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refactor(agent): drop AgentToolGroup for responses tools

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Ashwin Bharambe 2025-10-10 13:43:43 -07:00
parent c56b2deb7d
commit ce44b9d6f6
12 changed files with 4051 additions and 4225 deletions
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@ -15576,8 +15576,20 @@
"AgentConfig": { "AgentConfig": {
"type": "object", "type": "object",
"properties": { "properties": {
"sampling_params": { "max_output_tokens": {
"$ref": "#/components/schemas/SamplingParams" "type": "integer"
},
"temperature": {
"type": "number"
},
"top_p": {
"type": "number"
},
"stop": {
"type": "array",
"items": {
"type": "string"
}
}, },
"input_shields": { "input_shields": {
"type": "array", "type": "array",
@ -15591,40 +15603,25 @@
"type": "string" "type": "string"
} }
}, },
"toolgroups": { "tools": {
"type": "array", "type": "array",
"items": { "items": {
"$ref": "#/components/schemas/AgentTool" "$ref": "#/components/schemas/OpenAIResponseInputTool"
} }
}, },
"client_tools": { "client_tools": {
"type": "array", "type": "array",
"items": { "items": {
"$ref": "#/components/schemas/ToolDef" "oneOf": [
{
"$ref": "#/components/schemas/OpenAIResponseInputTool"
},
{
"$ref": "#/components/schemas/ToolDef"
}
]
} }
}, },
"tool_choice": {
"type": "string",
"enum": [
"auto",
"required",
"none"
],
"title": "ToolChoice",
"description": "Whether tool use is required or automatic. This is a hint to the model which may not be followed. It depends on the Instruction Following capabilities of the model.",
"deprecated": true
},
"tool_prompt_format": {
"type": "string",
"enum": [
"json",
"function_tag",
"python_list"
],
"title": "ToolPromptFormat",
"description": "Prompt format for calling custom / zero shot tools.",
"deprecated": true
},
"tool_config": { "tool_config": {
"$ref": "#/components/schemas/ToolConfig" "$ref": "#/components/schemas/ToolConfig"
}, },
@ -15650,7 +15647,7 @@
"description": "Optional flag indicating whether session data has to be persisted" "description": "Optional flag indicating whether session data has to be persisted"
}, },
"response_format": { "response_format": {
"$ref": "#/components/schemas/ResponseFormat", "$ref": "#/components/schemas/OpenAIResponseFormatParam",
"description": "Optional response format configuration" "description": "Optional response format configuration"
} }
}, },
@ -15662,232 +15659,6 @@
"title": "AgentConfig", "title": "AgentConfig",
"description": "Configuration for an agent." "description": "Configuration for an agent."
}, },
"AgentTool": {
"oneOf": [
{
"type": "string"
},
{
"type": "object",
"properties": {
"name": {
"type": "string"
},
"args": {
"type": "object",
"additionalProperties": {
"oneOf": [
{
"type": "null"
},
{
"type": "boolean"
},
{
"type": "number"
},
{
"type": "string"
},
{
"type": "array"
},
{
"type": "object"
}
]
}
}
},
"additionalProperties": false,
"required": [
"name",
"args"
],
"title": "AgentToolGroupWithArgs"
}
]
},
"GrammarResponseFormat": {
"type": "object",
"properties": {
"type": {
"type": "string",
"enum": [
"json_schema",
"grammar"
],
"description": "Must be \"grammar\" to identify this format type",
"const": "grammar",
"default": "grammar"
},
"bnf": {
"type": "object",
"additionalProperties": {
"oneOf": [
{
"type": "null"
},
{
"type": "boolean"
},
{
"type": "number"
},
{
"type": "string"
},
{
"type": "array"
},
{
"type": "object"
}
]
},
"description": "The BNF grammar specification the response should conform to"
}
},
"additionalProperties": false,
"required": [
"type",
"bnf"
],
"title": "GrammarResponseFormat",
"description": "Configuration for grammar-guided response generation."
},
"GreedySamplingStrategy": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "greedy",
"default": "greedy",
"description": "Must be \"greedy\" to identify this sampling strategy"
}
},
"additionalProperties": false,
"required": [
"type"
],
"title": "GreedySamplingStrategy",
"description": "Greedy sampling strategy that selects the highest probability token at each step."
},
"JsonSchemaResponseFormat": {
"type": "object",
"properties": {
"type": {
"type": "string",
"enum": [
"json_schema",
"grammar"
],
"description": "Must be \"json_schema\" to identify this format type",
"const": "json_schema",
"default": "json_schema"
},
"json_schema": {
"type": "object",
"additionalProperties": {
"oneOf": [
{
"type": "null"
},
{
"type": "boolean"
},
{
"type": "number"
},
{
"type": "string"
},
{
"type": "array"
},
{
"type": "object"
}
]
},
"description": "The JSON schema the response should conform to. In a Python SDK, this is often a `pydantic` model."
}
},
"additionalProperties": false,
"required": [
"type",
"json_schema"
],
"title": "JsonSchemaResponseFormat",
"description": "Configuration for JSON schema-guided response generation."
},
"ResponseFormat": {
"oneOf": [
{
"$ref": "#/components/schemas/JsonSchemaResponseFormat"
},
{
"$ref": "#/components/schemas/GrammarResponseFormat"
}
],
"discriminator": {
"propertyName": "type",
"mapping": {
"json_schema": "#/components/schemas/JsonSchemaResponseFormat",
"grammar": "#/components/schemas/GrammarResponseFormat"
}
}
},
"SamplingParams": {
"type": "object",
"properties": {
"strategy": {
"oneOf": [
{
"$ref": "#/components/schemas/GreedySamplingStrategy"
},
{
"$ref": "#/components/schemas/TopPSamplingStrategy"
},
{
"$ref": "#/components/schemas/TopKSamplingStrategy"
}
],
"discriminator": {
"propertyName": "type",
"mapping": {
"greedy": "#/components/schemas/GreedySamplingStrategy",
"top_p": "#/components/schemas/TopPSamplingStrategy",
"top_k": "#/components/schemas/TopKSamplingStrategy"
}
},
"description": "The sampling strategy."
},
"max_tokens": {
"type": "integer",
"default": 0,
"description": "The maximum number of tokens that can be generated in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length."
},
"repetition_penalty": {
"type": "number",
"default": 1.0,
"description": "Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics."
},
"stop": {
"type": "array",
"items": {
"type": "string"
},
"description": "Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence."
}
},
"additionalProperties": false,
"required": [
"strategy"
],
"title": "SamplingParams",
"description": "Sampling parameters."
},
"ToolConfig": { "ToolConfig": {
"type": "object", "type": "object",
"properties": { "properties": {
@ -15933,54 +15704,6 @@
"title": "ToolConfig", "title": "ToolConfig",
"description": "Configuration for tool use." "description": "Configuration for tool use."
}, },
"TopKSamplingStrategy": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "top_k",
"default": "top_k",
"description": "Must be \"top_k\" to identify this sampling strategy"
},
"top_k": {
"type": "integer",
"description": "Number of top tokens to consider for sampling. Must be at least 1"
}
},
"additionalProperties": false,
"required": [
"type",
"top_k"
],
"title": "TopKSamplingStrategy",
"description": "Top-k sampling strategy that restricts sampling to the k most likely tokens."
