API Reference: Inference - TensorZero Docs

`POST /inference`

The inference endpoint is the core of the TensorZero Gateway API. Under the hood, the gateway validates the request, samples a variant from the function, handles templating when applicable, and routes the inference to the appropriate model provider. If a problem occurs, it attempts to gracefully fallback to a different model provider or variant. After a successful inference, it returns the data to the client and asynchronously stores structured information in the database.

See the API Reference for POST /openai/v1/chat/completions for an inference endpoint compatible with the OpenAI API.

Request

`additional_tools`

Type: a list of tools (see below)
Required: no (default: [])

A list of tools defined at inference time that the model is allowed to call. This field allows for dynamic tool use, i.e. defining tools at runtime. You should prefer to define tools in the configuration file if possible. Only use this field if dynamic tool use is necessary for your use case. Each tool is an object with the following fields: description, name, parameters, and strict. The fields are identical to those in the configuration file, except that the parameters field should contain the JSON schema itself rather than a path to it. See Configuration Reference for more details.

`allowed_tools`

Type: list of strings
Required: no

A list of tool names that the model is allowed to call. The tools must be defined in the configuration file. Any tools provided in additional_tools are always allowed, irrespective of this field.

`cache_options`

Type: object
Required: no (default: {"enabled": "write_only"})

Options for controlling inference caching behavior. The object has the fields below. See Inference Caching for more details.

`cache_options.enabled`

Type: string
Required: no (default: "write_only")

The cache mode to use. Must be one of:

"write_only" (default): Only write to cache but don’t serve cached responses
"read_only": Only read from cache but don’t write new entries
"on": Both read from and write to cache
"off": Disable caching completely

Note: When using dryrun=true, the gateway never writes to the cache.

`cache_options.max_age_s`

Type: integer
Required: no (default: null)

Maximum age in seconds for cache entries. If set, cached responses older than this value will not be used. For example, if you set max_age_s=3600, the gateway will only use cache entries that were created in the last hour.

`credentials`

Type: object (a map from dynamic credential names to API keys)
Required: no (default: no credentials)

Each model provider in your TensorZero configuration can be configured to accept credentials at inference time by using the dynamic location (e.g. dynamic::my_dynamic_api_key_name). See the configuration reference for more details. The gateway expects the credentials to be provided in the credentials field of the request body as specified below. The gateway will return a 400 error if the credentials are not provided and the model provider has been configured with dynamic credentials.

Example

[models.my_model_name.providers.my_provider_name]
# ...
# Note: the name of the credential field (e.g. `api_key_location`) depends on the provider type
api_key_location = "dynamic::my_dynamic_api_key_name"
# ...

{
  // ...
  "credentials": {
    // ...
    "my_dynamic_api_key_name": "sk-..."
    // ...
  }
  // ...
}

`dryrun`

Type: boolean
Required: no

If true, the inference request will be executed but won’t be stored to the database. The gateway will still call the downstream model providers. This field is primarily for debugging and testing, and you should generally not use it in production.

`episode_id`

Type: UUID
Required: no

The ID of an existing episode to associate the inference with. For the first inference of a new episode, you should not provide an episode_id. If null, the gateway will generate a new episode ID and return it in the response. Only use episode IDs that were returned by the TensorZero gateway.

`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 three fields:

variant_name or model_provider_name: The modification will only be applied to the specified variant or model provider
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 in the configuration file. 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 in the inference request…

{
  // ...
  "extra_body": [
    {
      "variant_name": "my_variant", // or "model_provider_name": "my_model_provider"
      "pointer": "/agi",
      "value": true
    },
    {
      "variant_name": "my_variant", // or "model_provider_name": "my_model_provider"
      "pointer": "/safety_checks/no_agi",
      "value": {
        "bypass": "on"
      }
    }
  ]
}

…overrides the request body (for my_variant only) 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 modify 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 three fields:

variant_name or model_provider_name: The modification will only be applied to the specified variant or model provider
name: The name of the header to modify
value: The value to set the header to

You can also set extra_headers in the configuration file. The values provided at inference-time take priority over the values in the configuration file.

