fix: exclude null fields from streaming chunks for Open WebUI compatibility

OpenAI's API omits null fields in streaming chunks, but Tatlock was
including them (content: null, reasoning_content: null). This caused
parsing issues in Open WebUI's streaming handler.

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

.gitea/workflows/build.yml

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

CHANGELOG.md

@@ -7,6 +7,37 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [2.0.5] - 2026-02-05
+
+### Fixed
+
+- **Streaming JSON compatibility** - Exclude null fields from streaming chunks using `exclude_none=True`; OpenAI's API omits null fields entirely, and including them (e.g., `content: null`, `reasoning_content: null`) caused parsing issues in Open WebUI
+
+## [2.0.4] - 2026-02-05
+
+### Fixed
+
+- **Open WebUI streaming compatibility** - Replaced `sse_starlette` `EventSourceResponse` with plain `StreamingResponse` for chat completions; `sse_starlette` added `\r\n` line endings and extra SSE fields that Open WebUI couldn't parse
+
+## [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 = "2.0.5"
 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
+        # Force tool_choice to make LLM actually call tools
-        from pydantic_ai.settings import ModelSettings
+        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/chat/router.py

@@ -7,7 +7,7 @@ import logging
 from typing import AsyncGenerator
 
 from fastapi import APIRouter
-from sse_starlette.sse import EventSourceResponse
+from starlette.responses import StreamingResponse
 
 from src.chat import service
 from src.chat.schemas import (
@@ -22,36 +22,33 @@ router = APIRouter(prefix="/chat", tags=["chat"])
 
 async def _stream_response(
     request: ChatCompletionRequest,
-) -> AsyncGenerator[dict, None]:
+) -> AsyncGenerator[str, None]:
     """
     Generate SSE stream for chat completion.
 
-    EventSourceResponse adds "data: " prefix automatically.
+    Yields raw SSE-formatted strings matching OpenAI's format exactly:
-    We just yield the dict/string content.
+    data: {json}\n\n
     """
     try:
         async for chunk in service.create_chat_completion_stream(request):
-            # Yield dict - EventSourceResponse will format as SSE
+            yield f"data: {chunk.model_dump_json(exclude_none=True)}\n\n"
-            yield {"data": chunk.model_dump_json()}
 
-        # Send [DONE] message
+        yield "data: [DONE]\n\n"
-        yield {"data": "[DONE]"}
 
     except Exception as e:
         logger.error(f"Error in streaming response: {e}")
-        error_data = {"error": {"message": str(e), "type": "internal_error"}}
+        error_data = json.dumps({"error": {"message": str(e), "type": "internal_error"}})
-        yield {"data": json.dumps(error_data)}
+        yield f"data: {error_data}\n\n"
 
 
 @router.post("/completions", response_model=ChatCompletionResponse)
 async def create_chat_completion(
     request: ChatCompletionRequest,
-) -> ChatCompletionResponse | EventSourceResponse:
+) -> ChatCompletionResponse | StreamingResponse:
     """
     Create chat completion (OpenAI-compatible).
 
     Supports both regular and streaming responses.
-    Currently returns mock lorem ipsum responses.
 
     Args:
         request: Chat completion request
@@ -63,6 +60,13 @@ async def create_chat_completion(
 
     if request.stream:
         logger.info("Streaming response requested")
-        return EventSourceResponse(_stream_response(request))
+        return StreamingResponse(
+            _stream_response(request),
+            media_type="text/event-stream",
+            headers={
+                "Cache-Control": "no-store",
+                "X-Accel-Buffering": "no",
+            },
+        )
 
     return await service.create_chat_completion(request)

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,