},
"TopPSamplingStrategy": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "top_p",
"default": "top_p",
"description": "Must be \"top_p\" to identify this sampling strategy"
},
"temperature": {
"type": "number",
"description": "Controls randomness in sampling. Higher values increase randomness"
},
"top_p": {
"type": "number",
"default": 0.95,
"description": "Cumulative probability threshold for nucleus sampling. Defaults to 0.95"
}
},
"additionalProperties": false,
"required": [
"type"
],
"title": "TopPSamplingStrategy",
"description": "Top-p (nucleus) sampling strategy that samples from the smallest set of tokens with cumulative probability >= p."
},
"CreateAgentRequest": { "CreateAgentRequest": {
"type": "object", "type": "object",
"properties": { "properties": {
@ -16100,8 +15823,11 @@
"default": "inference" "default": "inference"
}, },
"model_response": { "model_response": {
"$ref": "#/components/schemas/CompletionMessage", "$ref": "#/components/schemas/OpenAIAssistantMessageParam",
"description": "The response from the LLM." "description": "The response from the LLM."
},
"finish_reason": {
"type": "string"
} }
}, },
"additionalProperties": false, "additionalProperties": false,
@ -16153,7 +15879,17 @@
"description": "The IDs of the vector databases to retrieve context from." "description": "The IDs of the vector databases to retrieve context from."
}, },
"inserted_context": { "inserted_context": {
"$ref": "#/components/schemas/InterleavedContent", "oneOf": [
{
"type": "string"
},
{
"type": "array",
"items": {
"$ref": "#/components/schemas/OpenAIChatCompletionContentPartParam"
}
}
],
"description": "The context retrieved from the vector databases." "description": "The context retrieved from the vector databases."
} }
}, },
@ -16287,14 +16023,14 @@
"tool_calls": { "tool_calls": {
"type": "array", "type": "array",
"items": { "items": {
"$ref": "#/components/schemas/ToolCall" "$ref": "#/components/schemas/OpenAIChatCompletionToolCall"
}, },
"description": "The tool calls to execute." "description": "The tool calls to execute."
}, },
"tool_responses": { "tool_responses": {
"type": "array", "type": "array",
"items": { "items": {
"$ref": "#/components/schemas/ToolResponse" "$ref": "#/components/schemas/OpenAIToolMessageParam"
}, },
"description": "The tool responses from the tool calls." "description": "The tool responses from the tool calls."
} }
@ -16310,71 +16046,6 @@
"title": "ToolExecutionStep", "title": "ToolExecutionStep",
"description": "A tool execution step in an agent turn." "description": "A tool execution step in an agent turn."
}, },
"ToolResponse": {
"type": "object",
"properties": {
"call_id": {
"type": "string",
"description": "Unique identifier for the tool call this response is for"
},
"tool_name": {
"oneOf": [
{
"type": "string",
"enum": [
"brave_search",
"wolfram_alpha",
"photogen",
"code_interpreter"
],
"title": "BuiltinTool"
},
{
"type": "string"
}
],
"description": "Name of the tool that was invoked"
},
"content": {
"$ref": "#/components/schemas/InterleavedContent",
"description": "The response content from the tool"
},
"metadata": {
"type": "object",
"additionalProperties": {
"oneOf": [
{
"type": "null"
},
{
"type": "boolean"
},
{
"type": "number"
},
{
"type": "string"
},
{
"type": "array"
},
{
"type": "object"
}
]
},
"description": "(Optional) Additional metadata about the tool response"
}
},
"additionalProperties": false,
"required": [
"call_id",
"tool_name",
"content"
],
"title": "ToolResponse",
"description": "Response from a tool invocation."
},
"Turn": { "Turn": {
"type": "object", "type": "object",
"properties": { "properties": {
@ -16389,14 +16060,7 @@
"input_messages": { "input_messages": {
"type": "array", "type": "array",
"items": { "items": {
"oneOf": [ "$ref": "#/components/schemas/OpenAIMessageParam"
{
"$ref": "#/components/schemas/UserMessage"
},
{
"$ref": "#/components/schemas/ToolResponseMessage"
}
]
}, },
"description": "List of messages that initiated this turn" "description": "List of messages that initiated this turn"
}, },
@ -16430,9 +16094,12 @@
"description": "Ordered list of processing steps executed during this turn" "description": "Ordered list of processing steps executed during this turn"
}, },
"output_message": { "output_message": {
"$ref": "#/components/schemas/CompletionMessage", "$ref": "#/components/schemas/OpenAIAssistantMessageParam",
"description": "The model's generated response containing content and metadata" "description": "The model's generated response containing content and metadata"
}, },
"finish_reason": {
"type": "string"
},
"output_attachments": { "output_attachments": {
"type": "array", "type": "array",
"items": { "items": {
@ -16443,13 +16110,10 @@
{ {
"type": "string" "type": "string"
}, },
{
"$ref": "#/components/schemas/InterleavedContentItem"
},
{ {
"type": "array", "type": "array",
"items": { "items": {
"$ref": "#/components/schemas/InterleavedContentItem" "$ref": "#/components/schemas/OpenAIChatCompletionContentPartParam"
} }
}, },
{ {
@ -16502,14 +16166,7 @@
"messages": { "messages": {
"type": "array", "type": "array",
"items": { "items": {
"oneOf": [ "$ref": "#/components/schemas/OpenAIMessageParam"
{
"$ref": "#/components/schemas/UserMessage"
},
{
"$ref": "#/components/schemas/ToolResponseMessage"
}
]
}, },
"description": "List of messages to start the turn with." "description": "List of messages to start the turn with."
}, },
@ -16527,13 +16184,10 @@
{ {
"type": "string" "type": "string"
}, },
{
"$ref": "#/components/schemas/InterleavedContentItem"
},
{ {
"type": "array", "type": "array",
"items": { "items": {
"$ref": "#/components/schemas/InterleavedContentItem" "$ref": "#/components/schemas/OpenAIChatCompletionContentPartParam"
} }
}, },
{ {
@ -16557,12 +16211,12 @@
}, },
"description": "(Optional) List of documents to create the turn with." "description": "(Optional) List of documents to create the turn with."
}, },
"toolgroups": { "tools": {
"type": "array", "type": "array",
"items": { "items": {
"$ref": "#/components/schemas/AgentTool" "$ref": "#/components/schemas/OpenAIResponseInputTool"
}, },
"description": "(Optional) List of toolgroups to create the turn with, will be used in addition to the agent's config toolgroups for the request." "description": "(Optional) List of tools to create the turn with, will be used in addition to the agent's config tools for the request."
}, },
"tool_config": { "tool_config": {
"$ref": "#/components/schemas/ToolConfig", "$ref": "#/components/schemas/ToolConfig",
@ -17015,7 +16669,7 @@
"tool_responses": { "tool_responses": {
"type": "array", "type": "array",
"items": { "items": {
"$ref": "#/components/schemas/ToolResponse" "$ref": "#/components/schemas/OpenAIToolMessageParam"
}, },
"description": "The tool call responses to resume the turn with." "description": "The tool call responses to resume the turn with."
