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Sai Soundararaj 2025-07-01 18:03:28 -07:00
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from collections.abc import AsyncIterator
from datetime import datetime
from enum import StrEnum
from typing import Annotated, Any, Literal, Protocol, runtime_checkable
from pydantic import BaseModel, ConfigDict, Field
from llama_stack.apis.common.content_types import URL, ContentDelta, InterleavedContent
from llama_stack.apis.common.responses import Order, PaginatedResponse
from llama_stack.apis.inference import (
CompletionMessage,
ResponseFormat,
SamplingParams,
ToolCall,
ToolChoice,
ToolConfig,
ToolPromptFormat,
ToolResponse,
ToolResponseMessage,
UserMessage,
)
from llama_stack.apis.safety import SafetyViolation
from llama_stack.apis.tools import ToolDef
from llama_stack.schema_utils import json_schema_type, register_schema, webmethod
from .openai_responses import (
ListOpenAIResponseInputItem,
ListOpenAIResponseObject,
OpenAIDeleteResponseObject,
OpenAIResponseInput,
OpenAIResponseInputTool,
OpenAIResponseObject,
OpenAIResponseObjectStream,
OpenAIResponseText,
)
class Attachment(BaseModel):
"""An attachment to an agent turn.
:param content: The content of the attachment.
:param mime_type: The MIME type of the attachment.
"""
content: InterleavedContent | URL = Field(description="The content of the attachment, which can be either interleaved content (text and images) or a URL pointing to the content.")
mime_type: str = Field(description="The MIME type of the attachment, specifying the format of the content (e.g., 'image/jpeg', 'text/plain').")
class Document(BaseModel):
"""A document to be used by an agent.
:param content: The content of the document.
:param mime_type: The MIME type of the document.
"""
content: InterleavedContent | URL = Field(description="The content of the document, which can be either interleaved content (text and images) or a URL pointing to the content.")
mime_type: str = Field(description="The MIME type of the document, specifying the format of the content (e.g., 'application/pdf', 'text/plain').")
class StepCommon(BaseModel):
"""A common step in an agent turn.
:param turn_id: The ID of the turn.
:param step_id: The ID of the step.
:param started_at: The time the step started.
:param completed_at: The time the step completed.
"""
turn_id: str = Field(description="Unique identifier for the turn within a session.")
step_id: str = Field(description="Unique identifier for the step within a turn.")
started_at: datetime | None = Field(default=None, description="Timestamp when the operation began.")
completed_at: datetime | None = Field(default=None, description="Timestamp when the operation finished, if completed.")
class StepType(StrEnum):
"""Type of the step in an agent turn.
:cvar inference: The step is an inference step that calls an LLM.
:cvar tool_execution: The step is a tool execution step that executes a tool call.
:cvar shield_call: The step is a shield call step that checks for safety violations.
:cvar memory_retrieval: The step is a memory retrieval step that retrieves context for vector dbs.
"""
inference = "inference"
tool_execution = "tool_execution"
shield_call = "shield_call"
memory_retrieval = "memory_retrieval"
@json_schema_type
class InferenceStep(StepCommon):
"""An inference step in an agent turn.
:param model_response: The response from the LLM.
"""
model_config = ConfigDict(protected_namespaces=())
step_type: Literal[StepType.inference] = Field(StepType.inference, description="The type of the step, which is 'inference' for this model.")
model_response: CompletionMessage = Field(description="The response message from the language model for this inference step.")
@json_schema_type
class ToolExecutionStep(StepCommon):
"""A tool execution step in an agent turn.
:param tool_calls: The tool calls to execute.
:param tool_responses: The tool responses from the tool calls.
"""
step_type: Literal[StepType.tool_execution] = Field(StepType.tool_execution, description="The type of the step, which is 'tool_execution' for this model.")
tool_calls: list[ToolCall] = Field(description="A list of tool calls that were requested by the model during this step.")
tool_responses: list[ToolResponse] = Field(description="A list of responses obtained from executing the tool calls.")
@json_schema_type
class ShieldCallStep(StepCommon):
"""A shield call step in an agent turn.
:param violation: The violation from the shield call.
