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POST /openai/v1/chat/completions

The /openai/v1/chat/completions endpoint allows TensorZero users to make TensorZero inferences with the OpenAI client. The gateway translates the OpenAI request parameters into the arguments expected by the inference endpoint and calls the same underlying implementation. This endpoint supports most of the features supported by the inference endpoint, but there are some limitations. Most notably, this endpoint doesn’t support dynamic credentials, so they must be specified with a different method.
See the API Reference for POST /inference for more details on inference with the native TensorZero API.

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Request

The OpenAI-compatible inference endpoints translate the OpenAI request parameters into the arguments expected by the inference endpoint. TensorZero-specific parameters are prefixed with tensorzero:: (e.g. tensorzero::episode_id). These fields should be provided as extra body parameters in the request body.
The gateway will use the credentials specified in the tensorzero.toml file. In most cases, these credentials will be environment variables available to the TensorZero gateway — not your OpenAI client.API keys sent from the OpenAI client will be ignored.

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tensorzero::cache_options

  • Type: object
  • Required: no
Controls caching behavior for inference requests. This object accepts two fields:
  • enabled (string): The cache mode. Can 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
  • max_age_s (integer or null): Maximum age in seconds for cache entries to be considered valid when reading from cache. Does not set a TTL for cache expiration. Default is null (no age limit).
When using the OpenAI client libraries, pass this parameter via extra_body. See the Inference Caching guide for more details.

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tensorzero::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. 
[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"
# ...

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

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tensorzero::deny_unknown_fields

  • Type: boolean
  • Required: no (default: false)
If true, the gateway will return an error if the request contains any unknown or unrecognized fields. By default, unknown fields are ignored with a warning logged. This field does not affect the tensorzero::extra_body field, only unknown fields at the root of the request body. This field should be provided as an extra body parameter in the request body. 
response = oai.chat.completions.create(
    model="tensorzero::model_name::openai::gpt-5-mini",
    messages=[
        {
            "role": "user",
            "content": "Tell me a fun fact.",
        }
    ],
    extra_body={
        "tensorzero::deny_unknown_fields": True,
    },
    ultrathink=True,  # made-up parameter → `deny_unknown_fields` would reject this request
)

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tensorzero::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. This field should be provided as an extra body parameter in the request body.

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tensorzero::episode_id

  • Type: UUID
  • Required: no
The ID of an existing episode to associate the inference with. If null, the gateway will generate a new episode ID and return it in the response. See Episodes for more information. This field should be provided as an extra body parameter in the request body.

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tensorzero::extra_body

  • Type: array of objects (see below)
  • Required: no
The tensorzero::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.
The OpenAI SDKs generally also support such functionality.If you use the OpenAI SDK’s extra_body field, it will override the request from the client to the gateway. If you use tensorzero::extra_body, it will override the request from the gateway to the model provider.
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.
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…
{
  // ...
  "tensorzero::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"
    }
  }
}

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tensorzero::extra_headers

  • Type: array of objects (see below)
  • Required: no
The tensorzero::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.
The OpenAI SDKs generally also support such functionality.If you use the OpenAI SDK’s extra_headers field, it will override the request from the client to the gateway. If you use tensorzero::extra_headers, it will override the request from the gateway to the model provider.
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.
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

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tensorzero::params

  • Type: object
  • Required: no
Allows you to override inference parameters dynamically at request time. This field accepts an object with a chat_completion field containing any of the following parameters:
  • frequency_penalty (float): Penalizes tokens based on their frequency
  • json_mode (object): Controls JSON output formatting
  • max_tokens (integer): Maximum number of tokens to generate
  • presence_penalty (float): Penalizes tokens based on their presence
  • reasoning_effort (string): Effort level for reasoning models
  • seed (integer): Random seed for deterministic outputs
  • service_tier (string): Service tier for the request
  • stop_sequences (list of strings): Sequences that stop generation
  • temperature (float): Controls randomness in the output
  • thinking_budget_tokens (integer): Token budget for thinking/reasoning
  • top_p (float): Nucleus sampling parameter
  • verbosity (string): Output verbosity level
When using the OpenAI-compatible endpoint, parameters provided directly in the request body (e.g., top-level temperature, max_tokens) take precedence over values specified in tensorzero::params.
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:3000/openai/v1",
    api_key="your_api_key",
)

response = client.chat.completions.create(
    model="tensorzero::function_name::my_function",
    messages=[
        {"role": "user", "content": "Explain quantum computing"}
    ],
    extra_body={
        "tensorzero::params": {
            "chat_completion": {
                "temperature": 0.7,
                "max_tokens": 500,
                "reasoning_effort": "high"
            }
        }
    }
)