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": [
    {
      "variant_name": "my_variant", // or "model_provider_name": "my_model_provider"
      "name": "Safety-Checks",
      "value": "off"
    },
    {
      "variant_name": "my_variant", // or "model_provider_name": "my_model_provider"
      "name": "Intelligence-Level",
      "value": "AGI"
    }
  ]
}

…overrides the request headers to:

Safety-Checks: off
Intelligence-Level: AGI

`function_name`

Type: string
Required: either function_name or model_name must be provided

The name of the function to call. The function must be defined in the configuration file. Alternatively, you can use the model_name field to call a model directly, without the need to define a function. See below for more details.

`include_original_response`

Type: boolean
Required: no

If true, the original response from the model will be included in the response in the original_response field as a string. See original_response in the response section for more details.

`input`

Type: varies
Required: yes

The input to the function. The type of the input depends on the function type.

`input.messages`

Type: list of messages (see below)
Required: no (default: [])

A list of messages to provide to the model. Each message is an object with the following fields:

role: The role of the message (assistant or user).
content: The content of the message (see below).

The content field can be have one of the following types:

string: the text for a text message (only allowed if there is no schema for that role)
list of content blocks: the content blocks for the message (see below)

A content block is an object with the field type and additional fields depending on the type. If the content block has type text, it must have either of the following additional fields:

text: The text for the content block.
arguments: A JSON object containing the function arguments for TensorZero functions with templates and schemas (see Prompt Templates & Schemas for details).

If the content block has type tool_call, it must have the following additional fields:

arguments: The arguments for the tool call.
id: The ID for the content block.
name: The name of the tool for the content block.

If the content block has type tool_result, it must have the following additional fields:

id: The ID for the content block.
name: The name of the tool for the content block.
result: The result of the tool call.

If the content block has type file, it must have exactly one of the following additional fields:

url: The URL for a remote file.
mime_type and data: The MIME type (e.g. image/png, image/jpeg, application/pdf) and base64-encoded data for an embedded file.

See the Multimodal Inference guide for more details on how to use images in inference. If the content block has type raw_text, it must have the following additional fields:

value: The text for the content block. This content block will ignore any relevant templates and schemas for this function.

If the content block has type thought, it must have the following additional fields:

text: The text for the content block.

If the content block has type unknown, it must have the following additional fields:

data: The original content block from the provider, without any validation or transformation by TensorZero.
model_provider_name (optional): A string specifying when this content block should be included in the model provider input. If set, the content block will only be provided to this specific model provider. If not set, the content block is passed to all model providers.

For example, the following hypothetical unknown content block will send the daydreaming content block to inference requests targeting the your_model_provider_name model provider.

{
  "type": "unknown",
  "data": {
    "type": "daydreaming",
    "dream": "..."
  },
  "model_provider_name": "tensorzero::model_name::your_model_name::provider_name::your_model_provider_name"
}

Certain reasoning models (e.g. DeepSeek R1) can include thought content blocks in the response. These content blocks can’t directly be used as inputs to subsequent inferences in multi-turn scenarios. If you need to provide thought content blocks to a model, you should convert them to text content blocks.

This is the most complex field in the entire API. See this example for more details.

