorzero.toml` configuration file (if supported, see below)	`model_name="{provider_type}::{model_name}"`

The following model providers support short-hand model names: anthropic, deepseek, fireworks, google_ai_studio_gemini, gcp_vertex_gemini, gcp_vertex_anthropic, hyperbolic, groq, mistral, openai, openrouter, together, and xai.

For example, if you have the following configuration:

tensorzero.toml

[models.gpt-4o]
routing = ["openai", "azure"]

[models.gpt-4o.providers.openai]
# ...

[models.gpt-4o.providers.azure]
# ...

Then:

model = "gpt-4o" calls the gpt-4o model in your configuration, which supports fallback from openai to azure. See Retries & Fallbacks for details.
model = "openai::gpt-4o" calls the OpenAI API directly for the gpt-4o model, ignoring the gpt-4o model defined above.

`presence_penalty`

Type: float
Required: no (default: null)

Penalizes new tokens based on that have already appeared in the text so far if positive, encourages them if negative.

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
presence_penalty = 0.5
# ...

`retries`

Type: object with optional keys num_retries and max_delay_s
Required: no (defaults to num_retries = 0 and a max_delay_s = 10)

TensorZero’s retry strategy is truncated exponential backoff with jitter. The num_retries parameter defines the number of retries (not including the initial request). The max_delay_s parameter defines the maximum delay between retries.

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
retries = { num_retries = 3, max_delay_s = 10 }
# ...

`seed`

Type: integer
Required: no (default: null)

Defines the seed to use for the variant.

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
seed = 42

`system_template`

Type: string (path)
Required: no

Defines the path to the system template file. The path is relative to the configuration file.This file should contain a MiniJinja template for the system messages. If the template uses any variables, the variables should be defined in the function’s system_schema field.

// tensorzero.toml
[functions.draft-email]
# ...
system_schema = "./functions/draft-email/system_schema.json"
# ...

[functions.draft-email.variants.prompt-v1]
# ...
system_template = "./functions/draft-email/prompt-v1/system_template.minijinja"
# ...

`temperature`

Type: float
Required: no (default: null)

Defines the temperature to use for the variant.

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
temperature = 0.5
# ...

`timeouts`

Type: object
Required: no

The timeouts object allows you to set granular timeouts for requests using this variant.You can define timeouts for non-streaming and streaming requests separately: timeouts.non_streaming.total_ms corresponds to the total request duration and timeouts.streaming.ttft_ms corresponds to the time to first token (TTFT).For example, the following configuration sets a 15-second timeout for non-streaming requests and a 3-second timeout for streaming requests (TTFT):

[functions.function_name.variants.variant_name]
# ...
timeouts = { non_streaming.total_ms = 15000, streaming.ttft_ms = 3000 }
# ...

The specified timeouts apply to the scope of an entire variant inference request, including all retries and fallbacks across its model’s providers. You can also set timeouts at the model level and provider level. Multiple timeouts can be active simultaneously.

`top_p`

Type: float, between 0 and 1
Required: no (default: null)

Defines the top_p to use for the variant during nucleus sampling. Typically at most one of top_p and temperature is set.

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
top_p = 0.3
# ...

`user_template`

Type: string (path)
Required: no

Defines the path to the user template file. The path is relative to the configuration file.This file should contain a MiniJinja template for the user messages. If the template uses any variables, the variables should be defined in the function’s user_schema field.

// tensorzero.toml
[functions.draft-email]
# ...
user_schema = "./functions/draft-email/user_schema.json"
# ...

[functions.draft-email.variants.prompt-v1]
# ...
user_template = "./functions/draft-email/prompt-v1/user_template.minijinja"
# ...

`weight`

Type: float
Required: no (default: 0)

Defines the weight of the variant. When you call a function, the weight determines the relative importance of the variant when sampling.Variants will be sampled with a probability proportional to their weight. For example, if variant A has a weight of 1.0 and variant B has a weight of 3.0, variant A will be sampled with probability 1.0 / (1.0 + 3.0) = 25% and variant B will be sampled with probability 3.0 / (1.0 + 3.0) = 75%.You can disable a variant by setting its weight to 0. The variant will only be used if there are no other variants available for sampling or if the variant is requested explicitly in the request with variant_name. This is useful for defining fallback variants, which won’t be used unless no other variants are available.