}, },
@ -17285,6 +16939,23 @@
"title": "BenchmarkConfig", "title": "BenchmarkConfig",
"description": "A benchmark configuration for evaluation." "description": "A benchmark configuration for evaluation."
}, },
"GreedySamplingStrategy": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "greedy",
"default": "greedy",
"description": "Must be \"greedy\" to identify this sampling strategy"
}
},
"additionalProperties": false,
"required": [
"type"
],
"title": "GreedySamplingStrategy",
"description": "Greedy sampling strategy that selects the highest probability token at each step."
},
"ModelCandidate": { "ModelCandidate": {
"type": "object", "type": "object",
"properties": { "properties": {
@ -17315,6 +16986,104 @@
"title": "ModelCandidate", "title": "ModelCandidate",
"description": "A model candidate for evaluation." "description": "A model candidate for evaluation."
}, },
"SamplingParams": {
"type": "object",
"properties": {
"strategy": {
"oneOf": [
{
"$ref": "#/components/schemas/GreedySamplingStrategy"
},
{
"$ref": "#/components/schemas/TopPSamplingStrategy"
},
{
"$ref": "#/components/schemas/TopKSamplingStrategy"
}
],
"discriminator": {
"propertyName": "type",
"mapping": {
"greedy": "#/components/schemas/GreedySamplingStrategy",
"top_p": "#/components/schemas/TopPSamplingStrategy",
"top_k": "#/components/schemas/TopKSamplingStrategy"
}
},
"description": "The sampling strategy."
},
"max_tokens": {
"type": "integer",
"default": 0,
"description": "The maximum number of tokens that can be generated in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length."
},
"repetition_penalty": {
"type": "number",
"default": 1.0,
"description": "Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics."
},
"stop": {
"type": "array",
"items": {
"type": "string"
},
"description": "Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence."
}
},
"additionalProperties": false,
"required": [
"strategy"
],
"title": "SamplingParams",
"description": "Sampling parameters."
},
"TopKSamplingStrategy": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "top_k",
"default": "top_k",
"description": "Must be \"top_k\" to identify this sampling strategy"
},
"top_k": {
"type": "integer",
"description": "Number of top tokens to consider for sampling. Must be at least 1"
}
},
"additionalProperties": false,
"required": [
"type",
"top_k"
],
"title": "TopKSamplingStrategy",
"description": "Top-k sampling strategy that restricts sampling to the k most likely tokens."
},
"TopPSamplingStrategy": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "top_p",
"default": "top_p",
"description": "Must be \"top_p\" to identify this sampling strategy"
},
"temperature": {
"type": "number",
"description": "Controls randomness in sampling. Higher values increase randomness"
},
"top_p": {
"type": "number",
"default": 0.95,
"description": "Cumulative probability threshold for nucleus sampling. Defaults to 0.95"
}
},
"additionalProperties": false,
"required": [
"type"
],
"title": "TopPSamplingStrategy",
"description": "Top-p (nucleus) sampling strategy that samples from the smallest set of tokens with cumulative probability >= p."
},
"EvaluateRowsRequest": { "EvaluateRowsRequest": {
"type": "object", "type": "object",
"properties": { "properties": {

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@ -11663,8 +11663,16 @@ components:
AgentConfig: AgentConfig:
type: object type: object
properties: properties:
sampling_params: max_output_tokens:
$ref: '#/components/schemas/SamplingParams' type: integer
temperature:
type: number
top_p:
type: number
stop:
type: array
items:
type: string
input_shields: input_shields:
type: array type: array
items: items:
@ -11673,36 +11681,16 @@ components:
type: array type: array
items: items:
type: string type: string
toolgroups: tools:
type: array type: array
items: items:
$ref: '#/components/schemas/AgentTool' $ref: '#/components/schemas/OpenAIResponseInputTool'
client_tools: client_tools:
type: array type: array
items: items:
$ref: '#/components/schemas/ToolDef' oneOf:
tool_choice: - $ref: '#/components/schemas/OpenAIResponseInputTool'
type: string - $ref: '#/components/schemas/ToolDef'
enum:
- auto
- required
- none
title: ToolChoice
description: >-
Whether tool use is required or automatic. This is a hint to the model
which may not be followed. It depends on the Instruction Following capabilities
of the model.
deprecated: true
tool_prompt_format:
type: string
enum:
- json
- function_tag
- python_list
title: ToolPromptFormat
description: >-
Prompt format for calling custom / zero shot tools.
deprecated: true
tool_config: tool_config:
$ref: '#/components/schemas/ToolConfig' $ref: '#/components/schemas/ToolConfig'
max_infer_iters: max_infer_iters:
@ -11725,7 +11713,7 @@ components:
description: >- description: >-
Optional flag indicating whether session data has to be persisted Optional flag indicating whether session data has to be persisted
response_format: response_format:
$ref: '#/components/schemas/ResponseFormat' $ref: '#/components/schemas/OpenAIResponseFormatParam'
description: Optional response format configuration description: Optional response format configuration
additionalProperties: false additionalProperties: false
required: required:
@ -11733,157 +11721,6 @@ components:
- instructions - instructions
title: AgentConfig title: AgentConfig
description: Configuration for an agent. description: Configuration for an agent.
AgentTool:
oneOf:
- type: string
- type: object
properties:
name:
type: string
args:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
additionalProperties: false
required:
- name
- args
title: AgentToolGroupWithArgs
GrammarResponseFormat:
type: object
properties:
type:
type: string
enum:
- json_schema
- grammar
description: >-
Must be "grammar" to identify this format type
const: grammar
default: grammar
bnf:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: >-
The BNF grammar specification the response should conform to
additionalProperties: false
required:
- type
- bnf
title: GrammarResponseFormat
description: >-
Configuration for grammar-guided response generation.
GreedySamplingStrategy:
type: object
properties:
type:
type: string
const: greedy
default: greedy
description: >-
Must be "greedy" to identify this sampling strategy
additionalProperties: false
required:
- type
title: GreedySamplingStrategy
description: >-
Greedy sampling strategy that selects the highest probability token at each
step.
JsonSchemaResponseFormat:
type: object
properties:
type:
type: string
enum:
- json_schema
- grammar
description: >-
Must be "json_schema" to identify this format type
const: json_schema
default: json_schema
json_schema:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: >-
The JSON schema the response should conform to. In a Python SDK, this
is often a `pydantic` model.
additionalProperties: false
required:
- type
- json_schema
title: JsonSchemaResponseFormat
description: >-
Configuration for JSON schema-guided response generation.
ResponseFormat:
oneOf:
- $ref: '#/components/schemas/JsonSchemaResponseFormat'
- $ref: '#/components/schemas/GrammarResponseFormat'
discriminator:
propertyName: type
mapping:
json_schema: '#/components/schemas/JsonSchemaResponseFormat'
grammar: '#/components/schemas/GrammarResponseFormat'
SamplingParams:
type: object
properties:
strategy:
oneOf:
- $ref: '#/components/schemas/GreedySamplingStrategy'
- $ref: '#/components/schemas/TopPSamplingStrategy'
- $ref: '#/components/schemas/TopKSamplingStrategy'
discriminator:
propertyName: type
mapping:
greedy: '#/components/schemas/GreedySamplingStrategy'
top_p: '#/components/schemas/TopPSamplingStrategy'
top_k: '#/components/schemas/TopKSamplingStrategy'
description: The sampling strategy.
max_tokens:
type: integer
default: 0
description: >-
The maximum number of tokens that can be generated in the completion.
