refactor!: convert runner ABCs to Protocols and consolidate into providers namespace

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: jsonbailey

packages/sdk/server-ai/src/ldai/__init__.py

@@ -27,9 +27,14 @@
     ModelConfig,
     ProviderConfig,
 )
+from ldai.providers import (
+    AgentGraphResult,
+    AgentGraphRunner,
+    AgentResult,
+    AgentRunner,
+    ToolRegistry,
+)
 from ldai.providers.types import EvalScore, JudgeResponse
-from ldai.runners import AgentGraphRunner, AgentRunner
-from ldai.runners.types import AgentGraphResult, AgentResult, ToolRegistry
 from ldai.tracker import AIGraphTracker
 
 __all__ = [

packages/sdk/server-ai/src/ldai/providers/__init__.py

@@ -1,9 +1,31 @@
+from ldai.providers.agent_graph_runner import AgentGraphRunner
+from ldai.providers.agent_runner import AgentRunner
 from ldai.providers.ai_provider import AIProvider
 from ldai.providers.model_runner import ModelRunner
 from ldai.providers.runner_factory import RunnerFactory
+from ldai.providers.types import (
+    AgentGraphResult,
+    AgentResult,
+    EvalScore,
+    JudgeResponse,
+    LDAIMetrics,
+    ModelResponse,
+    StructuredResponse,
+    ToolRegistry,
+)
 
 __all__ = [
     'AIProvider',
+    'AgentGraphResult',
+    'AgentGraphRunner',
+    'AgentResult',
+    'AgentRunner',
+    'EvalScore',
+    'JudgeResponse',
+    'LDAIMetrics',
+    'ModelResponse',
     'ModelRunner',
     'RunnerFactory',
+    'StructuredResponse',
+    'ToolRegistry',
 ]

packages/sdk/server-ai/src/ldai/providers/agent_graph_runner.py

@@ -0,0 +1,23 @@
+from typing import Any, Protocol, runtime_checkable
+
+from ldai.providers.types import AgentGraphResult
+
+
+@runtime_checkable
+class AgentGraphRunner(Protocol):
+    """
+    Runtime capability interface for multi-agent graph execution.
+
+    An AgentGraphRunner is a focused, configured object returned by
+    AIProvider.create_agent_graph(). It holds all provider wiring internally —
+    the caller just passes input.
+    """
+
+    async def run(self, input: Any) -> AgentGraphResult:
+        """
+        Run the agent graph with the given input.
+
+        :param input: The input to the agent graph (string prompt or structured input)
+        :return: AgentGraphResult containing the output, raw response, and metrics
+        """
+        ...

packages/sdk/server-ai/src/ldai/providers/agent_runner.py

@@ -0,0 +1,23 @@
+from typing import Any, Protocol, runtime_checkable
+
+from ldai.providers.types import AgentResult
+
+
+@runtime_checkable
+class AgentRunner(Protocol):
+    """
+    Runtime capability interface for single-agent execution.
+
+    An AgentRunner is a focused, configured object returned by
+    AIProvider.create_agent(). It holds all provider wiring internally —
+    the caller just passes input.
+    """
+
+    async def run(self, input: Any) -> AgentResult:
+        """
+        Run the agent with the given input.
+
+        :param input: The input to the agent (string prompt or structured input)
+        :return: AgentResult containing the output, raw response, and metrics
+        """
+        ...

packages/sdk/server-ai/src/ldai/providers/model_runner.py

@@ -1,29 +1,28 @@
-from abc import ABC, abstractmethod
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Protocol, runtime_checkable
 
 from ldai.models import LDMessage
 from ldai.providers.types import ModelResponse, StructuredResponse
 
 
-class ModelRunner(ABC):
+@runtime_checkable
+class ModelRunner(Protocol):
     """
     Runtime capability interface for model invocation.
 
     A ModelRunner is a focused, configured object returned by
-    AIConnector.create_model(). It knows exactly which model to call
+    AIProvider.create_model(). It knows exactly which model to call
     and with what parameters — the caller just passes messages.
     """
 
-    @abstractmethod
     async def invoke_model(self, messages: List[LDMessage]) -> ModelResponse:
         """
         Invoke the model with an array of messages.
 
         :param messages: Array of LDMessage objects representing the conversation
         :return: ModelResponse containing the model's response and metrics
         """
+        ...
 
-    @abstractmethod
     async def invoke_structured_model(
         self,
         messages: List[LDMessage],
@@ -36,3 +35,4 @@ async def invoke_structured_model(
         :param response_structure: Dictionary defining the JSON schema for output structure
         :return: StructuredResponse containing the structured data
         """
+        ...

packages/sdk/server-ai/src/ldai/providers/types.py

@@ -3,11 +3,15 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional
+from typing import Any, Callable, Dict, List, Optional
 
 from ldai.models import LDMessage
 from ldai.tracker import TokenUsage
 
+# Type alias for a registry of tools available to an agent.
+# Keys are tool names; values are the callable implementations.
+ToolRegistry = Dict[str, Callable]
+
 
 @dataclass
 class LDAIMetrics:
@@ -94,3 +98,23 @@ def to_dict(self) -> Dict[str, Any]:
         if self.error is not None:
             result['error'] = self.error
         return result
+
+
+@dataclass
+class AgentResult:
+    """
+    Result from a single-agent run.
+    """
+    output: str
+    raw: Any
+    metrics: LDAIMetrics
+
+
+@dataclass
+class AgentGraphResult:
+    """
+    Result from an agent graph run.
+    """
+    output: str
+    raw: Any
+    metrics: LDAIMetrics