"""
step_type: Literal[StepType.shield_call] = Field(StepType.shield_call, description="The type of the step, which is 'shield_call' for this model.")
violation: SafetyViolation | None = Field(description="If a safety violation was detected by the shield, this field contains details about the violation.")
@json_schema_type
class MemoryRetrievalStep(StepCommon):
"""A memory retrieval step in an agent turn.
:param vector_db_ids: The IDs of the vector databases to retrieve context from.
:param inserted_context: The context retrieved from the vector databases.
"""
step_type: Literal[StepType.memory_retrieval] = Field(StepType.memory_retrieval, description="The type of the step, which is 'memory_retrieval' for this model.")
# TODO: should this be List[str]?
vector_db_ids: str = Field(description="The ID of the vector database used for retrieving context.")
inserted_context: InterleavedContent = Field(description="The context that was retrieved from the vector database and inserted into the conversation.")
Step = Annotated[
InferenceStep | ToolExecutionStep | ShieldCallStep | MemoryRetrievalStep,
Field(discriminator="step_type"),
]
@json_schema_type
class Turn(BaseModel):
"""A single turn in an interaction with an Agentic System."""
turn_id: str = Field(description="Unique identifier for the turn within a session.")
session_id: str = Field(description="Unique identifier for the conversation session.")
input_messages: list[UserMessage | ToolResponseMessage] = Field(description="The list of input messages that initiated the turn, which can be from a user or a tool.")
steps: list[Step] = Field(description="An ordered list of processing steps (e.g., inference, tool execution) that occurred during this turn.")
output_message: CompletionMessage = Field(description="The final message generated by the agent at the end of the turn.")
output_attachments: list[Attachment] | None = Field(default_factory=lambda: [], description="A list of optional files or media attached to the agent's response.")
started_at: datetime = Field(description="Timestamp when the operation began.")
completed_at: datetime | None = Field(default=None, description="Timestamp when the operation finished, if completed.")
@json_schema_type
class Session(BaseModel):
"""A single session of an interaction with an Agentic System."""
session_id: str = Field(description="Unique identifier for the conversation session.")
session_name: str = Field(description="A user-defined name for the session for easier identification.")
turns: list[Turn] = Field(description="A list of all the turns that have occurred within this session.")
started_at: datetime = Field(description="Timestamp when the resource was created.")
class AgentToolGroupWithArgs(BaseModel):
name: str = Field(description="The name of the tool group.")
args: dict[str, Any] = Field(description="A dictionary of arguments to be passed to the tool group.")
AgentToolGroup = str | AgentToolGroupWithArgs
register_schema(AgentToolGroup, name="AgentTool")
class AgentConfigCommon(BaseModel):
sampling_params: SamplingParams | None = Field(default_factory=SamplingParams, description="Parameters to control the sampling behavior of the model during generation.")
input_shields: list[str] | None = Field(default_factory=lambda: [], description="A list of shield identifiers to be applied to the input.")
output_shields: list[str] | None = Field(default_factory=lambda: [], description="A list of shield identifiers to be applied to the output.")
toolgroups: list[AgentToolGroup] | None = Field(default_factory=lambda: [], description="A list of tool groups available to the agent.")
client_tools: list[ToolDef] | None = Field(default_factory=lambda: [], description="A list of tool definitions provided by the client.")
tool_choice: ToolChoice | None = Field(default=None, deprecated="use tool_config instead", description="(Deprecated) The method for choosing which tools to use.")
tool_prompt_format: ToolPromptFormat | None = Field(default=None, deprecated="use tool_config instead", description="(Deprecated) The format for presenting tool prompts to the model.")
tool_config: ToolConfig | None = Field(default=None, description="Configuration for tool usage, including tool choice and prompt format.")
max_infer_iters: int | None = Field(10, description="The maximum number of inference iterations allowed per turn.")
def model_post_init(self, __context):
if self.tool_config:
if self.tool_choice and self.tool_config.tool_choice != self.tool_choice:
raise ValueError("tool_choice is deprecated. Use tool_choice in tool_config instead.")
if self.tool_prompt_format and self.tool_config.tool_prompt_format != self.tool_prompt_format:
raise ValueError("tool_prompt_format is deprecated. Use tool_prompt_format in tool_config instead.")
else:
params = {}
if self.tool_choice:
params["tool_choice"] = self.tool_choice
if self.tool_prompt_format:
params["tool_prompt_format"] = self.tool_prompt_format
self.tool_config = ToolConfig(**params)
@json_schema_type
class AgentConfig(AgentConfigCommon):
"""Configuration for an agent.
