Source code for reminix.client.resources.agents

"""Auto-generated operation classes.

This file is auto-generated by the operators generator.
Do not edit this file manually.
"""

from typing import TYPE_CHECKING, Any, Dict, List, Optional

from reminix.client.types import (
    ChatRequest,
    ChatResponse,
    InvokeRequest,
    InvokeResponse
)
from reminix.client.utils.pagination import PaginatedResponse

if TYPE_CHECKING:
    from reminix.client.client import Client




class Agents:
    """Operations for agents."""



    def __init__(self, client: "Client") -> None:
        """Initialize Agents operations.

        Args:
            client: The Reminix client instance
        """
        self._client = client




    async def invoke(
        self,
        name: str,
        request: InvokeRequest
    ) -> InvokeResponse:
        """Invoke an agent.

Execute a one-shot task with an agent. This endpoint is designed for task-oriented operations
where you provide input and receive a complete output.

**Use cases:**
- Data analysis and processing
- Content generation (with streaming for long outputs)
- One-time operations that don't require conversation history
- API-like operations

**Streaming:** Set `stream: true` in the request body to receive Server-Sent Events (SSE)
stream with incremental chunks. Useful for long-running tasks or real-time progress updates.

Args:
    name: Unique, URL-safe agent name within the project
    request: InvokeRequest object

Returns:
    InvokeResponse: Agent invocation successful

Example::

    request = InvokeRequest(
        input={},
        stream=False,
        context="example"
    )

    result = await client.agents.invoke(
        "example",
        request
    )
    print(result)

Raises:
        APIError: Bad Request - Invalid request body
        AuthenticationError: Unauthorized - Invalid or missing API key
        APIError: Agent or deployment not found
        APIError: Internal Server Error
        NetworkError: If a network error occurs"""
        response = await self._client.request("POST", f"/agents/{name}/invoke", json=request.model_dump(mode='json', exclude_none=True))
        return InvokeResponse(**response)





    async def chat(
        self,
        name: str,
        request: ChatRequest
    ) -> ChatResponse:
        """Chat with an agent.

Have a conversational interaction with an agent. This endpoint maintains conversation context
through the messages array, allowing for multi-turn conversations.

**Use cases:**
- Customer support chatbots
- AI assistants with memory
- Multi-step question answering
- Conversational agents that need context

**Message format:** Follows OpenAI-compliant message structure with support for:
- `system`, `user`, `assistant`, and `tool` roles
- Multimodal content (text + images)
- Tool/function calling

**Streaming:** Set `stream: true` in the request body to receive Server-Sent Events (SSE)
stream with incremental chunks. Perfect for ChatGPT-like real-time chat interfaces.

Args:
    name: Unique, URL-safe agent name within the project
    request: ChatRequest object

Returns:
    ChatResponse: Chat response successful

Example::

    request = ChatRequest(
        messages=[],
        stream=False,
        context="example"
    )

    result = await client.agents.chat(
        "example",
        request
    )
    print(result)

Raises:
        APIError: Bad Request - Invalid request body
        AuthenticationError: Unauthorized - Invalid or missing API key
        APIError: Agent or deployment not found
        APIError: Internal Server Error
        NetworkError: If a network error occurs"""
        response = await self._client.request("POST", f"/agents/{name}/chat", json=request.model_dump(mode='json', exclude_none=True))
        return ChatResponse(**response)