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
weight = 1.0
# ...

type: "experimental_best_of_n"

`candidates`

Type: list of strings
Required: yes

This inference strategy generates N candidate responses, and an evaluator model selects the best one. This approach allows you to leverage multiple prompts or variants to increase the likelihood of getting a high-quality response.The candidates parameter specifies a list of variant names used to generate candidate responses. For example, if you have two variants defined (promptA and promptB), you could set up the candidates list to generate two responses using promptA and one using promptB using the snippet below. The evaluator would then choose the best response from these three candidates.

// tensorzero.toml
[functions.draft-email.variants.promptA]
type = "chat_completion"
# ...

[functions.draft-email.variants.promptB]
type = "chat_completion"
# ...

[functions.draft-email.variants.best-of-n]
type = "experimental_best_of_n"
candidates = ["promptA", "promptA", "promptB"] # 3 candidate generations
# ...

`evaluator`

Type: object
Required: yes

The evaluator parameter specifies the configuration for the model that will evaluate and select the best response from the generated candidates.The evaluator is configured similarly to a chat_completion variant, but without the type field. The prompts here should be prompts that you would use to solve the original problem, as the gateway has special-purpose handling and templates to convert them to an evaluator.

[functions.draft-email.variants.best-of-n]
type = "experimental_best_of_n"
# ...

[functions.draft-email.variants.best-of-n.evaluator]
# Same fields as a `chat_completion` variant (excl.`type`), e.g.:
# user_template = "functions/draft-email/best-of-n/user.minijinja"
# ...

`timeout_s`

Type: float
Required: no (default: 300s)

The timeout_s parameter specifies the maximum time in seconds allowed for generating candidate responses. Any candidate that takes longer than this duration to generate a response will be dropped from consideration.

[functions.draft-email.variants.best-of-n]
type = "experimental_best_of_n"
timeout_s = 60
# ...

`timeouts`

Type: object
Required: no

[functions.function_name.variants.variant_name]
# ...
timeouts = { non_streaming.total_ms = 15000, streaming.ttft_ms = 3000 }
# ...

The specified timeouts apply to the scope of an entire variant inference request, including all inference requests to candidates and the evaluator. You can also set timeouts at the model level and provider level. Multiple timeouts can be active simultaneously.

`weight`

Type: float
Required: no (default: 0)

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
weight = 1.0
# ...

type: "experimental_chain_of_thought"

type: "experimental_mixture_of_n"

`candidates`

Type: list of strings
Required: yes

This inference strategy generates N candidate responses, and a fuser model combines them to produce a final answer. This approach allows you to leverage multiple prompts or variants to increase the likelihood of getting a high-quality response.The candidates parameter specifies a list of variant names used to generate candidate responses. For example, if you have two variants defined (promptA and promptB), you could set up the candidates list to generate two responses using promptA and one using promptB using the snippet below. The fuser would then combine the three responses.

// tensorzero.toml
[functions.draft-email.variants.promptA]
type = "chat_completion"
# ...

[functions.draft-email.variants.promptB]
type = "chat_completion"
# ...

[functions.draft-email.variants.mixture-of-n]
type = "experimental_mixture_of_n"
candidates = ["promptA", "promptA", "promptB"] # 3 candidate generations
# ...

`fuser`

Type: object
Required: yes

The fuser parameter specifies the configuration for the model that will evaluate and combine the elements.The evaluator is configured similarly to a chat_completion variant, but without the type field. The prompts here should be prompts that you would use to solve the original problem, as the gateway has special-purpose handling and templates to convert them to a fuser.

[functions.draft-email.variants.mixture-of-n]
type = "experimental_mixture_of_n"
# ...

[functions.draft-email.variants.mixture-of-n.fuser]
# Same fields as a `chat_completion` variant (excl.`type`), e.g.:
# user_template = "functions/draft-email/mixture-of-n/user.minijinja"
# ...