The token count of your prompt plus max_tokens cannot exceed the model's
context length.
repetition_penalty:
type: number
default: 1.0
description: >-
Number between -2.0 and 2.0. Positive values penalize new tokens based
on whether they appear in the text so far, increasing the model's likelihood
to talk about new topics.
stop:
type: array
items:
type: string
description: >-
Up to 4 sequences where the API will stop generating further tokens. The
returned text will not contain the stop sequence.
additionalProperties: false
required:
- strategy
title: SamplingParams
description: Sampling parameters.
ToolConfig: ToolConfig:
type: object type: object
properties: properties:
@ -11932,51 +11769,6 @@ components:
additionalProperties: false additionalProperties: false
title: ToolConfig title: ToolConfig
description: Configuration for tool use. description: Configuration for tool use.
TopKSamplingStrategy:
type: object
properties:
type:
type: string
const: top_k
default: top_k
description: >-
Must be "top_k" to identify this sampling strategy
top_k:
type: integer
description: >-
Number of top tokens to consider for sampling. Must be at least 1
additionalProperties: false
required:
- type
- top_k
title: TopKSamplingStrategy
description: >-
Top-k sampling strategy that restricts sampling to the k most likely tokens.
TopPSamplingStrategy:
type: object
properties:
type:
type: string
const: top_p
default: top_p
description: >-
Must be "top_p" to identify this sampling strategy
temperature:
type: number
description: >-
Controls randomness in sampling. Higher values increase randomness
top_p:
type: number
default: 0.95
description: >-
Cumulative probability threshold for nucleus sampling. Defaults to 0.95
additionalProperties: false
required:
- type
title: TopPSamplingStrategy
description: >-
Top-p (nucleus) sampling strategy that samples from the smallest set of tokens
with cumulative probability >= p.
CreateAgentRequest: CreateAgentRequest:
type: object type: object
properties: properties:
@ -12072,8 +11864,10 @@ components:
const: inference const: inference
default: inference default: inference
model_response: model_response:
$ref: '#/components/schemas/CompletionMessage' $ref: '#/components/schemas/OpenAIAssistantMessageParam'
description: The response from the LLM. description: The response from the LLM.
finish_reason:
type: string
additionalProperties: false additionalProperties: false
required: required:
- turn_id - turn_id
@ -12115,7 +11909,11 @@ components:
description: >- description: >-
The IDs of the vector databases to retrieve context from. The IDs of the vector databases to retrieve context from.
inserted_context: inserted_context:
$ref: '#/components/schemas/InterleavedContent' oneOf:
- type: string
- type: array
items:
$ref: '#/components/schemas/OpenAIChatCompletionContentPartParam'
description: >- description: >-
The context retrieved from the vector databases. The context retrieved from the vector databases.
additionalProperties: false additionalProperties: false
@ -12226,12 +12024,12 @@ components:
tool_calls: tool_calls:
type: array type: array
items: items:
$ref: '#/components/schemas/ToolCall' $ref: '#/components/schemas/OpenAIChatCompletionToolCall'
description: The tool calls to execute. description: The tool calls to execute.
tool_responses: tool_responses:
type: array type: array
items: items:
$ref: '#/components/schemas/ToolResponse' $ref: '#/components/schemas/OpenAIToolMessageParam'
description: The tool responses from the tool calls. description: The tool responses from the tool calls.
additionalProperties: false additionalProperties: false
required: required:
@ -12242,46 +12040,6 @@ components:
- tool_responses - tool_responses
title: ToolExecutionStep title: ToolExecutionStep
description: A tool execution step in an agent turn. description: A tool execution step in an agent turn.
ToolResponse:
type: object
properties:
call_id:
type: string
description: >-
Unique identifier for the tool call this response is for
tool_name:
oneOf:
- type: string
enum:
- brave_search
- wolfram_alpha
- photogen
- code_interpreter
title: BuiltinTool
- type: string
description: Name of the tool that was invoked
content:
$ref: '#/components/schemas/InterleavedContent'
description: The response content from the tool
metadata:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: >-
(Optional) Additional metadata about the tool response
additionalProperties: false
required:
- call_id
- tool_name
- content
title: ToolResponse
description: Response from a tool invocation.
Turn: Turn:
type: object type: object
properties: properties:
@ -12296,9 +12054,7 @@ components:
input_messages: input_messages:
type: array type: array
items: items:
oneOf: $ref: '#/components/schemas/OpenAIMessageParam'
- $ref: '#/components/schemas/UserMessage'
- $ref: '#/components/schemas/ToolResponseMessage'
description: >- description: >-
List of messages that initiated this turn List of messages that initiated this turn
steps: steps:
@ -12319,9 +12075,11 @@ components:
description: >- description: >-
Ordered list of processing steps executed during this turn Ordered list of processing steps executed during this turn
output_message: output_message:
$ref: '#/components/schemas/CompletionMessage' $ref: '#/components/schemas/OpenAIAssistantMessageParam'
description: >- description: >-
The model's generated response containing content and metadata The model's generated response containing content and metadata
finish_reason:
type: string
output_attachments: output_attachments:
type: array type: array
items: items:
@ -12330,10 +12088,9 @@ components:
content: content:
oneOf: oneOf:
- type: string - type: string
- $ref: '#/components/schemas/InterleavedContentItem'
- type: array - type: array
items: items:
$ref: '#/components/schemas/InterleavedContentItem' $ref: '#/components/schemas/OpenAIChatCompletionContentPartParam'
- $ref: '#/components/schemas/URL' - $ref: '#/components/schemas/URL'
description: The content of the attachment. description: The content of the attachment.
mime_type: mime_type:
@ -12373,9 +12130,7 @@ components:
messages: messages:
type: array type: array
items: items:
oneOf: $ref: '#/components/schemas/OpenAIMessageParam'
- $ref: '#/components/schemas/UserMessage'
- $ref: '#/components/schemas/ToolResponseMessage'
description: List of messages to start the turn with. description: List of messages to start the turn with.
stream: stream:
type: boolean type: boolean
@ -12390,10 +12145,9 @@ components:
content: content:
oneOf: oneOf:
- type: string - type: string
- $ref: '#/components/schemas/InterleavedContentItem'
- type: array - type: array
items: items:
$ref: '#/components/schemas/InterleavedContentItem' $ref: '#/components/schemas/OpenAIChatCompletionContentPartParam'
- $ref: '#/components/schemas/URL' - $ref: '#/components/schemas/URL'
description: The content of the document. description: The content of the document.
mime_type: mime_type:
@ -12407,13 +12161,13 @@ components:
description: A document to be used by an agent. description: A document to be used by an agent.
description: >- description: >-
(Optional) List of documents to create the turn with. (Optional) List of documents to create the turn with.
toolgroups: tools:
type: array type: array
items: items:
$ref: '#/components/schemas/AgentTool' $ref: '#/components/schemas/OpenAIResponseInputTool'
description: >- description: >-
(Optional) List of toolgroups to create the turn with, will be used in (Optional) List of tools to create the turn with, will be used in addition
addition to the agent's config toolgroups for the request. to the agent's config tools for the request.
tool_config: tool_config:
$ref: '#/components/schemas/ToolConfig' $ref: '#/components/schemas/ToolConfig'
description: >- description: >-
@ -12764,7 +12518,7 @@ components:
tool_responses: tool_responses:
type: array type: array
items: items:
$ref: '#/components/schemas/ToolResponse' $ref: '#/components/schemas/OpenAIToolMessageParam'
description: >- description: >-
The tool call responses to resume the turn with. The tool call responses to resume the turn with.
stream: stream:
@ -12955,6 +12709,22 @@ components:
title: BenchmarkConfig title: BenchmarkConfig
description: >- description: >-
A benchmark configuration for evaluation. A benchmark configuration for evaluation.