:param model: The model identifier to use for the agent
:param instructions: The system instructions for the agent
:param name: Optional name for the agent, used in telemetry and identification
:param enable_session_persistence: Optional flag indicating whether session data has to be persisted
:param response_format: Optional response format configuration
"""
model: str = Field(description="The model identifier to use for the agent.")
instructions: str = Field(description="The system instructions for the agent.")
name: str | None = Field(default=None, description="An optional name for the agent, used for identification and telemetry.")
enable_session_persistence: bool | None = Field(False, description="If true, the agent's session data will be persisted across interactions.")
response_format: ResponseFormat | None = Field(default=None, description="The format for the response, such as JSON or text.")
@json_schema_type
class Agent(BaseModel):
agent_id: str = Field(description="Unique identifier for the agent.")
agent_config: AgentConfig = Field(description="The configuration settings for this agent.")
created_at: datetime = Field(description="Timestamp when the resource was created.")
class AgentConfigOverridablePerTurn(AgentConfigCommon):
instructions: str | None = Field(default=None, description="The system instructions for the agent, which can be overridden for a single turn.")
class AgentTurnResponseEventType(StrEnum):
step_start = "step_start"
step_complete = "step_complete"
step_progress = "step_progress"
turn_start = "turn_start"
turn_complete = "turn_complete"
turn_awaiting_input = "turn_awaiting_input"
@json_schema_type
class AgentTurnResponseStepStartPayload(BaseModel):
event_type: Literal[AgentTurnResponseEventType.step_start] = Field(AgentTurnResponseEventType.step_start, description="The type of the event, which is 'step_start' for this model.")
step_type: StepType = Field(description="The type of the step that is starting (e.g., 'inference', 'tool_execution').")
step_id: str = Field(description="Unique identifier for the step within a turn.")
metadata: dict[str, Any] | None = Field(default_factory=lambda: {}, description="A dictionary of metadata associated with the start of the step.")
@json_schema_type
class AgentTurnResponseStepCompletePayload(BaseModel):
event_type: Literal[AgentTurnResponseEventType.step_complete] = Field(AgentTurnResponseEventType.step_complete, description="The type of the event, which is 'step_complete' for this model.")
step_type: StepType = Field(description="The type of the step that has completed (e.g., 'inference', 'tool_execution').")
step_id: str = Field(description="Unique identifier for the step within a turn.")
step_details: Step = Field(description="Detailed information about the step that has been completed.")
@json_schema_type
class AgentTurnResponseStepProgressPayload(BaseModel):
model_config = ConfigDict(protected_namespaces=())
event_type: Literal[AgentTurnResponseEventType.step_progress] = Field(AgentTurnResponseEventType.step_progress, description="The type of the event, which is 'step_progress' for this model.")
step_type: StepType = Field(description="The type of the step that is in progress, typically 'inference'.")
step_id: str = Field(description="Unique identifier for the step within a turn.")
delta: ContentDelta = Field(description="The incremental content delta generated during the step's progress.")
@json_schema_type
class AgentTurnResponseTurnStartPayload(BaseModel):
event_type: Literal[AgentTurnResponseEventType.turn_start] = Field(AgentTurnResponseEventType.turn_start, description="The type of the event, which is 'turn_start' for this model.")
turn_id: str = Field(description="Unique identifier for the turn within a session.")
@json_schema_type
class AgentTurnResponseTurnCompletePayload(BaseModel):
event_type: Literal[AgentTurnResponseEventType.turn_complete] = Field(AgentTurnResponseEventType.turn_complete, description="The type of the event, which is 'turn_complete' for this model.")
turn: Turn = Field(description="The complete turn object, containing all steps and messages for the turn.")