Example

{
  // ...
  "input": {
    "messages": [
      // If you don't have a user (or assistant) schema...
      {
        "role": "user", // (or "assistant")
        "content": "What is the weather in Tokyo?"
      },
      // If you have a user (or assistant) schema...
      {
        "role": "user", // (or "assistant")
        "content": [
          {
            "type": "text",
            "arguments": {
              "location": "Tokyo"
            }
          }
        ]
      },
      // If the model previously called a tool...
      {
        "role": "assistant",
        "content": [
          {
            "type": "tool_call",
            "id": "0",
            "name": "get_temperature",
            "arguments": "{\"location\": \"Tokyo\"}"
          }
        ]
      },
      // ...and you're providing the result of that tool call...
      {
        "role": "user",
        "content": [
          {
            "type": "tool_result",
            "id": "0",
            "name": "get_temperature",
            "result": "70"
          }
        ]
      },
      // You can also specify a text message using a content block...
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What about NYC?" // (or object if there is a schema)
          }
        ]
      },
      // You can also provide multiple content blocks in a single message...
      {
        "role": "assistant",
        "content": [
          {
            "type": "text",
            "text": "Sure, I can help you with that." // (or object if there is a schema)
          },
          {
            "type": "tool_call",
            "id": "0",
            "name": "get_temperature",
            "arguments": "{\"location\": \"New York\"}"
          }
        ]
      }
      // ...
    ]
    // ...
  }
  // ...
}

`input.system`

Type: string or object
Required: no

The input for the system message. If the function does not have a system schema, this field should be a string. If the function has a system schema, this field should be an object that matches the schema.

`model_name`

Type: string
Required: either model_name or function_name must be provided

The name of the model to call. Under the hood, the gateway will use a built-in passthrough chat function called tensorzero::default.

To call…	Use this format…
A function defined as `[functions.my_function]` in your `tensorzero.toml` configuration file	`function_name="my_function"` (not `model_name`)
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, gcp_vertex_anthropic, gcp_vertex_gemini, google_ai_studio_gemini, groq, hyperbolic, 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]
# ...

[functions.extract-data]
# ...

Then:

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

Be careful about the different prefixes: model_name="gpt-4o" will use the [models.gpt-4o] model defined in the tensorzero.toml file, whereas model_name="openai::gpt-4o" will call the OpenAI API directly for the gpt-4o model.

`output_schema`

Type: object (valid JSON Schema)
Required: no

If set, this schema will override the output_schema defined in the function configuration for a JSON function. This dynamic output schema is used for validating the output of the function, and sent to providers which support structured outputs.

`parallel_tool_calls`

Type: boolean
Required: no

If true, the function will be allowed to request multiple tool calls in a single conversation turn. If not set, we default to the configuration value for the function being called. Most model providers do not support parallel tool calls. In those cases, the gateway ignores this field. At the moment, only Fireworks AI and OpenAI support parallel tool calls.

`params`

Type: object (see below)
Required: no (default: {})

Override inference-time parameters for a particular variant type. This fields allows for dynamic inference parameters, i.e. defining parameters at runtime. This field’s format is { variant_type: { param: value, ... }, ... }. You should prefer to set these parameters in the configuration file if possible. Only use this field if you need to set these parameters dynamically at runtime. Note that the parameters will apply to every variant of the specified type. Currently, we support the following:

chat_completion
- frequency_penalty
- json_mode
- max_tokens
- presence_penalty
- seed
- stop_sequences
- temperature
- top_p

See Configuration Reference for more details on the parameters, and Examples below for usage.

Example

For example, if you wanted to dynamically override the temperature parameter for a chat_completion variants, you’d include the following in the request body:

{
  // ...
  "params": {
    "chat_completion": {
      "temperature": 0.7
    }
  }
  // ...
}

See “Chat Function with Dynamic Inference Parameters” for a complete example.

`stream`

Type: boolean
Required: no

If true, the gateway will stream the response from the model provider.

`tags`

Type: flat JSON object with string keys and values
Required: no

User-provided tags to associate with the inference. For example, {"user_id": "123"} or {"author": "Alice"}.

`tool_choice`

Type: string
Required: no

If set, overrides the tool choice strategy for the request. 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 section of the configuration file or provided in additional_tools.

`variant_name`

Type: string
Required: no

If set, pins the inference request to a particular variant (not recommended). You should generally not set this field, and instead let the TensorZero gateway assign a variant. This field is primarily used for testing or debugging purposes.