GreedySamplingStrategy:
type: object
properties:
type:
type: string
const: greedy
default: greedy
description: >-
Must be "greedy" to identify this sampling strategy
additionalProperties: false
required:
- type
title: GreedySamplingStrategy
description: >-
Greedy sampling strategy that selects the highest probability token at each
step.
ModelCandidate: ModelCandidate:
type: object type: object
properties: properties:
@ -12980,6 +12750,92 @@ components:
- sampling_params - sampling_params
title: ModelCandidate title: ModelCandidate
description: A model candidate for evaluation. description: A model candidate for evaluation.
SamplingParams:
type: object
properties:
strategy:
oneOf:
- $ref: '#/components/schemas/GreedySamplingStrategy'
- $ref: '#/components/schemas/TopPSamplingStrategy'
- $ref: '#/components/schemas/TopKSamplingStrategy'
discriminator:
propertyName: type
mapping:
greedy: '#/components/schemas/GreedySamplingStrategy'
top_p: '#/components/schemas/TopPSamplingStrategy'
top_k: '#/components/schemas/TopKSamplingStrategy'
description: The sampling strategy.
max_tokens:
type: integer
default: 0
description: >-
The maximum number of tokens that can be generated in the completion.
The token count of your prompt plus max_tokens cannot exceed the model's
context length.
repetition_penalty:
type: number
default: 1.0
description: >-
Number between -2.0 and 2.0. Positive values penalize new tokens based
on whether they appear in the text so far, increasing the model's likelihood
to talk about new topics.
stop:
type: array
items:
type: string
description: >-
Up to 4 sequences where the API will stop generating further tokens. The
returned text will not contain the stop sequence.
additionalProperties: false
required:
- strategy
title: SamplingParams
description: Sampling parameters.
TopKSamplingStrategy:
type: object
properties:
type:
type: string
const: top_k
default: top_k
description: >-
Must be "top_k" to identify this sampling strategy
top_k:
type: integer
description: >-
Number of top tokens to consider for sampling. Must be at least 1
additionalProperties: false
required:
- type
- top_k
title: TopKSamplingStrategy
description: >-
Top-k sampling strategy that restricts sampling to the k most likely tokens.
TopPSamplingStrategy:
type: object
properties:
type:
type: string
const: top_p
default: top_p
description: >-
Must be "top_p" to identify this sampling strategy
temperature:
type: number
description: >-
Controls randomness in sampling. Higher values increase randomness
top_p:
type: number
default: 0.95
description: >-
Cumulative probability threshold for nucleus sampling. Defaults to 0.95
additionalProperties: false
required:
- type
title: TopPSamplingStrategy
description: >-
Top-p (nucleus) sampling strategy that samples from the smallest set of tokens
with cumulative probability >= p.
EvaluateRowsRequest: EvaluateRowsRequest:
type: object type: object
properties: properties:

22
llama_stack/apis/agents/agents.py
View file

@ -20,9 +20,7 @@ from llama_stack.apis.inference import (
OpenAIMessageParam, OpenAIMessageParam,
OpenAIResponseFormatParam, OpenAIResponseFormatParam,
OpenAIToolMessageParam, OpenAIToolMessageParam,
ToolChoice,
ToolConfig, ToolConfig,
ToolPromptFormat,
) )
from llama_stack.apis.safety import SafetyViolation from llama_stack.apis.safety import SafetyViolation
from llama_stack.apis.tools import ToolDef from llama_stack.apis.tools import ToolDef
@ -107,7 +105,6 @@ class StepType(StrEnum):
memory_retrieval = "memory_retrieval" memory_retrieval = "memory_retrieval"
@json_schema_type
@json_schema_type @json_schema_type
class InferenceStep(StepCommon): class InferenceStep(StepCommon):
"""An inference step in an agent turn. """An inference step in an agent turn.
@ -208,15 +205,6 @@ class Session(BaseModel):
started_at: datetime started_at: datetime
class AgentToolGroupWithArgs(BaseModel):
name: str
args: dict[str, Any]
AgentToolGroup = str | AgentToolGroupWithArgs
register_schema(AgentToolGroup, name="AgentTool")
class AgentConfigCommon(BaseModel): class AgentConfigCommon(BaseModel):
max_output_tokens: int | None = None max_output_tokens: int | None = None
temperature: float | None = None temperature: float | None = None
@ -225,7 +213,7 @@ class AgentConfigCommon(BaseModel):
input_shields: list[str] | None = Field(default_factory=list) input_shields: list[str] | None = Field(default_factory=list)
output_shields: list[str] | None = Field(default_factory=list) output_shields: list[str] | None = Field(default_factory=list)
toolgroups: list[AgentToolGroup] | None = Field(default_factory=list) tools: list[OpenAIResponseInputTool] | None = Field(default_factory=list)
client_tools: list[OpenAIResponseInputTool | ToolDef] | None = Field(default_factory=list) client_tools: list[OpenAIResponseInputTool | ToolDef] | None = Field(default_factory=list)
tool_config: ToolConfig | None = Field(default=None) tool_config: ToolConfig | None = Field(default=None)
@ -419,7 +407,7 @@ class AgentTurnCreateRequest(AgentConfigOverridablePerTurn):
:param session_id: Unique identifier for the conversation session :param session_id: Unique identifier for the conversation session
:param messages: List of messages to start the turn with :param messages: List of messages to start the turn with
:param documents: (Optional) List of documents to provide to the agent :param documents: (Optional) List of documents to provide to the agent
:param toolgroups: (Optional) List of tool groups to make available for this turn :param tools: (Optional) List of tools to make available for this turn
:param stream: (Optional) Whether to stream the response :param stream: (Optional) Whether to stream the response
:param tool_config: (Optional) Tool configuration to override agent defaults :param tool_config: (Optional) Tool configuration to override agent defaults
""" """
@ -430,7 +418,7 @@ class AgentTurnCreateRequest(AgentConfigOverridablePerTurn):
messages: list[OpenAIMessageParam] messages: list[OpenAIMessageParam]
documents: list[Document] | None = None documents: list[Document] | None = None
toolgroups: list[AgentToolGroup] | None = Field(default_factory=lambda: []) tools: list[OpenAIResponseInputTool] | None = Field(default_factory=lambda: [])
stream: bool | None = False stream: bool | None = False
tool_config: ToolConfig | None = None tool_config: ToolConfig | None = None
@ -524,7 +512,7 @@ class Agents(Protocol):
messages: list[OpenAIMessageParam], messages: list[OpenAIMessageParam],
stream: bool | None = False, stream: bool | None = False,
documents: list[Document] | None = None, documents: list[Document] | None = None,
toolgroups: list[AgentToolGroup] | None = None, tools: list[OpenAIResponseInputTool] | None = None,
tool_config: ToolConfig | None = None, tool_config: ToolConfig | None = None,
) -> Turn | AsyncIterator[AgentTurnResponseStreamChunk]: ) -> Turn | AsyncIterator[AgentTurnResponseStreamChunk]:
"""Create a new turn for an agent. """Create a new turn for an agent.