@json_schema_type
class AgentTurnResponseTurnAwaitingInputPayload(BaseModel):
event_type: Literal[AgentTurnResponseEventType.turn_awaiting_input] = Field(AgentTurnResponseEventType.turn_awaiting_input, description="The type of the event, which is 'turn_awaiting_input' for this model.")
turn: Turn = Field(description="The turn object that is awaiting user or tool input to proceed.")
AgentTurnResponseEventPayload = Annotated[
AgentTurnResponseStepStartPayload
| AgentTurnResponseStepProgressPayload
| AgentTurnResponseStepCompletePayload
| AgentTurnResponseTurnStartPayload
| AgentTurnResponseTurnCompletePayload
| AgentTurnResponseTurnAwaitingInputPayload,
Field(discriminator="event_type"),
]
register_schema(AgentTurnResponseEventPayload, name="AgentTurnResponseEventPayload")
@json_schema_type
class AgentTurnResponseEvent(BaseModel):
payload: AgentTurnResponseEventPayload = Field(description="The payload of the event, containing the specific event data.")
@json_schema_type
class AgentCreateResponse(BaseModel):
agent_id: str = Field(description="Unique identifier for the newly created agent.")
@json_schema_type
class AgentSessionCreateResponse(BaseModel):
session_id: str = Field(description="Unique identifier for the newly created conversation session.")
@json_schema_type
class AgentTurnCreateRequest(AgentConfigOverridablePerTurn):
agent_id: str = Field(description="Unique identifier for the agent.")
session_id: str = Field(description="Unique identifier for the conversation session.")
# TODO: figure out how we can simplify this and make why
# ToolResponseMessage needs to be here (it is function call
# execution from outside the system)
messages: list[UserMessage | ToolResponseMessage] = Field(description="A list of messages that form the input for this turn.")
documents: list[Document] | None = Field(default=None, description="A list of documents to be used as context for this turn.")
toolgroups: list[AgentToolGroup] | None = Field(default_factory=lambda: [], description="A list of tool groups to be used for this turn, in addition to the agent's default tool groups.")
stream: bool | None = Field(default=False, description="Whether to stream the response as a series of events.")
tool_config: ToolConfig | None = Field(default=None, description="Configuration for tool usage for this turn, overriding the agent's default tool configuration.")
@json_schema_type
class AgentTurnResumeRequest(BaseModel):
agent_id: str = Field(description="Unique identifier for the agent.")
session_id: str = Field(description="Unique identifier for the conversation session.")
turn_id: str = Field(description="Unique identifier for the turn within a session that should be resumed.")
tool_responses: list[ToolResponse] = Field(description="A list of tool responses required to resume the turn.")
stream: bool | None = Field(default=False, description="Whether to stream the response as a series of events.")
@json_schema_type
class AgentTurnResponseStreamChunk(BaseModel):
"""streamed agent turn completion response."""
event: AgentTurnResponseEvent = Field(description="The event that occurred during the turn, sent as part of the stream.")
@json_schema_type
class AgentStepResponse(BaseModel):
step: Step = Field(description="The details of a specific step within a turn.")
@runtime_checkable
class Agents(Protocol):
"""Agents API for creating and interacting with agentic systems.
Main functionalities provided by this API:
- Create agents with specific instructions and ability to use tools.
- Interactions with agents are grouped into sessions ("threads"), and each interaction is called a "turn".
- Agents can be provided with various tools (see the ToolGroups and ToolRuntime APIs for more details).
- Agents can be provided with various shields (see the Safety API for more details).
- Agents can also use Memory to retrieve information from knowledge bases. See the RAG Tool and Vector IO APIs for more details.
"""
@webmethod(route="/agents", method="POST", descriptive_name="create_agent")
async def create_agent(
self,
agent_config: AgentConfig,
) -> AgentCreateResponse:
"""Create an agent with the given configuration.
:param agent_config: The configuration for the agent.
:returns: An AgentCreateResponse with the agent ID.
"""
...
