chore: import upstream snapshot with attribution

autoagent/types.py

@@ -0,0 +1,41 @@
+from litellm.types.utils import ChatCompletionMessageToolCall, Function, Message
+from typing import List, Callable, Union, Optional, Tuple, Dict
+
+# Third-party imports
+from pydantic import BaseModel
+
+AgentFunction = Callable[[], Union[str, "Agent", dict]]
+
+
+class Agent(BaseModel):
+    name: str = "Agent"
+    model: str = "gpt-4o"
+    instructions: Union[str, Callable[[], str]] = "You are a helpful agent."
+    functions: List[AgentFunction] = []
+    tool_choice: str = None
+    parallel_tool_calls: bool = False
+    examples: Union[List[Tuple[dict, str]], Callable[[], str]] = []
+    handle_mm_func: Callable[[], str] = None
+    agent_teams: Dict[str, Callable] = {}
+
+
+class Response(BaseModel):
+    messages: List = []
+    agent: Optional[Agent] = None
+    context_variables: dict = {}
+
+
+class Result(BaseModel):
+    """
+    Encapsulates the possible return values for an agent function.
+
+    Attributes:
+        value (str): The result value as a string.
+        agent (Agent): The agent instance, if applicable.
+        context_variables (dict): A dictionary of context variables.
+    """
+
+    value: str = ""
+    agent: Optional[Agent] = None
+    context_variables: dict = {}
+    image: Optional[str] = None # base64 encoded image