`timeout_s`

Type: float
Required: no (default: 300s)

[functions.draft-email.variants.mixture-of-n]
type = "experimental_mixture_of_n"
timeout_s = 60
# ...

`timeouts`

Type: object
Required: no

[functions.function_name.variants.variant_name]
# ...
timeouts = { non_streaming.total_ms = 15000, streaming.ttft_ms = 3000 }
# ...

The specified timeouts apply to the scope of an entire variant inference request, including all inference requests to candidates and the fuser. You can also set timeouts at the model level and provider level. Multiple timeouts can be active simultaneously.

`weight`

Type: float
Required: no (default: 0)

[functions.draft-email.variants.mixture-of-n]
# ...
weight = 1.0
# ...

type: "experimental_dynamic_in_context_learning"

`embedding_model`

Type: string
Required: yes

The name of the embedding model to call.

To call…	Use this format…
A model defined as `[models.my_model]` in your `tensorzero.toml` configuration file	`model_name=“my_model”`
A model offered by a model provider, without defining it in your `tensorzero.toml` configuration file (if supported, see below)	`model_name="{provider_type}::{model_name}"`

For example, if you have the following configuration:

tensorzero.toml

[embedding_models.text-embedding-3-small]
#...

[embedding_models.text-embedding-3-small.providers.openai]
# ...

[embedding_models.text-embedding-3-small.providers.azure]
# ...

Then:

embedding_model = "text-embedding-3-small" calls the text-embedding-3-small model in your configuration.
embedding_model = "openai::text-embedding-3-small" calls the OpenAI API directly for the text-embedding-3-small model, ignoring the text-embedding-3-small model defined above.

`extra_body`

Type: array of objects (see below)
Required: no

pointer: A JSON Pointer string specifying where to modify the request body
One of the following:
- value: The value to insert at that location; it can be of any type including nested types
- delete = true: Deletes the field at the specified location, if present.

Example: `extra_body`

If TensorZero would normally send this request body to the provider…

{
  "project": "tensorzero",
  "safety_checks": {
    "no_internet": false,
    "no_agi": true
  }
}

…then the following extra_body…

extra_body = [
  { pointer = "/agi", value = true},
  { pointer = "/safety_checks/no_agi", value = { bypass = "on" }}
]

…overrides the request body to:

{
  "agi": true,
  "project": "tensorzero",
  "safety_checks": {
    "no_internet": false,
    "no_agi": {
      "bypass": "on"
    }
  }
}

`extra_headers`

Type: array of objects (see below)
Required: no

name (string): The name of the header to modify (e.g. anthropic-beta)
One of the following:
- value (string): The value of the header (e.g. token-efficient-tools-2025-02-19)
- delete = true: Deletes the header from the request, if present

You can also set extra_headers for a model provider entry. The model provider extra_headers entries take priority over variant extra_headers entries.

Example: `extra_headers`

If TensorZero would normally send the following request headers to the provider…

Safety-Checks: on

…then the following extra_headers…

extra_headers = [
  { name = "Safety-Checks", value = "off"},
  { name = "Intelligence-Level", value = "AGI"}
]

…overrides the request headers to:

Safety-Checks: off
Intelligence-Level: AGI

`json_mode`

Type: string
Required: no (default: strict)

Defines the strategy for generating JSON outputs.This parameter is only supported for variants of functions with type = "json".The supported modes are:

off: Make a chat completion request without any special JSON handling (not recommended).
on: Make a chat completion request with JSON mode (if supported by the provider).
strict: Make a chat completion request with strict JSON mode (if supported by the provider). For example, the TensorZero Gateway uses Structured Outputs for OpenAI.
implicit_tool: Make a special-purpose tool use request under the hood, and convert the tool call into a JSON response.

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
json_mode = "strict"
# ...

`k`

Type: non-negative integer
Required: yes

Defines the number of examples to retrieve for the inference.

// tensorzero.toml
[functions.draft-email.variants.dicl]
# ...
k = 10
# ...

`max_tokens`

Type: integer
Required: no (default: null)

Defines the maximum number of tokens to generate.