@ -534,7 +522,7 @@ class Agents(Protocol):
:param messages: List of messages to start the turn with. :param messages: List of messages to start the turn with.
:param stream: (Optional) If True, generate an SSE event stream of the response. Defaults to False. :param stream: (Optional) If True, generate an SSE event stream of the response. Defaults to False.
:param documents: (Optional) List of documents to create the turn with. :param documents: (Optional) List of documents to create the turn with.
:param toolgroups: (Optional) List of toolgroups to create the turn with, will be used in addition to the agent's config toolgroups for the request. :param tools: (Optional) List of tools to create the turn with, will be used in addition to the agent's config tools for the request.
:param tool_config: (Optional) The tool configuration to create the turn with, will be used to override the agent's tool_config. :param tool_config: (Optional) The tool configuration to create the turn with, will be used to override the agent's tool_config.
:returns: If stream=False, returns a Turn object. :returns: If stream=False, returns a Turn object.
If stream=True, returns an SSE event stream of AgentTurnResponseStreamChunk. If stream=True, returns an SSE event stream of AgentTurnResponseStreamChunk.

2
llama_stack/core/library_client.py
View file

@ -100,8 +100,6 @@ def convert_to_pydantic(annotation: Any, value: Any) -> Any:
return TypeAdapter(annotation).validate_python(value) return TypeAdapter(annotation).validate_python(value)
except Exception as e: except Exception as e:
# TODO: this is workardound for having Union[str, AgentToolGroup] in API schema.
# We should get rid of any non-discriminated unions in the API schema.
if origin is Union: if origin is Union:
for union_type in get_args(annotation): for union_type in get_args(annotation):
try: try:

222
llama_stack/providers/inline/agents/meta_reference/agent_instance.py
View file

@ -10,16 +10,13 @@ import re
import uuid import uuid
import warnings import warnings
from collections.abc import AsyncGenerator from collections.abc import AsyncGenerator
from typing import Any
from datetime import UTC, datetime from datetime import UTC, datetime
from typing import Any
import httpx import httpx
from llama_stack.apis.agents import ( from llama_stack.apis.agents import (
AgentConfig, AgentConfig,
OpenAIResponseInputTool,
AgentToolGroup,
AgentToolGroupWithArgs,
AgentTurnCreateRequest, AgentTurnCreateRequest,
AgentTurnResponseEvent, AgentTurnResponseEvent,
AgentTurnResponseEventType, AgentTurnResponseEventType,
@ -33,12 +30,19 @@ from llama_stack.apis.agents import (
Attachment, Attachment,
Document, Document,
InferenceStep, InferenceStep,
OpenAIResponseInputTool,
ShieldCallStep, ShieldCallStep,
Step, Step,
StepType, StepType,
ToolExecutionStep, ToolExecutionStep,
Turn, Turn,
) )
from llama_stack.apis.agents.openai_responses import (
OpenAIResponseInputToolFileSearch,
OpenAIResponseInputToolFunction,
OpenAIResponseInputToolMCP,
OpenAIResponseInputToolWebSearch,
)
from llama_stack.apis.common.content_types import URL, ToolCallDelta, ToolCallParseStatus from llama_stack.apis.common.content_types import URL, ToolCallDelta, ToolCallParseStatus
from llama_stack.apis.common.errors import SessionNotFoundError from llama_stack.apis.common.errors import SessionNotFoundError
from llama_stack.apis.inference import ( from llama_stack.apis.inference import (
@ -47,13 +51,12 @@ from llama_stack.apis.inference import (
Inference, Inference,
Message, Message,
OpenAIAssistantMessageParam, OpenAIAssistantMessageParam,
OpenAIDeveloperMessageParam,
OpenAIChatCompletionContentPartImageParam, OpenAIChatCompletionContentPartImageParam,
OpenAIChatCompletionContentPartTextParam, OpenAIChatCompletionContentPartTextParam,
OpenAIChatCompletionMessageContent, OpenAIChatCompletionMessageContent,
OpenAIChatCompletionToolCall, OpenAIChatCompletionToolCall,
OpenAIChatCompletionToolCallFunction, OpenAIChatCompletionToolCallFunction,
OpenAIImageURL, OpenAIDeveloperMessageParam,
OpenAIMessageParam, OpenAIMessageParam,
OpenAISystemMessageParam, OpenAISystemMessageParam,
OpenAIToolMessageParam, OpenAIToolMessageParam,
@ -123,7 +126,9 @@ def _openai_tool_call_to_legacy(tool_call: OpenAIChatCompletionToolCall) -> Tool
def _legacy_tool_call_to_openai(tool_call: ToolCall, index: int | None = None) -> OpenAIChatCompletionToolCall: def _legacy_tool_call_to_openai(tool_call: ToolCall, index: int | None = None) -> OpenAIChatCompletionToolCall:
function_name = tool_call.tool_name if not isinstance(tool_call.tool_name, BuiltinTool) else tool_call.tool_name.value function_name = (
tool_call.tool_name if not isinstance(tool_call.tool_name, BuiltinTool) else tool_call.tool_name.value
)
return OpenAIChatCompletionToolCall( return OpenAIChatCompletionToolCall(
index=index, index=index,
id=tool_call.call_id, id=tool_call.call_id,
@ -178,9 +183,9 @@ def _coerce_to_text(content: Any) -> str:
if isinstance(content, list): if isinstance(content, list):
return "\n".join(_coerce_to_text(item) for item in content) return "\n".join(_coerce_to_text(item) for item in content)
if hasattr(content, "text"): if hasattr(content, "text"):
return getattr(content, "text") return content.text
if hasattr(content, "image"): if hasattr(content, "image"):
image = getattr(content, "image") image = content.image
if hasattr(image, "url") and image.url: if hasattr(image, "url") and image.url:
return getattr(image.url, "uri", "") return getattr(image.url, "uri", "")
return str(content) return str(content)
@ -200,10 +205,7 @@ def _openai_message_param_to_legacy(message: OpenAIMessageParam) -> Message:
# Map developer messages to user role for legacy compatibility # Map developer messages to user role for legacy compatibility
return UserMessage(content=_openai_message_content_to_text(message.content)) return UserMessage(content=_openai_message_content_to_text(message.content))
if isinstance(message, OpenAIAssistantMessageParam): if isinstance(message, OpenAIAssistantMessageParam):
tool_calls = [ tool_calls = [_openai_tool_call_to_legacy(tool_call) for tool_call in message.tool_calls or []]
_openai_tool_call_to_legacy(tool_call)
for tool_call in message.tool_calls or []
]
return CompletionMessage( return CompletionMessage(
content=_openai_message_content_to_text(message.content) if message.content is not None else "", content=_openai_message_content_to_text(message.content) if message.content is not None else "",
stop_reason=StopReason.end_of_turn, stop_reason=StopReason.end_of_turn,
@ -279,6 +281,10 @@ class ChatAgent(ShieldRunnerMixin):
self.created_at = created_at self.created_at = created_at
self.telemetry_enabled = telemetry_enabled self.telemetry_enabled = telemetry_enabled
self.tool_defs: list[ToolDefinition] = []
self.tool_name_to_args: dict[str | BuiltinTool, dict[str, Any]] = {}
self.client_tools_config: list[OpenAIResponseInputTool | ToolDef] = []
ShieldRunnerMixin.__init__( ShieldRunnerMixin.__init__(
self, self,
safety_api, safety_api,
@ -367,7 +373,7 @@ class ChatAgent(ShieldRunnerMixin):
if self.agent_config.name: if self.agent_config.name:
span.set_attribute("agent_name", self.agent_config.name) span.set_attribute("agent_name", self.agent_config.name)
