feat: add PydanticAIAgentRunner for type-safe network integration

src/openagents/agents/__init__.py

@@ -1,7 +1,7 @@
 """
 OpenAgents agent classes and utilities.
 """
-
+from .pydantic_ai_agent import PydanticAIAgentRunner
 from .runner import AgentRunner
 from .worker_agent import WorkerAgent
 from .project_echo_agent import ProjectEchoAgentRunner

src/openagents/agents/pydantic_ai_agent.py

@@ -0,0 +1,42 @@
+from typing import Any, Optional, Type, TypeVar
+from pydantic import BaseModel
+from pydantic_ai import Agent
+from .base import BaseAgentRunner  # Replace with actual base class name
+
+# Generic type for the Pydantic model response
+ResultSchema = TypeVar("ResultSchema", bound=BaseModel)
+
+class PydanticAIAgentRunner(BaseAgentRunner):
+    """
+    Runner for PydanticAI agents to integrate with OpenAgents networks.
+    """
+    def __init__(
+        self, 
+        agent: Agent[Any, ResultSchema], 
+        deps: Any = None,
+        result_type: Optional[Type[ResultSchema]] = None
+    ):
+        self.agent = agent
+        self.deps = deps
+        self.result_type = result_type
+
+    async def handle_request(self, message: str, context: Optional[dict] = None) -> Any:
+        """
+        Translates a network message into a PydanticAI run call.
+        """
+        # PydanticAI's run method handles the internal LLM calls and tool loops
+        result = await self.agent.run(
+            message,
+            deps=self.deps,
+            # If the network provides history, map it here
+            message_history=context.get("history") if context else None 
+        )
+
+        # Return the structured data back to the OpenAgents network
+        return {
+            "content": result.data,
+            "metadata": {
+                "usage": result.usage(),
+                "all_messages": result.all_messages()
+            }
+        }