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
max_tokens = 100
# ...

`model`

Type: string
Required: yes

The name of the model to call.

To call…	Use this format…
A model defined as `[models.my_model]` in your `tensorzero.toml` configuration file	`model_name=“my_model”`
A model offered by a model provider, without defining it in your `tensorzero.toml` configuration file (if supported, see below)	`model_name="{provider_type}::{model_name}"`

For example, if you have the following configuration:

tensorzero.toml

[models.gpt-4o]
routing = ["openai", "azure"]

[models.gpt-4o.providers.openai]
# ...

[models.gpt-4o.providers.azure]
# ...

Then:

model = "gpt-4o" calls the gpt-4o model in your configuration, which supports fallback from openai to azure. See Retries & Fallbacks for details.
model = "openai::gpt-4o" calls the OpenAI API directly for the gpt-4o model, ignoring the gpt-4o model defined above.

`retries`

Type: object with optional keys num_retries and max_delay_s
Required: no (defaults to num_retries = 0 and a max_delay_s = 10)

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
retries = { num_retries = 3, max_delay_s = 10 }
# ...

`seed`

Type: integer
Required: no (default: null)

Defines the seed to use for the variant.

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
seed = 42

`system_instructions`

Type: string (path)
Required: no

Defines the path to the system instructions file. The path is relative to the configuration file.The system instruction is a text file that will be added to the evaluator’s system prompt. Unlike system_template, it doesn’t support variables. This file contains static instructions that define the behavior and role of the AI assistant for the specific function variant.

// tensorzero.toml
[functions.draft-email.variants.dicl]
# ...
system_instructions = "./functions/draft-email/prompt-v1/system_template.txt"
# ...

`temperature`

Type: float
Required: no (default: null)

Defines the temperature to use for the variant.

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
temperature = 0.5
# ...

`timeouts`

Type: object
Required: no

[functions.function_name.variants.variant_name]
# ...
timeouts = { non_streaming.total_ms = 15000, streaming.ttft_ms = 3000 }
# ...

The specified timeouts apply to the scope of an entire variant inference request, including both inference requests to the embedding model and the generation model. You can also set timeouts at the model level and provider level. Multiple timeouts can be active simultaneously.

`weight`

Type: float
Required: no (default: 0)

// tensorzero.toml
[functions.draft-email.variants.prompt-v1]
# ...
weight = 1.0
# ...

`type: "experimental_chain_of_thought"`

Besides the type parameter, this variant has the same configuration options as the chat_completion variant type. Please refer to that documentation to see what options are available.

`[metrics]`

The [metrics] section defines the behavior of a metric. You can define multiple metrics by including multiple [metrics.metric_name] sections. The metric name can’t be comment or demonstration, as those names are reserved for internal use. If your metric_name is not a basic string, it can be escaped with quotation marks. For example, periods are not allowed in basic strings, so you can define beats-gpt-3.5 as [metrics."beats-gpt-3.5"].

// tensorzero.toml
[metrics.task-completed]
# fieldA = ...
# fieldB = ...
# ...

[metrics.user-rating]
# fieldA = ...
# fieldB = ...
# ...

`level`

Type: string
Required: yes

Defines whether the metric applies to individual inference or across entire episodes. The supported levels are inference and episode.

// tensorzero.toml
[metrics.valid-output]
# ...
level = "inference"
# ...

[metrics.task-completed]
# ...
level = "episode"
# ...

`optimize`

Type: string
Required: yes

Defines whether the metric should be maximized or minimized. The supported values are max and min.

// tensorzero.toml
[metrics.mistakes-made]
# ...
optimize = "min"
# ...

[metrics.user-rating]
# ...
optimize = "max"
# ...

`type`

Type: string
Required: yes

Defines the type of the metric. The supported metric types are boolean and float.

// tensorzero.toml
[metrics.user-rating]
# ...
type = "float"
# ...

[metrics.task-completed]
# ...
type = "boolean"
# ...