await self._initialize_tools(request.toolgroups) await self._initialize_tools(request.tools)
async for chunk in self._run_turn(request, turn_id): async for chunk in self._run_turn(request, turn_id):
yield chunk yield chunk
@ -682,12 +688,11 @@ class ChatAgent(ShieldRunnerMixin):
# Build a map of custom tools to their definitions for faster lookup # Build a map of custom tools to their definitions for faster lookup
client_tools: dict[str, OpenAIResponseInputTool | ToolDef] = {} client_tools: dict[str, OpenAIResponseInputTool | ToolDef] = {}
if self.agent_config.client_tools: for tool in self.client_tools_config or []:
for tool in self.agent_config.client_tools: if isinstance(tool, ToolDef) and tool.name:
if isinstance(tool, ToolDef) and tool.name: client_tools[tool.name] = tool
client_tools[tool.name] = tool elif getattr(tool, "type", None) == "function" and getattr(tool, "name", None):
elif getattr(tool, "type", None) == "function" and getattr(tool, "name", None): client_tools[tool.name] = tool
client_tools[tool.name] = tool
while True: while True:
step_id = str(uuid.uuid4()) step_id = str(uuid.uuid4())
inference_start_time = datetime.now(UTC).isoformat() inference_start_time = datetime.now(UTC).isoformat()
@ -987,91 +992,124 @@ class ChatAgent(ShieldRunnerMixin):
async def _initialize_tools( async def _initialize_tools(
self, self,
toolgroups_for_turn: list[AgentToolGroup] | None = None, tools_for_turn: list[OpenAIResponseInputTool] | None = None,
) -> None: ) -> None:
toolgroup_to_args = {}
for toolgroup in (self.agent_config.toolgroups or []) + (toolgroups_for_turn or []):
if isinstance(toolgroup, AgentToolGroupWithArgs):
tool_group_name, _ = self._parse_toolgroup_name(toolgroup.name)
toolgroup_to_args[tool_group_name] = toolgroup.args
# Determine which tools to include
tool_groups_to_include = toolgroups_for_turn or self.agent_config.toolgroups or []
agent_config_toolgroups = []
for toolgroup in tool_groups_to_include:
name = toolgroup.name if isinstance(toolgroup, AgentToolGroupWithArgs) else toolgroup
if name not in agent_config_toolgroups:
agent_config_toolgroups.append(name)
toolgroup_to_args = toolgroup_to_args or {}
tool_name_to_def: dict[str | BuiltinTool, ToolDefinition] = {} tool_name_to_def: dict[str | BuiltinTool, ToolDefinition] = {}
tool_name_to_args: dict[str | BuiltinTool, dict[str, Any]] = {} tool_name_to_args: dict[str | BuiltinTool, dict[str, Any]] = {}
client_tools_map: dict[str, OpenAIResponseInputTool | ToolDef] = {}
def add_tool_definition(identifier: str | BuiltinTool, tool_definition: ToolDefinition) -> None:
if identifier in tool_name_to_def:
raise ValueError(f"Tool {identifier} already exists")
tool_name_to_def[identifier] = tool_definition
def add_client_tool(tool: OpenAIResponseInputTool | ToolDef) -> None:
name = getattr(tool, "name", None)
if isinstance(tool, ToolDef):
name = tool.name
if not name:
raise ValueError("Client tools must have a name")
if name not in client_tools_map:
client_tools_map[name] = tool
tool_definition = _client_tool_to_tool_definition(tool)
add_tool_definition(tool_definition.tool_name, tool_definition)
if self.agent_config.client_tools: if self.agent_config.client_tools:
for tool in self.agent_config.client_tools: for tool in self.agent_config.client_tools:
tool_definition = _client_tool_to_tool_definition(tool) add_client_tool(tool)
if tool_name_to_def.get(tool_definition.tool_name):
raise ValueError(f"Tool {tool_definition.tool_name} already exists")
tool_name_to_def[tool_definition.tool_name] = tool_definition
for toolgroup_name_with_maybe_tool_name in agent_config_toolgroups:
toolgroup_name, input_tool_name = self._parse_toolgroup_name(toolgroup_name_with_maybe_tool_name)
tools = await self.tool_groups_api.list_tools(toolgroup_id=toolgroup_name)
if not tools.data:
available_tool_groups = ", ".join(
[t.identifier for t in (await self.tool_groups_api.list_tool_groups()).data]
)
raise ValueError(f"Toolgroup {toolgroup_name} not found, available toolgroups: {available_tool_groups}")
if input_tool_name is not None and not any(tool.name == input_tool_name for tool in tools.data):
raise ValueError(
f"Tool {input_tool_name} not found in toolgroup {toolgroup_name}. Available tools: {', '.join([tool.name for tool in tools.data])}"
)
for tool_def in tools.data: effective_tools = tools_for_turn
if toolgroup_name.startswith("builtin") and toolgroup_name != RAG_TOOL_GROUP: if effective_tools is None:
identifier: str | BuiltinTool | None = tool_def.name effective_tools = self.agent_config.tools
if identifier == "web_search":
identifier = BuiltinTool.brave_search
else:
identifier = BuiltinTool(identifier)
else:
# add if tool_name is unspecified or the tool_def identifier is the same as the tool_name
if input_tool_name in (None, tool_def.name):
identifier = tool_def.name
else:
identifier = None
if tool_name_to_def.get(identifier, None): for tool in effective_tools or []:
raise ValueError(f"Tool {identifier} already exists") if isinstance(tool, OpenAIResponseInputToolFunction):
if identifier: add_client_tool(tool)
tool_name_to_def[identifier] = ToolDefinition( continue
resolved_tools = await self._resolve_response_tool(tool)
for identifier, definition, args in resolved_tools:
add_tool_definition(identifier, definition)
if args:
existing_args = tool_name_to_args.get(identifier, {})
tool_name_to_args[identifier] = {**existing_args, **args}
self.tool_defs = list(tool_name_to_def.values())
self.tool_name_to_args = tool_name_to_args
self.client_tools_config = list(client_tools_map.values())
async def _resolve_response_tool(
self,
tool: OpenAIResponseInputTool,
) -> list[tuple[str | BuiltinTool, ToolDefinition, dict[str, Any]]]:
if isinstance(tool, OpenAIResponseInputToolWebSearch):
tool_def = await self.tool_groups_api.get_tool(WEB_SEARCH_TOOL)
if tool_def is None:
raise ValueError("web_search tool is not registered")
identifier: str | BuiltinTool = BuiltinTool.brave_search
return [
(
identifier,
ToolDefinition(
tool_name=identifier, tool_name=identifier,
description=tool_def.description, description=tool_def.description,
input_schema=tool_def.input_schema, input_schema=tool_def.input_schema,
),
{},
)
]
if isinstance(tool, OpenAIResponseInputToolFileSearch):
tool_def = await self.tool_groups_api.get_tool(MEMORY_QUERY_TOOL)
if tool_def is None:
raise ValueError("knowledge_search tool is not registered")
args: dict[str, Any] = {
"vector_db_ids": tool.vector_store_ids,
}
if tool.filters is not None:
args["filters"] = tool.filters
if tool.max_num_results is not None:
args["max_num_results"] = tool.max_num_results
if tool.ranking_options is not None:
args["ranking_options"] = tool.ranking_options.model_dump()
return [
(
tool_def.name,
ToolDefinition(
tool_name=tool_def.name,
description=tool_def.description,
input_schema=tool_def.input_schema,
),
args,
)
]
if isinstance(tool, OpenAIResponseInputToolMCP):
toolgroup_id = tool.server_label
if not toolgroup_id.startswith("mcp::"):
toolgroup_id = f"mcp::{toolgroup_id}"
tools = await self.tool_groups_api.list_tools(toolgroup_id=toolgroup_id)
if not tools.data:
raise ValueError(
f"No tools registered for MCP server '{tool.server_label}'. Ensure the toolgroup '{toolgroup_id}' is registered."