​
tensorzero::provider_tools

  • Type: array of objects
  • Required: no (default: [])
A list of provider-specific built-in tools that can be used by the model during inference. These are tools that run server-side on the provider’s infrastructure, such as OpenAI’s web search tool. Each object in the array has the following fields:
  • scope (object, optional): Limits which model/provider combination can use this tool. If omitted, the tool is available to all compatible providers.
    • model_name (string): The model name as defined in your configuration
    • model_provider_name (string): The provider name for that model
  • tool (object, required): The provider-specific tool configuration as defined by the provider’s API
When using OpenAI client libraries, pass this parameter via extra_body. This field allows for dynamic provider tool configuration at runtime. You should prefer to define provider tools in the configuration file if possible (see Configuration Reference). Only use this field if dynamic provider tool configuration is necessary for your use case. 
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:3000/openai/v1",
    api_key="your_api_key",
)

response = client.chat.completions.create(
    model="tensorzero::function_name::my_function",
    messages=[
        {"role": "user", "content": "What were the latest developments in AI this week?"}
    ],
    extra_body={
        "tensorzero::provider_tools": [
            {
                "tool": {
                    "type": "web_search"
                }
            }
        ]
    }
)
This makes the web search tool available to all compatible providers configured for the function.
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:3000/openai/v1",
    api_key="your_api_key",
)

response = client.chat.completions.create(
    model="tensorzero::function_name::my_function",
    messages=[
        {"role": "user", "content": "What were the latest developments in AI this week?"}
    ],
    extra_body={
        "tensorzero::provider_tools": [
            {
                "scope": {
                    "model_name": "gpt-5-mini",
                    "model_provider_name": "openai"
                },
                "tool": {
                    "type": "web_search"
                }
            }
        ]
    }
)
This makes the web search tool available only to the OpenAI provider for the gpt-5-mini model.

​
tensorzero::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"}.

​
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. Overrides the frequency_penalty setting for any chat completion variants being used.

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max_completion_tokens

  • Type: integer
  • Required: no (default: null)
Limits the number of tokens that can be generated by the model in a chat completion variant. If both this and max_tokens are set, the smaller value is used.

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max_tokens

  • Type: integer
  • Required: no (default: null)
Limits the number of tokens that can be generated by the model in a chat completion variant. If both this and max_completion_tokens are set, the smaller value is used.

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messages

  • Type: list
  • Required: yes
A list of messages to provide to the model. Each message is an object with the following fields:
  • role (required): The role of the message sender in an OpenAI message (assistant, system, tool, or user).
  • content (required for user and system messages and optional for assistant and tool messages): The content of the message. The content must be either a string or an array of content blocks (see below).
  • tool_calls (optional for assistant messages, otherwise disallowed): A list of tool calls. Each tool call is an object with the following fields:
    • id: A unique identifier for the tool call
    • type: The type of tool being called (currently only "function" is supported)
    • function: An object containing:
      • name: The name of the function to call
      • arguments: A JSON string containing the function arguments
  • tool_call_id (required for tool messages, otherwise disallowed): The ID of the tool call to associate with the message. This should be one that was originally returned by the gateway in a tool call id field.
A content block is an object that can have type text, image_url, or TensorZero-specific types. If the content block has type text, it must have either of the following additional fields:
  • text: The text for the content block.
  • tensorzero::arguments: A JSON object containing the function arguments for TensorZero functions with templates and schemas (see Create a prompt template for details).
If a content block has type image_url, it must have the following additional fields:
  • "image_url": A JSON object with the following fields:
    • url: The URL for a remote image (e.g. "https://example.com/image.png") or base64-encoded data for an embedded image (e.g. "data:image/png;base64,...").
    • detail (optional): Controls the fidelity of image processing. Only applies to image files; ignored for other file types. Can be low, high, or auto. Affects token consumption and image quality.
The TensorZero-specific content block types are:
  • tensorzero::raw_text: Bypasses templates and schemas, sending text directly to the model. Useful for testing prompts or dynamic injection without configuration changes. Must have a value field containing the text.
  • tensorzero::template: Explicitly specify a template to use. Must have name and arguments fields.