@webmethod(
route="/agents/{agent_id}/session/{session_id}/turn", method="POST", descriptive_name="create_agent_turn"
)
async def create_agent_turn(
self,
agent_id: str,
session_id: str,
messages: list[UserMessage | ToolResponseMessage],
stream: bool | None = False,
documents: list[Document] | None = None,
toolgroups: list[AgentToolGroup] | None = None,
tool_config: ToolConfig | None = None,
) -> Turn | AsyncIterator[AgentTurnResponseStreamChunk]:
"""Create a new turn for an agent.
:param agent_id: The ID of the agent to create the turn for.
:param session_id: The ID of the session to create the turn for.
: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 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 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.
If stream=True, returns an SSE event stream of AgentTurnResponseStreamChunk.
"""
...
@webmethod(
route="/agents/{agent_id}/session/{session_id}/turn/{turn_id}/resume",
method="POST",
descriptive_name="resume_agent_turn",
)
async def resume_agent_turn(
self,
agent_id: str,
session_id: str,
turn_id: str,
tool_responses: list[ToolResponse],
stream: bool | None = False,
) -> Turn | AsyncIterator[AgentTurnResponseStreamChunk]:
"""Resume an agent turn with executed tool call responses.
When a Turn has the status `awaiting_input` due to pending input from client side tool calls, this endpoint can be used to submit the outputs from the tool calls once they are ready.
:param agent_id: The ID of the agent to resume.
:param session_id: The ID of the session to resume.
:param turn_id: The ID of the turn to resume.
:param tool_responses: The tool call responses to resume the turn with.
:param stream: Whether to stream the response.
:returns: A Turn object if stream is False, otherwise an AsyncIterator of AgentTurnResponseStreamChunk objects.
"""
...
@webmethod(
route="/agents/{agent_id}/session/{session_id}/turn/{turn_id}",
method="GET",
)
async def get_agents_turn(
self,
agent_id: str,
session_id: str,
turn_id: str,
) -> Turn:
"""Retrieve an agent turn by its ID.
:param agent_id: The ID of the agent to get the turn for.
:param session_id: The ID of the session to get the turn for.
:param turn_id: The ID of the turn to get.
:returns: A Turn.
"""
...
@webmethod(
route="/agents/{agent_id}/session/{session_id}/turn/{turn_id}/step/{step_id}",
method="GET",
)
async def get_agents_step(
self,
agent_id: str,
session_id: str,
turn_id: str,
step_id: str,
) -> AgentStepResponse:
"""Retrieve an agent step by its ID.
:param agent_id: The ID of the agent to get the step for.
:param session_id: The ID of the session to get the step for.
:param turn_id: The ID of the turn to get the step for.
:param step_id: The ID of the step to get.
:returns: An AgentStepResponse.
"""
...
@webmethod(route="/agents/{agent_id}/session", method="POST", descriptive_name="create_agent_session")
async def create_agent_session(
self,
agent_id: str,
session_name: str,
) -> AgentSessionCreateResponse:
"""Create a new session for an agent.
:param agent_id: The ID of the agent to create the session for.
:param session_name: The name of the session to create.
:returns: An AgentSessionCreateResponse.
"""
...
@webmethod(route="/agents/{agent_id}/session/{session_id}", method="GET")
async def get_agents_session(
self,
session_id: str,
agent_id: str,
turn_ids: list[str] | None = None,
) -> Session:
"""Retrieve an agent session by its ID.
:param session_id: The ID of the session to get.
:param agent_id: The ID of the agent to get the session for.
:param turn_ids: (Optional) List of turn IDs to filter the session by.
:returns: A Session.
"""
...
@webmethod(route="/agents/{agent_id}/session/{session_id}", method="DELETE")
async def delete_agents_session(
self,
session_id: str,
agent_id: str,
) -> None:
"""Delete an agent session by its ID and its associated turns.
:param session_id: The ID of the session to delete.
:param agent_id: The ID of the agent to delete the session for.
"""
...
@webmethod(route="/agents/{agent_id}", method="DELETE")
async def delete_agent(
self,
agent_id: str,
) -> None:
"""Delete an agent by its ID and its associated sessions and turns.
:param agent_id: The ID of the agent to delete.