)
resolved: list[tuple[str | BuiltinTool, ToolDefinition, dict[str, Any]]] = []
for tool_def in tools.data:
resolved.append(
(
tool_def.name,
ToolDefinition(
tool_name=tool_def.name,
description=tool_def.description,
input_schema=tool_def.input_schema,
),
{},
) )
tool_name_to_args[identifier] = toolgroup_to_args.get(toolgroup_name, {}) )
return resolved
self.tool_defs, self.tool_name_to_args = ( raise ValueError(f"Unsupported tool type '{getattr(tool, 'type', None)}' in agent configuration")
list(tool_name_to_def.values()),
tool_name_to_args,
)
def _parse_toolgroup_name(self, toolgroup_name_with_maybe_tool_name: str) -> tuple[str, str | None]:
"""Parse a toolgroup name into its components.
Args:
toolgroup_name: The toolgroup name to parse (e.g. "builtin::rag/knowledge_search")
Returns:
A tuple of (tool_type, tool_group, tool_name)
"""
split_names = toolgroup_name_with_maybe_tool_name.split("/")
if len(split_names) == 2:
# e.g. "builtin::rag"
tool_group, tool_name = split_names
else:
tool_group, tool_name = split_names[0], None
return tool_group, tool_name
async def execute_tool_call_maybe( async def execute_tool_call_maybe(
self, self,

7
llama_stack/providers/inline/agents/meta_reference/agents.py
View file

@ -15,7 +15,6 @@ from llama_stack.apis.agents import (
Agents, Agents,
AgentSessionCreateResponse, AgentSessionCreateResponse,
AgentStepResponse, AgentStepResponse,
AgentToolGroup,
AgentTurnCreateRequest, AgentTurnCreateRequest,
AgentTurnResumeRequest, AgentTurnResumeRequest,
Document, Document,
@ -32,9 +31,9 @@ from llama_stack.apis.agents.openai_responses import OpenAIResponseText
from llama_stack.apis.common.responses import PaginatedResponse from llama_stack.apis.common.responses import PaginatedResponse
from llama_stack.apis.inference import ( from llama_stack.apis.inference import (
Inference, Inference,
ToolConfig,
OpenAIMessageParam, OpenAIMessageParam,
OpenAIToolMessageParam, OpenAIToolMessageParam,
ToolConfig,
) )
from llama_stack.apis.safety import Safety from llama_stack.apis.safety import Safety
from llama_stack.apis.tools import ToolGroups, ToolRuntime from llama_stack.apis.tools import ToolGroups, ToolRuntime
@ -156,7 +155,7 @@ class MetaReferenceAgentsImpl(Agents):
agent_id: str, agent_id: str,
session_id: str, session_id: str,
messages: list[OpenAIMessageParam], messages: list[OpenAIMessageParam],
toolgroups: list[AgentToolGroup] | None = None, tools: list[OpenAIResponseInputTool] | None = None,
documents: list[Document] | None = None, documents: list[Document] | None = None,
stream: bool | None = False, stream: bool | None = False,
tool_config: ToolConfig | None = None, tool_config: ToolConfig | None = None,
@ -166,7 +165,7 @@ class MetaReferenceAgentsImpl(Agents):
session_id=session_id, session_id=session_id,
messages=messages, messages=messages,
stream=True, stream=True,
toolgroups=toolgroups, tools=tools,
documents=documents, documents=documents,
tool_config=tool_config, tool_config=tool_config,
) )

10
tests/integration/agents/test_agents.py
View file

@ -12,12 +12,8 @@ import requests
from llama_stack_client import Agent, AgentEventLogger, Document from llama_stack_client import Agent, AgentEventLogger, Document
from llama_stack_client.types.shared_params.agent_config import AgentConfig, ToolConfig from llama_stack_client.types.shared_params.agent_config import AgentConfig, ToolConfig
from llama_stack.apis.agents.agents import ( from llama_stack.apis.agents.agents import AgentConfig as Server__AgentConfig
AgentConfig as Server__AgentConfig, from llama_stack.apis.inference import ToolChoice
)
from llama_stack.apis.agents.agents import (
ToolChoice,
)
def get_boiling_point(liquid_name: str, celcius: bool = True) -> int: def get_boiling_point(liquid_name: str, celcius: bool = True) -> int:
@ -187,7 +183,7 @@ def test_tool_config(agent_config):
temperature=1.0, temperature=1.0,
top_p=0.9, top_p=0.9,
max_output_tokens=512, max_output_tokens=512,
toolgroups=[], tools=[],
enable_session_persistence=False, enable_session_persistence=False,
) )
agent_config = AgentConfig( agent_config = AgentConfig(

8
tests/unit/providers/agent/test_meta_reference_agent.py
View file

@ -74,7 +74,13 @@ def sample_agent_config():
max_output_tokens=0, max_output_tokens=0,
input_shields=["string"], input_shields=["string"],
output_shields=["string"], output_shields=["string"],
toolgroups=["mcp::my_mcp_server"], tools=[
{
"type": "mcp",
"server_label": "my_mcp_server",
"server_url": "http://example.com/mcp",
}
],
client_tools=[ client_tools=[
{ {
"type": "function", "type": "function",