Response

The response format depends on the function type (as defined in the configuration file) and whether the response is streamed or not.

Chat Function

When the function type is chat, the response is structured as follows.

In regular (non-streaming) mode, the response is a JSON object with the following fields:

`content`

Type: a list of content blocks (see below)

The content blocks generated by the model.A content block can have type equal to text and tool_call. Reasoning models (e.g. DeepSeek R1) might also include thought content blocks.If type is text, the content block has the following fields:

text: The text for the content block.

If type is tool_call, the content block has the following fields:

arguments (object): The validated arguments for the tool call (null if invalid).
id (string): The ID of the content block.
name (string): The validated name of the tool (null if invalid).
raw_arguments (string): The arguments for the tool call generated by the model (which might be invalid).
raw_name (string): The name of the tool generated by the model (which might be invalid).

If type is thought, the content block has the following fields:

text (string): The text of the thought.

If the model provider responds with a content block of an unknown type, it will be included in the response as a content block of type unknown with the following additional fields:

data: The original content block from the provider, without any validation or transformation by TensorZero.
model_provider_name: The fully-qualified name of the model provider that returned the content block.

For example, if the model provider your_model_provider_name returns a content block of type daydreaming, it will be included in the response like this:

{
  "type": "unknown",
  "data": {
    "type": "daydreaming",
    "dream": "..."
  },
  "model_provider_name": "tensorzero::model_name::your_model_name::provider_name::your_model_provider_name"
}

`episode_id`

Type: UUID

The ID of the episode associated with the inference.

`inference_id`

Type: UUID

The ID assigned to the inference.

`original_response`

Type: string (optional)

The original response from the model provider (only available when include_original_response is true).The returned data depends on the variant type:

chat_completion: raw response from the inference to the model
experimental_best_of_n_sampling: raw response from the inference to the evaluator
experimental_mixture_of_n_sampling: raw response from the inference to the fuser
experimental_dynamic_in_context_learning: raw response from the inference to the model
experimental_chain_of_thought: raw response from the inference to the model

`variant_name`

Type: string

The name of the variant used for the inference.

`usage`

Type: object (optional)

The usage metrics for the inference.The object has the following fields:

input_tokens: The number of input tokens used for the inference.
output_tokens: The number of output tokens used for the inference.

JSON Function

When the function type is json, the response is structured as follows.

In regular (non-streaming) mode, the response is a JSON object with the following fields:

`inference_id`

Type: UUID

The ID assigned to the inference.

`episode_id`

Type: UUID

The ID of the episode associated with the inference.

`original_response`

Type: string (optional)

The original response from the model provider (only available when include_original_response is true).The returned data depends on the variant type:

chat_completion: raw response from the inference to the model
experimental_best_of_n_sampling: raw response from the inference to the evaluator
experimental_mixture_of_n_sampling: raw response from the inference to the fuser
experimental_dynamic_in_context_learning: raw response from the inference to the model
experimental_chain_of_thought: raw response from the inference to the model

`output`

Type: object (see below)

The output object contains the following fields:

raw: The raw response from the model provider (which might be invalid JSON).
parsed: The parsed response from the model provider (null if invalid JSON).

`variant_name`

Type: string

The name of the variant used for the inference.

`usage`

Type: object (optional)

The usage metrics for the inference.The object has the following fields:

input_tokens: The number of input tokens used for the inference.
output_tokens: The number of output tokens used for the inference.

Examples

Chat Function

Configuration

// tensorzero.toml
# ...
[functions.draft_email]
type = "chat"
# ...