​
model

  • Type: string
  • Required: yes
The name of the TensorZero function or model being called, with the appropriate prefix.
To call…Use this format…
A function defined as [functions.my_function] in your tensorzero.toml configuration filetensorzero::function_name::my_function
A model defined as [models.my_model] in your tensorzero.toml configuration filetensorzero::model_name::my_model
A model offered by a model provider, without defining it in your tensorzero.toml configuration file (if supported, see below)tensorzero::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:
  • tensorzero::function_name::extract-data calls the extract-data function defined above.
  • tensorzero::model_name::gpt-4o calls the gpt-4o model in your configuration, which supports fallback from openai to azure. See Retries & Fallbacks for details.
  • tensorzero::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: tensorzero::model_name::gpt-4o will use the [models.gpt-4o] model defined in the tensorzero.toml file, whereas tensorzero::model_name::openai::gpt-4o will call the OpenAI API directly for the gpt-4o model.

​
parallel_tool_calls

  • Type: boolean
  • Required: no (default: null)
Overrides the parallel_tool_calls setting for the function being called.

​
presence_penalty

  • Type: float
  • Required: no (default: null)
Penalizes new tokens based on whether they appear in the text so far if positive, encourages them if negative. Overrides the presence_penalty setting for any chat completion variants being used.

​
response_format

  • Type: either a string or an object
  • Required: no (default: null)
Options here are "text", "json_object", and "{"type": "json_schema", "schema": ...}", where the schema field contains a valid JSON schema. This field is not actually respected except for the "json_schema" variant, in which the schema field can be used to dynamically set the output schema for a json function.

​
seed

  • Type: integer
  • Required: no (default: null)
Overrides the seed setting for any chat completion variants being used.

​
stop_sequences

  • Type: list of strings
  • Required: no (default: null)
Overrides the stop_sequences setting for any chat completion variants being used.

​
stream

  • Type: boolean
  • Required: no (default: false)
If true, the gateway will stream the response to the client in an OpenAI-compatible format.

​
stream_options

  • Type: object with field "include_usage"
  • Required: no (default: null)
If "include_usage" is true, the gateway will include usage information in the response.
If the following stream_options is provided…
{
  ...
  "stream_options": {
    "include_usage": true
  }
  ...
}
…then the gateway will include usage information in the response.
{
  ...
  "usage": {
    "prompt_tokens": 123,
    "completion_tokens": 456,
    "total_tokens": 579
  }
  ...

​
temperature

  • Type: float
  • Required: no (default: null)
Overrides the temperature setting for any chat completion variants being used.

​
tools

  • Type: list of tool objects (see below)
  • Required: no (default: null)
Allows the user to dynamically specify tools at inference time in addition to those that are specified in the configuration. Each tool object has the following structure:
  • type: Must be "function"
  • function: An object containing:
    • name: The name of the function (string, required)
    • description: A description of what the function does (string, optional)
    • parameters: A JSON Schema object describing the function’s parameters (required)
    • strict: Whether to enforce strict schema validation (boolean, defaults to false)

​
tool_choice

  • Type: string or object
  • Required: no (default: "none" if no tools are present, "auto" if tools are present)
Controls which (if any) tool is called by the model by overriding the value in configuration. Supported values:
  • "none": The model will not call any tool and instead generates a message
  • "auto": The model can pick between generating a message or calling one or more tools
  • "required": The model must call one or more tools
  • {"type": "function", "function": {"name": "my_function"}}: Forces the model to call the specified tool
  • {"type": "allowed_tools", "allowed_tools": {"tools": [...], "mode": "auto"|"required"}}: Restricts which tools can be called

​
top_p

  • Type: float
  • Required: no (default: null)
Overrides the top_p setting for any chat completion variants being used.

​
tensorzero::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. This field should be provided as an extra body parameter in the request body.

​
Response

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

​
choices

  • Type: list of choice objects, where each choice contains:
    • index: A zero-based index indicating the choice’s position in the list (integer)
    • finish_reason: Always "stop".
    • message: An object containing:
      • content: The message content (string, optional)
      • tool_calls: List of tool calls made by the model (optional). The format is the same as in the request.
      • role: The role of the message sender (always "assistant").
The OpenAI-compatible inference endpoint can’t handle unknown content blocks in the response. If the model provider returns an unknown content block, the gateway will drop the content block from the response and log a warning.If you need to access unknown content blocks, use the native TensorZero API. See the Inference API Reference for details.

​
created

  • Type: integer
The Unix timestamp (in seconds) of when the inference was created.

​
episode_id

  • Type: UUID
The ID of the episode that the inference was created for.

​
id

  • Type: UUID
The inference ID.

​
model

  • Type: string
The name of the variant that was actually used for the inference.

​
object

  • Type: string
The type of the inference object (always "chat.completion").