`[tools.tool_name]`

The [tools.tool_name] section defines the behavior of a tool. You can define multiple tools by including multiple [tools.tool_name] sections. If your tool_name is not a basic string, it can be escaped with quotation marks. For example, periods are not allowed in basic strings, so you can define run-python-3.10 as [tools."run-python-3.10"]. You can enable a tool for a function by adding it to the function’s tools field.

// tensorzero.toml
[functions.weather-chatbot]
# ...
type = "chat"
tools = [
  # ...
  "get-temperature"
  # ...
]
# ...

[tools.get-temperature]
# ...

`description`

Type: string
Required: yes

Defines the description of the tool provided to the model. You can typically materially improve the quality of responses by providing a detailed description of the tool.

// tensorzero.toml
[tools.get-temperature]
# ...
description = "Get the current temperature in a given location (e.g. \"Tokyo\") using the specified unit (must be \"celsius\" or \"fahrenheit\")."
# ...

`parameters`

Type: string (path)
Required: yes

Defines the path to the parameters file. The path is relative to the configuration file. This file should contain a JSON Schema for the parameters of the tool.

// tensorzero.toml
[tools.get-temperature]
# ...
parameters = "./tools/get-temperature.json"
# ...

`strict`

Type: boolean
Required: no (default: false)

If set to true, the TensorZero Gateway attempts to use strict JSON generation for the tool parameters. This typically improves the quality of responses. Only a few providers support strict JSON generation. For example, the TensorZero Gateway uses Structured Outputs for OpenAI. If the provider does not support strict mode, the TensorZero Gateway ignores this field.

// tensorzero.toml
[tools.get-temperature]
# ...
strict = true
# ...

`name`

Type: string
Required: no (defaults to the tool ID)

Defines the tool name to be sent to model providers. By default, TensorZero will use the tool ID in the configuration as the tool name sent to model providers. For example, if you define a tool as [tools.my_tool] but don’t specify the name, the name will be my_tool. This field allows you to specify a different name to be sent. This field is particularly useful if you want to define multiple tools that share the same name (e.g. for different functions). At inference time, the gateway ensures that an inference request doesn’t have multiple tools with the same name.

`[object_storage]`

The [object_storage] section defines the behavior of object storage, which is used for storing images used during multimodal inference.

`type`

Type: string
Required: yes

Defines the type of object storage to use. The supported types are:

s3_compatible: Use an S3-compatible object storage service.
filesystem: Store images in a local directory.
disabled: Disable object storage.

See the following sections for more details on each type.

type: "s3_compatible"

type: "filesystem"

type: "disabled"

Introduction

Gateway

Optimization

Evaluations

UI

​[gateway]

​base_path

​bind_address

​debug

​enable_template_filesystem_access

​export.otlp.traces.enabled

​observability.async_writes

​observability.enabled

​[models.model_name]

​routing

​timeouts

​[models.model_name.providers.provider_name]

​extra_body

​extra_headers

​timeouts

​type

​[embedding_models.model_name]

​routing

​[embedding_models.model_name.providers.provider_name]

​type

​[functions.function_name]

​assistant_schema

​description

​system_schema

​type

​user_schema

​[functions.function_name.variants.variant_name]

​type

​type: "experimental_chain_of_thought"

​[metrics]

​level

​optimize

​type

​[tools.tool_name]

​description

​parameters

​strict

​name

​[object_storage]

​type

`[gateway]`

`base_path`

`bind_address`

`debug`

`enable_template_filesystem_access`

`export.otlp.traces.enabled`

`observability.async_writes`

`observability.enabled`

`[models.model_name]`

`routing`

`timeouts`

`[models.model_name.providers.provider_name]`

`extra_body`

`extra_headers`

`timeouts`

`type`

`[embedding_models.model_name]`

`routing`

`[embedding_models.model_name.providers.provider_name]`

`type`

`[functions.function_name]`

`assistant_schema`

`description`

`system_schema`

`type`

`user_schema`

`[functions.function_name.variants.variant_name]`

`type`

`type: "experimental_chain_of_thought"`

`[metrics]`

`level`

`optimize`

`type`

`[tools.tool_name]`

`description`

`parameters`

`strict`

`name`

`[object_storage]`

`type`