Request

POST /inference

from tensorzero import AsyncTensorZeroGateway

async with await AsyncTensorZeroGateway.build_http(gateway_url="http://localhost:3000") as client:
    result = await client.inference(
        function_name="draft_email",
        input={
            "system": "You are an AI assistant...",
            "messages": [
                {
                  "role": "user",
                  "content": "I need to write an email to Gabriel explaining..."
                }
            ]
        }
        # optional: stream=True,
    )

Response

POST /inference

{
  "inference_id": "00000000-0000-0000-0000-000000000000",
  "episode_id": "11111111-1111-1111-1111-111111111111",
  "variant_name": "prompt_v1",
  "content": [
    {
      "type": "text",
      "text": "Hi Gabriel,\n\nI noticed...",
    }
  ]
  "usage": {
    "input_tokens": 100,
    "output_tokens": 100
  }
}

Chat Function with Schemas

Configuration

// tensorzero.toml
# ...
[functions.draft_email]
type = "chat"
system_schema = "system_schema.json"
user_schema = "user_schema.json"
# ...

// system_schema.json
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "properties": {
    "tone": {
      "type": "string"
    }
  },
  "required": ["tone"],
  "additionalProperties": false
}

// user_schema.json
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "properties": {
    "recipient": {
      "type": "string"
    },
    "email_purpose": {
      "type": "string"
    }
  },
  "required": ["recipient", "email_purpose"],
  "additionalProperties": false
}

Request

POST /inference

from tensorzero import AsyncTensorZeroGateway

async with await AsyncTensorZeroGateway.build_http(gateway_url="http://localhost:3000") as client:
    result = await client.inference(
        function_name="draft_email",
        input={
            "system": {"tone": "casual"},
            "messages": [
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "text",
                            "arguments": {
                                "recipient": "Gabriel",
                                "email_purpose": "Request a meeting to..."
                            }
                        }
                    ]
                }
            ]
        }
        # optional: stream=True,
    )

Response

POST /inference

{
  "inference_id": "00000000-0000-0000-0000-000000000000",
  "episode_id": "11111111-1111-1111-1111-111111111111",
  "variant_name": "prompt_v1",
  "content": [
    {
      "type": "text",
      "text": "Hi Gabriel,\n\nI noticed...",
    }
  ]
  "usage": {
    "input_tokens": 100,
    "output_tokens": 100
  }
}

Chat Function with Tool Use

Configuration

// tensorzero.toml
# ...

[functions.weather_bot]
type = "chat"
tools = ["get_temperature"]

# ...

[tools.get_temperature]
description = "Get the current temperature in a given location"
parameters = "get_temperature.json"

# ...

// get_temperature.json
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "properties": {
    "location": {
      "type": "string",
      "description": "The location to get the temperature for (e.g. \"New York\")"
    },
    "units": {
      "type": "string",
      "description": "The units to get the temperature in (must be \"fahrenheit\" or \"celsius\")",
      "enum": ["fahrenheit", "celsius"]
    }
  },
  "required": ["location"],
  "additionalProperties": false
}

Request

POST /inference

from tensorzero import AsyncTensorZeroGateway

async with await AsyncTensorZeroGateway.build_http(gateway_url="http://localhost:3000") as client:
    result = await client.inference(
        function_name="weather_bot",
        input={
            "messages": [
                {
                    "role": "user",
                    "content": "What is the weather like in Tokyo?"
                }
            ]
        }
        # optional: stream=True,
    )

Response

POST /inference

{
  "inference_id": "00000000-0000-0000-0000-000000000000",
  "episode_id": "11111111-1111-1111-1111-111111111111",
  "variant_name": "prompt_v1",
  "content": [
    {
      "type": "tool_call",
      "arguments": {
        "location": "Tokyo",
        "units": "celsius"
      },
      "id": "123456789",
      "name": "get_temperature",
      "raw_arguments": "{\"location\": \"Tokyo\", \"units\": \"celsius\"}",
      "raw_name": "get_temperature"
    }
  ],
  "usage": {
    "input_tokens": 100,
    "output_tokens": 100
  }
}

Chat Function with Multi-Turn Tool Use

Configuration

// tensorzero.toml
# ...

