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API Reference

API Reference: Inference (OpenAI-Compatible)

API reference for the/openai/v1/chat/completions endpoint.

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 theinference endpoint and calls the same underlying implementation.This endpoint supports most of the features supported by theinference 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 theAPI Reference forPOST /inference for more details on inference with the native TensorZero API.

Request

The OpenAI-compatible inference endpoints translate the OpenAI request parameters into the arguments expected by theinference endpoint.TensorZero-specific parameters are prefixed withtensorzero:: (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 thetensorzero.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.

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 isnull (no age limit).
When using the OpenAI client libraries, pass this parameter viaextra_body.See theInference Caching guide for more details.

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 thedynamic location (e.g.dynamic::my_dynamic_api_key_name).See theconfiguration reference for more details.The gateway expects the credentials to be provided in thecredentials 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 typeapi_key_location ="dynamic::my_dynamic_api_key_name"# ...
{  // ...  "tensorzero::credentials": {    // ...    "my_dynamic_api_key_name":"sk-..."    // ...  }  // ...}

tensorzero::deny_unknown_fields

  • Type: boolean
  • Required: no (default:false)
Iftrue, 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 thetensorzero::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)

tensorzero::dryrun

  • Type: boolean
  • Required: no
Iftrue, 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.

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.SeeEpisodes for more information.This field should be provided as an extra body parameter in the request body.

tensorzero::extra_body

  • Type: array of objects (see below)
  • Required: no
Thetensorzero::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’sextra_body field, it will override the request from the client to the gateway.If you usetensorzero::extra_body, it will override the request from the gateway to the model provider.
Each object in the array must have two or three fields:
  • pointer: AJSON 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.
  • Optional: If one of the following is specified, the modification will only be applied to the specified variant, model, or model provider. If neither is specified, the modification applies to all model inferences.
    • variant_name
    • model_name
    • model_name andprovider_name
You can also setextra_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 followingextra_body in the inference request…
{  // ...  "tensorzero::extra_body": [    {      "variant_name":"my_variant",// or "model_name": "my_model", "provider_name": "my_provider"      "pointer":"/agi",      "value":true    },    {      // No `variant_name` or `model_name`/`provider_name` specified, so it applies to all variants and providers      "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"    }  }}

tensorzero::extra_headers

  • Type: array of objects (see below)
  • Required: no
Thetensorzero::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’sextra_headers field, it will override the request from the client to the gateway.If you usetensorzero::extra_headers, it will override the request from the gateway to the model provider.
Each object in the array must have two or three fields:
  • name: The name of the header to modify
  • value: The value to set the header to
  • Optional: If one of the following is specified, the modification will only be applied to the specified variant, model, or model provider. If neither is specified, the modification applies to all model inferences.
    • variant_name
    • model_name
    • model_name andprovider_name
You can also setextra_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 followingextra_headers…
{  "extra_headers": [    {      "variant_name":"my_variant",// or "model_name": "my_model", "provider_name": "my_provider"      "name":"Safety-Checks",      "value":"off"    },    {      // No `variant_name` or `model_name`/`provider_name` specified, so it applies to all variants and providers      "name":"Intelligence-Level",      "value":"AGI"    }  ]}
…overrides the request headers so thatSafety-Checks is set tooff only formy_variant, whileIntelligence-Level: AGI is applied globally to all variants and providers:
Safety-Checks: offIntelligence-Level: AGI

tensorzero::params

  • Type: object
  • Required: no
Allows you to override inference parameters dynamically at request time.This field accepts an object with achat_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, values specified intensorzero::params take precedence over parameters provided directly in the request body (e.g., top-leveltemperature,max_tokens) or inferred from other fields (e.g.,json_mode inferred fromresponse_format).
from openaiimport OpenAIclient= 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 viaextra_body.This field allows for dynamic provider tool configuration at runtime.You should prefer to define provider tools in the configuration file if possible (seeConfiguration Reference).Only use this field if dynamic provider tool configuration is necessary for your use case.
from openaiimport OpenAIclient= 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 openaiimport OpenAIclient= 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 thegpt-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 thefrequency_penalty setting for any chat completion variants being used.

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 andmax_tokens are set, the smaller value is used.

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 andmax_completion_tokens are set, the smaller value is used.

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, oruser).
  • content (required foruser andsystem messages and optional forassistant andtool messages): The content of the message.The content must be either a string or an array of content blocks (see below).
  • tool_calls (optional forassistant 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 fortool 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 callid field.
A content block is an object that can have typetext,image_url, or TensorZero-specific types.If the content block has typetext, 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 (seeCreate a prompt template for details).
If a content block has typeimage_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 below,high, orauto. Affects token consumption and image quality.
If a content block has typeinput_audio, it must have the following additional field:
  • input_audio: An object containing:
    • data: Base64-encoded audio data (without adata: prefix or MIME type header).
    • format: The audio format as a string (e.g.,"mp3","wav"). Note: The MIME type is detected from the actual audio bytes, and a warning is logged if the detected type differs from this field.
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 avalue field containing the text.
  • tensorzero::template: Explicitly specify a template to use. Must havename andarguments 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 yourtensorzero.toml configuration filetensorzero::function_name::my_function
A model defined as[models.my_model] in yourtensorzero.tomlconfiguration filetensorzero::model_name::my_model
A model offered by a model provider, without defining it in yourtensorzero.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, andxai.
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 theextract-data function defined above.
  • tensorzero::model_name::gpt-4o calls thegpt-4o model in your configuration, which supports fallback fromopenai toazure. SeeRetries & Fallbacks for details.
  • tensorzero::model_name::openai::gpt-4o calls the OpenAI API directly for thegpt-4o model, ignoring thegpt-4o model defined above.
Be careful about the different prefixes:tensorzero::model_name::gpt-4o will use the[models.gpt-4o] model defined in thetensorzero.toml file, whereastensorzero::model_name::openai::gpt-4o will call the OpenAI API directly for thegpt-4o model.

