fix: remove Steward analysis from user-visible reasoning

The Steward's internal routing analysis (DELEGATE, COMPLEXITY, etc.)
was being exposed in <think> blocks. This is implementation detail,
not useful reasoning for the user.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.gitea/workflows/build.yml

@@ -1,8 +1,9 @@
 name: Build and Push
 
 on:
-  release:
-    types: [published]
+  push:
+    tags:
+      - 'v[0-9]*'
 
 jobs:
   release:

CHANGELOG.md

@@ -7,6 +7,25 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [2.0.3] - 2026-02-05
+
+### Fixed
+
+- **Steward analysis leaking into responses** - Removed internal routing analysis (`DELEGATE: tatlock_core...`) from user-visible reasoning in both streaming and non-streaming paths
+
+## [2.0.2] - 2026-02-05
+
+### Fixed
+
+- **tool_choice format incompatibility** - Removed `extra_body` tool_choice hack for Claude backend; PydanticAI handles tool_choice natively for Anthropic, preventing infinite tool call loops
+- **CI trigger** - Changed workflow trigger from `release:published` to `push:tags:v[0-9]*`
+
+## [2.0.1] - 2026-02-05
+
+### Fixed
+
+- **Expert agent registration failure** - `AnthropicModel` does not accept `api_key` directly; now passes it via `AnthropicProvider`
+
 ## [2.0.0] - 2026-02-05
 
 ### Added

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "tatlock"
-version = "2.0.0"
+version = "2.0.3"
 description = "OpenAI-compatible API with Ollama backend"
 requires-python = ">=3.12"
 dependencies = []

src/agents/tatlock.py

@@ -494,13 +494,13 @@ class TatlockAgent(AgentInterface):
                 )
 
         # Run with scoped tools and tracker
-        # Force tool_choice: required to make LLM actually call tools
-        from pydantic_ai.settings import ModelSettings
+        # Force tool_choice to make LLM actually call tools
+        from src.anthropic.model_selector import get_tool_choice_settings
         result = await scoped_agent.run(
             enriched_message,
             message_history=pydantic_history if pydantic_history else None,
             deps=tool_tracker,
-            model_settings=ModelSettings(extra_body={"tool_choice": "required"})
+            model_settings=get_tool_choice_settings(),
         )
 
         logger.info(
@@ -624,7 +624,6 @@ class TatlockAgent(AgentInterface):
                 - tool_outputs: Dict mapping tool names to their outputs
                 - raw_output: The agent's raw text output
         """
-        from pydantic_ai.settings import ModelSettings
         from pydantic_ai.messages import (
             ModelRequest,
             ModelResponse,
@@ -684,11 +683,12 @@ class TatlockAgent(AgentInterface):
                 )
 
         # Run with scoped tools and tracker
+        from src.anthropic.model_selector import get_tool_choice_settings
         result = await scoped_agent.run(
             enriched_message,
             message_history=pydantic_history if pydantic_history else None,
             deps=tool_tracker,
-            model_settings=ModelSettings(extra_body={"tool_choice": "required"})
+            model_settings=get_tool_choice_settings(),
         )
 
         # Extract tool calls and results from the agent's messages

src/anthropic/__init__.py

@@ -7,11 +7,13 @@ Provides model selection with automatic fallback between Claude and Ollama.
 from src.anthropic.model_selector import (
     check_claude_health,
     get_model,
+    get_tool_choice_settings,
     is_claude_available,
 )
 
 __all__ = [
     "check_claude_health",
     "get_model",
+    "get_tool_choice_settings",
     "is_claude_available",
 ]

src/anthropic/model_selector.py

@@ -9,6 +9,7 @@ from typing import Union
 
 from pydantic_ai.models.anthropic import AnthropicModel
 from pydantic_ai.models.openai import OpenAIChatModel
+from pydantic_ai.providers.anthropic import AnthropicProvider
 
 from src.core.config import config
 from src.core.logging_config import get_logger
@@ -113,7 +114,7 @@ def get_model(prefer_cloud: bool | None = None) -> Union[AnthropicModel, OpenAIC
         )
         return AnthropicModel(
             model_name=config.ANTHROPIC_MODEL,
-            api_key=config.ANTHROPIC_API_KEY,
+            provider=AnthropicProvider(api_key=config.ANTHROPIC_API_KEY),
         )
 
     # Fall back to Ollama
@@ -131,6 +132,23 @@ def get_model(prefer_cloud: bool | None = None) -> Union[AnthropicModel, OpenAIC
     )
 
 
+def get_tool_choice_settings() -> 'ModelSettings':
+    """
+    Get model_settings for forcing tool calls on the first request.
+
+    For Claude: PydanticAI handles tool_choice natively, so no extra_body needed.
+    For Ollama: Pass tool_choice="required" via extra_body to force tool calling.
+    """
+    from pydantic_ai.settings import ModelSettings
+
+    if is_claude_available() and config.PREFER_CLOUD_BACKEND:
+        # PydanticAI's Anthropic model handles tool_choice internally
+        return ModelSettings()
+    else:
+        # Ollama needs explicit tool_choice via extra_body
+        return ModelSettings(extra_body={"tool_choice": "required"})
+
+
 def get_model_info() -> dict:
     """
     Get information about the current model configuration.

src/responses/service.py

@@ -651,16 +651,6 @@ async def create_response_with_steward(request: ResponseRequest) -> Response:
         # Build response output items
         output_items = []
 
-        # Add Steward reasoning as a reasoning output item
-        output_items.append(ReasoningOutputItem(
-            id=f"reasoning_{generate_id()}",
-            summary=[
-                "🎩 Steward's Analysis:",
-                enriched.steward_reasoning,
-            ],
-            status="completed"
-        ))
-
         # Add Tatlock's message
         output_items.append(MessageOutputItem(
             id=f"msg_{generate_id()}",

src/responses/streaming.py

@@ -166,26 +166,6 @@ class StreamingCoordinator:
                 conversation_id=conversation_id,
             )
 
-            # Stream Steward's analysis as reasoning summary
-            steward_lines = enriched.steward_reasoning.split('\n')
-            for line in steward_lines:
-                if line.strip():
-                    yield ReasoningSummaryDelta(delta=line + "\n")
-                    await asyncio.sleep(0.05)
-
-            yield ReasoningSummaryDone()
-
-            # Add Steward reasoning to output items
-            reasoning_item = ReasoningOutputItem(
-                id=f"reasoning_{generate_id()}",
-                summary=[
-                    "🎩 Steward's Analysis:",
-                    enriched.steward_reasoning,
-                ],
-                status="completed"
-            )
-            output_items.append(reasoning_item)
-
             # Initialize tool tracker
             tracker = ToolCallTracker(
                 recommended_capabilities=enriched.recommendation.recommended_capabilities,