[functions.weather_bot]
type = "chat"
tools = ["get_temperature"]

# ...

[tools.get_temperature]
description = "Get the current temperature in a given location"
parameters = "get_temperature.json"

# ...

// get_temperature.json
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "properties": {
    "location": {
      "type": "string",
      "description": "The location to get the temperature for (e.g. \"New York\")"
    },
    "units": {
      "type": "string",
      "description": "The units to get the temperature in (must be \"fahrenheit\" or \"celsius\")",
      "enum": ["fahrenheit", "celsius"]
    }
  },
  "required": ["location"],
  "additionalProperties": false
}

Request

POST /inference

from tensorzero import AsyncTensorZeroGateway

async with await AsyncTensorZeroGateway.build_http(gateway_url="http://localhost:3000") as client:
    result = await client.inference(
        function_name="weather_bot",
        input={
            "messages": [
                {
                    "role": "user",
                    "content": "What is the weather like in Tokyo?"
                },
                {
                    "role": "assistant",
                    "content": [
                        {
                            "type": "tool_call",
                            "arguments": {
                                "location": "Tokyo",
                                "units": "celsius"
                            },
                            "id": "123456789",
                            "name": "get_temperature",
                        }
                    ]
                },
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "tool_result",
                            "id": "123456789",
                            "name": "get_temperature",
                            "result": "25"  # the tool result must be a string
                        }
                    ]
                }
            ]
        }
        # optional: stream=True,
    )

Response

POST /inference

{
  "inference_id": "00000000-0000-0000-0000-000000000000",
  "episode_id": "11111111-1111-1111-1111-111111111111",
  "variant_name": "prompt_v1",
  "content": [
    {
      "type": "text",
      "content": [
        {
          "type": "text",
          "text": "The weather in Tokyo is 25 degrees Celsius."
        }
      ]
    }
  ],
  "usage": {
    "input_tokens": 100,
    "output_tokens": 100
  }
}

Chat Function with Dynamic Tool Use

Configuration

// tensorzero.toml
# ...

[functions.weather_bot]
type = "chat"
# Note: no `tools = ["get_temperature"]` field in configuration

# ...

Request

POST /inference

from tensorzero import AsyncTensorZeroGateway

async with await AsyncTensorZeroGateway.build_http(gateway_url="http://localhost:3000") as client:
    result = await client.inference(
        function_name="weather_bot",
        input={
            "messages": [
                {
                    "role": "user",
                    "content": "What is the weather like in Tokyo?"
                }
            ]
        },
        additional_tools=[
            {
                "name": "get_temperature",
                "description": "Get the current temperature in a given location",
                "parameters": {
                    "$schema": "http://json-schema.org/draft-07/schema#",
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The location to get the temperature for (e.g. \"New York\")"
                        },
                        "units": {
                            "type": "string",
                            "description": "The units to get the temperature in (must be \"fahrenheit\" or \"celsius\")",
                            "enum": ["fahrenheit", "celsius"]
                        }
                    },
                    "required": ["location"],
                    "additionalProperties": false
                }
            }
        ],
        # optional: stream=True,
    )

Response

POST /inference

{
  "inference_id": "00000000-0000-0000-0000-000000000000",
  "episode_id": "11111111-1111-1111-1111-111111111111",
  "variant_name": "prompt_v1",
  "content": [
    {
      "type": "tool_call",
      "arguments": {
        "location": "Tokyo",
        "units": "celsius"
      },
      "id": "123456789",
      "name": "get_temperature",
      "raw_arguments": "{\"location\": \"Tokyo\", \"units\": \"celsius\"}",
      "raw_name": "get_temperature"
    }
  ],
  "usage": {
    "input_tokens": 100,
    "output_tokens": 100
  }
}

Chat Function with Dynamic Inference Parameters

Configuration

// tensorzero.toml
# ...
[functions.draft_email]
type = "chat"
# ...

[functions.draft_email.variants.prompt_v1]
type = "chat_completion"
temperature = 0.5  # the API request will override this value
# ...