"""
...
@webmethod(route="/agents", method="GET")
async def list_agents(self, start_index: int | None = None, limit: int | None = None) -> PaginatedResponse:
"""List all agents.
:param start_index: The index to start the pagination from.
:param limit: The number of agents to return.
:returns: A PaginatedResponse.
"""
...
@webmethod(route="/agents/{agent_id}", method="GET")
async def get_agent(self, agent_id: str) -> Agent:
"""Describe an agent by its ID.
:param agent_id: ID of the agent.
:returns: An Agent of the agent.
"""
...
@webmethod(route="/agents/{agent_id}/sessions", method="GET")
async def list_agent_sessions(
self,
agent_id: str,
start_index: int | None = None,
limit: int | None = None,
) -> PaginatedResponse:
"""List all session(s) of a given agent.
:param agent_id: The ID of the agent to list sessions for.
:param start_index: The index to start the pagination from.
:param limit: The number of sessions to return.
:returns: A PaginatedResponse.
"""
...
# We situate the OpenAI Responses API in the Agents API just like we did things
# for Inference. The Responses API, in its intent, serves the same purpose as
# the Agents API above -- it is essentially a lightweight "agentic loop" with
# integrated tool calling.
#
# Both of these APIs are inherently stateful.
@webmethod(route="/openai/v1/responses/{response_id}", method="GET")
async def get_openai_response(
self,
response_id: str,
) -> OpenAIResponseObject:
"""Retrieve an OpenAI response by its ID.
:param response_id: The ID of the OpenAI response to retrieve.
:returns: An OpenAIResponseObject.
"""
...
@webmethod(route="/openai/v1/responses", method="POST")
async def create_openai_response(
self,
input: str | list[OpenAIResponseInput],
model: str,
instructions: str | None = None,
previous_response_id: str | None = None,
store: bool | None = True,
stream: bool | None = False,
temperature: float | None = None,
text: OpenAIResponseText | None = None,
tools: list[OpenAIResponseInputTool] | None = None,
max_infer_iters: int | None = 10, # this is an extension to the OpenAI API
) -> OpenAIResponseObject | AsyncIterator[OpenAIResponseObjectStream]:
"""Create a new OpenAI response.
:param input: Input message(s) to create the response.
:param model: The underlying LLM used for completions.
:param previous_response_id: (Optional) if specified, the new response will be a continuation of the previous response. This can be used to easily fork-off new responses from existing responses.
:returns: An OpenAIResponseObject.
"""
...
@webmethod(route="/openai/v1/responses", method="GET")
async def list_openai_responses(
self,
after: str | None = None,
limit: int | None = 50,
model: str | None = None,
order: Order | None = Order.desc,
) -> ListOpenAIResponseObject:
"""List all OpenAI responses.
:param after: The ID of the last response to return.
:param limit: The number of responses to return.
:param model: The model to filter responses by.
:param order: The order to sort responses by when sorted by created_at ('asc' or 'desc').
:returns: A ListOpenAIResponseObject.
"""
...
@webmethod(route="/openai/v1/responses/{response_id}/input_items", method="GET")
async def list_openai_response_input_items(
self,
response_id: str,
after: str | None = None,
before: str | None = None,
include: list[str] | None = None,
limit: int | None = 20,
order: Order | None = Order.desc,
) -> ListOpenAIResponseInputItem:
"""List input items for a given OpenAI response.
:param response_id: The ID of the response to retrieve input items for.
:param after: An item ID to list items after, used for pagination.
:param before: An item ID to list items before, used for pagination.
:param include: Additional fields to include in the response.
:param limit: A limit on the number of objects to be returned. Limit can range between 1 and 100, and the default is 20.
:param order: The order to return the input items in. Default is desc.
:returns: An ListOpenAIResponseInputItem.
"""
...
@webmethod(route="/openai/v1/responses/{response_id}", method="DELETE")
async def delete_openai_response(self, response_id: str) -> OpenAIDeleteResponseObject:
"""Delete an OpenAI response by its ID.
:param response_id: The ID of the OpenAI response to delete.
:returns: An OpenAIDeleteResponseObject
"""
...