parallel_tool_calls

  • Type: boolean
  • Required: no (default:null)
Overrides theparallel_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 thepresence_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 theschema field can be used to dynamically set the output schema for ajson function.

seed

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

stop_sequences

  • Type: list of strings
  • Required: no (default:null)
Overrides thestop_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" istrue, the gateway will include usage information in the response.
If the followingstream_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 thetemperature setting for any chat completion variants being used.

tools

  • Type: list oftool 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.
Function Tools
Function tools are the typical tools used with LLMs.Each function 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)
OpenAI Custom Tools
OpenAI custom tools are only supported by OpenAI models (both Chat Completions and Responses APIs).Using custom tools with other providers will result in an error.
OpenAI custom tools support alternative output formats beyond JSON Schema, such as freeform text or grammar-constrained output.Each custom tool object has the following structure:
  • type: Must be"custom"
  • custom: An object containing:
    • name: The name of the tool (string, required)
    • description: A description of what the tool does (string, optional)
    • format: The output format for the tool (object, optional):
      • {"type": "text"}: Freeform text output
      • {"type": "grammar", "grammar": {"syntax": "lark", "definition": "..."}}: Output constrained by aLark grammar
      • {"type": "grammar", "grammar": {"syntax": "regex", "definition": "..."}}: Output constrained by a regular expression
from openaiimport OpenAIclient= OpenAI(    base_url="http://localhost:3000/openai/v1",    api_key="your_api_key",)response= client.chat.completions.create(    model="tensorzero::model_name::openai::gpt-5-mini",    messages=[        {"role":"user","content":"Generate Python code to print 'Hello, World!'"}    ],    tools=[        {            "type":"custom",            "custom": {                "name":"code_generator",                "description":"Generates Python code snippets",                "format": {"type":"text"}            }        }    ],)

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 thetop_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 ofchoice 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 theInference 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)
In streaming mode, the response is anSSE stream of JSON messages, followed by a final[DONE] message.Each JSON message has the following fields:

choices

  • Type: list
A list of choices from the model, where each choice contains:
  • index: The index of the choice (integer)
  • finish_reason: always ""
  • delta: An object containing either:
    • content: The next piece of generated text (string), or
    • tool_calls: A list of tool calls, each containing the next piece of the tool call being generated

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
  • Required: no
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 openaiimport AsyncOpenAIasync 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    )
POST /inference
curl -X POST http://localhost:3000/openai/v1/chat/completions \  -H "Content-Type: application/json" \  -H "episode_id: your_episode_id_here" \  -d '{    "messages": [      {        "role": "system",        "content": [{"assistant_name": "Alfred Pennyworth"}]      },      {        "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  }}
In streaming mode, the response is anSSE stream of JSON messages, followed by a final[DONE] message.Each JSON message has the following fields:
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",      "delta": {        "content":"Hi Gabriel,\n\nI noticed..."      }    }  ],  "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 openaiimport AsyncOpenAIasync 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": {                    "$schema":"http://json-schema.org/draft-07/schema#",                    "type":"object",                    "properties": {                        "location": {                            "type":"string",                            "description":"The location to get the temperature for (e.g.\"New York\")"                        },                        "units": {                            "type":"string",                            "description":"The units to get the temperature in (must be\"fahrenheit\" or\"celsius\")",                            "enum": ["fahrenheit","celsius"]                        }                    },                    "required": ["location"],                    "additionalProperties": false                }              }            }        ],        # optional: stream=True,    )
POST /inference
curl -X POST http://localhost:3000/openai/v1/chat/completions \  -H "Content-Type: application/json" \  -d '{    "model": "tensorzero::function_name::weather_bot",    "input": {      "messages": [        {          "role": "user",          "content": "What is the weather like in Tokyo?"        }      ]    },    "tools": [      {        "type": "function",        "function": {          "name": "get_temperature",          "description": "Get the current temperature in a given location",          "parameters": {            "$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  }}
In streaming mode, the response is anSSE stream of JSON messages, followed by a final[DONE] message.Each JSON message has the following fields:
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\":" // a tool arguments delta            }          }        ]      }    }  ],  "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{  "$schema":"http://json-schema.org/draft-07/schema#",  "type":"object",  "properties": {    "email": {      "type":"string"    }  },  "required": ["email"]}
Request
  • Python
  • HTTP
POST /inference
from openaiimport AsyncOpenAIdynamic_output_schema= {  "$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,    )
POST /inference
curl -X POST http://localhost:3000/openai/v1/chat/completions \  -H "Content-Type: application/json" \  -d '{    "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..."        }      ]    },    "response_format": {      "type": "json_schema",      "schema": {        "$schema": "http://json-schema.org/draft-07/schema#",        "type": "object",        "properties": {          "email": { "type": "string" },          "domain": { "type": "string" }        },        "required": ["email", "domain"]      }    },    // 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  }}
In streaming mode, the response is anSSE stream of JSON messages, followed by a final[DONE] message.Each JSON message has the following fields:
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\":" // a JSON content delta      }    }  ],  "usage": {    "prompt_tokens":100,    "completion_tokens":100,    "total_tokens":200  }}
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