Request

POST /inference

from tensorzero import AsyncTensorZeroGateway

async with await AsyncTensorZeroGateway.build_http(gateway_url="http://localhost:3000") as client:
    result = await client.inference(
        function_name="draft_email",
        input={
            "system": "You are an AI assistant...",
            "messages": [
                {
                    "role": "user",
                    "content": "I need to write an email to Gabriel explaining..."
                }
            ]
        },
        # Override parameters for every variant with type "chat_completion"
        params={
            "chat_completion": {
                "temperature": 0.7,
            }
        },
        # optional: stream=True,
    )

Response

POST /inference

{
  "inference_id": "00000000-0000-0000-0000-000000000000",
  "episode_id": "11111111-1111-1111-1111-111111111111",
  "variant_name": "prompt_v1",
  "content": [
    {
      "type": "text",
      "text": "Hi Gabriel,\n\nI noticed...",
    }
  ]
  "usage": {
    "input_tokens": 100,
    "output_tokens": 100
  }
}

JSON Function

Configuration

// tensorzero.toml
# ...
[functions.extract_email]
type = "json"
output_schema = "output_schema.json"
# ...

// output_schema.json
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "properties": {
    "email": {
      "type": "string"
    }
  },
  "required": ["email"]
}

Request

POST /inference

from tensorzero import AsyncTensorZeroGateway

async with await AsyncTensorZeroGateway.build_http(gateway_url="http://localhost:3000") as client:
    result = await client.inference(
        function_name="extract_email",
        input={
            "system": "You are an AI assistant...",
            "messages": [
                {
                    "role": "user",
                    "content": "...blah blah blah [email protected] blah blah blah..."
                }
            ]
        }
        # optional: stream=True,
    )

Response

POST /inference

{
  "inference_id": "00000000-0000-0000-0000-000000000000",
  "episode_id": "11111111-1111-1111-1111-111111111111",
  "variant_name": "prompt_v1",
  "output": {
    "raw": "{\"email\": \"[email protected]\"}",
    "parsed": {
      "email": "[email protected]"
    }
  }
  "usage": {
    "input_tokens": 100,
    "output_tokens": 100
  }
}

Introduction

Gateway

Optimization

Evaluations

UI

​POST /inference

​Request

​additional_tools

​allowed_tools

​cache_options

cache_options.enabled

cache_options.max_age_s

​credentials

​dryrun

​episode_id

​extra_body

​extra_headers

​function_name

​include_original_response

​input

input.messages

input.system

​model_name

​output_schema

​parallel_tool_calls

​params

​stream

​tags

​tool_choice

​variant_name

​Response

​Chat Function

content

episode_id

inference_id

original_response

variant_name

usage

​JSON Function

inference_id

episode_id

original_response

output

variant_name

usage

​Examples

​Chat Function

​Chat Function with Schemas

​Chat Function with Tool Use

​Chat Function with Multi-Turn Tool Use

​Chat Function with Dynamic Tool Use

​Chat Function with Dynamic Inference Parameters

​JSON Function

`POST /inference`

Request

`additional_tools`

`allowed_tools`

`cache_options`

`cache_options.enabled`

`cache_options.max_age_s`

`credentials`

`dryrun`

`episode_id`

`extra_body`

`extra_headers`

`function_name`

`include_original_response`

`input`

`input.messages`

`input.system`

`model_name`

`output_schema`

`parallel_tool_calls`

`params`

`stream`

`tags`

`tool_choice`

`variant_name`

Response

Chat Function

`content`

`episode_id`

`inference_id`

`original_response`

`variant_name`

`usage`

JSON Function

`inference_id`

`episode_id`

`original_response`

`output`

`variant_name`

`usage`

Examples

Chat Function

Chat Function with Schemas

Chat Function with Tool Use

Chat Function with Multi-Turn Tool Use

Chat Function with Dynamic Tool Use

Chat Function with Dynamic Inference Parameters

JSON Function