​
system_fingerprint

  • Type: string
Always ""

​
usage

  • Type: object
Contains token usage information for the request and response, with the following fields:
  • prompt_tokens: Number of tokens in the prompt (integer)
  • completion_tokens: Number of tokens in the completion (integer)
  • total_tokens: Total number of tokens used (integer)

​
Examples

​
Chat Function with Structured System Prompt
Configuration
// tensorzero.toml
# ...
[functions.draft_email]
type = "chat"
system_schema = "functions/draft_email/system_schema.json"
# ...

// functions/draft_email/system_schema.json
{
  "type": "object",
  "properties": {
    "assistant_name": { "type": "string" }
  }
}
Request
  • Python
  • HTTP
POST /inference
from openai import AsyncOpenAI

async with AsyncOpenAI(
    base_url="http://localhost:3000/openai/v1"
) as client:
    result = await client.chat.completions.create(
        # there already was an episode_id from an earlier inference
        extra_body={"tensorzero::episode_id": str(episode_id)},
        messages=[
            {
                "role": "system",
                "content": [{"assistant_name": "Alfred Pennyworth"}]
                # NOTE: the JSON is in an array here so that a structured system message can be sent
            },
            {
                "role": "user",
                "content": "I need to write an email to Gabriel explaining..."
            }
        ],
        model="tensorzero::function_name::draft_email",
        temperature=0.4,
        # Optional: stream=True
    )
Response
  • Regular
  • Streaming
POST /inference
{
  "id": "00000000-0000-0000-0000-000000000000",
  "episode_id": "11111111-1111-1111-1111-111111111111",
  "model": "email_draft_variant",
  "choices": [
    {
      "index": 0,
      "finish_reason": "stop",
      "message": {
        "content": "Hi Gabriel,\n\nI noticed...",
        "role": "assistant"
      }
    }
  ],
  "usage": {
    "prompt_tokens": 100,
    "completion_tokens": 100,
    "total_tokens": 200
  }
}

​
Chat Function with Dynamic Tool Use
Configuration
// tensorzero.toml
# ...

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

# ...
Request
  • Python
  • HTTP
POST /inference
from openai import AsyncOpenAI

async with AsyncOpenAI(
    base_url="http://localhost:3000/openai/v1"
) as client:
    result = await client.chat.completions.create(
        model="tensorzero::function_name::weather_bot",
        input={
            "messages": [
                {
                    "role": "user",
                    "content": "What is the weather like in Tokyo?"
                }
            ]
        },
        tools=[
            {
              "type": "function",
              "function": {
                  "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
  • Regular
  • Streaming
POST /inference
{
  "id": "00000000-0000-0000-0000-000000000000",
  "episode_id": "11111111-1111-1111-1111-111111111111",
  "model": "weather_bot_variant",
  "choices": [
    {
      "index": 0,
      "finish_reason": "stop",
      "message": {
        "content": null,
        "tool_calls": [
          {
            "id": "123456789",
            "type": "function",
            "function": {
              "name": "get_temperature",
              "arguments": "{\"location\": \"Tokyo\", \"units\": \"celsius\"}"
            }
          }
        ],
        "role": "assistant"
      }
    }
  ],
  "usage": {
    "prompt_tokens": 100,
    "completion_tokens": 100,
    "total_tokens": 200
  }
}

​
Json Function with Dynamic Output Schema
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
  • Python
  • HTTP
POST /inference
from openai import AsyncOpenAI

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

async with AsyncOpenAI(
    base_url="http://localhost:3000/openai/v1"
) as client:
    result = await client.chat.completions.create(
        model="tensorzero::function_name::extract_email",
        input={
            "system": "You are an AI assistant...",
            "messages": [
                {
                    "role": "user",
                    "content": "...blah blah blah [email protected] blah blah blah..."
                }
            ]
        }
        # Override the output schema using the `response_format` field
        response_format={"type": "json_schema", "schema": dynamic_output_schema}
        # optional: stream=True,
    )
Response
  • Regular
  • Streaming
POST /inference
{
  "id": "00000000-0000-0000-0000-000000000000",
  "episode_id": "11111111-1111-1111-1111-111111111111",
  "model": "extract_email_variant",
  "choices": [
    {
      "index": 0,
      "finish_reason": "stop",
      "message": {
        "content": "{\"email\": \"[email protected]\", \"domain\": \"tensorzero.com\"}"
      }
    }
  ],
  "usage": {
    "prompt_tokens": 100,
    "completion_tokens": 100,
    "total_tokens": 200
  }
}