fix: resolve all test warnings and complete DeepBoner rebrand

Code fixes:
- Update OpenAIModel → OpenAIChatModel (pydantic-ai deprecation)
- Add pytest.importorskip guards to 3 test files for optional deps

Rebrand fixes (missed in initial rename):
- src/services/llamaindex_rag.py: collection name
- src/tools/clinicaltrials.py: User-Agent header
- 7 doc files: package names, URLs, examples

Config:
- Add targeted pytest warning filters for Pydantic mock introspection
(known upstream issue: pydantic/pydantic#9927)

Result: 127 tests pass, 0 warnings (was 50 warnings)

docs/architecture/design-patterns.md

@@ -811,7 +811,7 @@ uvx fastmcp run src/mcp_servers/pubmed_server.py
     "pubmed": {
       "command": "python",
       "args": ["-m", "src.mcp_servers.pubmed_server"],
-      "cwd": "/path/to/deepcritical"
+      "cwd": "/path/to/deepboner"
     }
   }
 }

docs/brainstorming/04_OPENALEX_INTEGRATION.md

@@ -212,7 +212,7 @@ class OpenAlexTool(SearchTool):
                     "filter": "type:article,is_oa:true",
                     "sort": "cited_by_count:desc",
                     "per_page": max_results,
-                    "mailto": "deepcritical@example.com",  # Polite pool
+                    "mailto": "deepboner@example.com",  # Polite pool
                 },
             )
             data = resp.json()

docs/brainstorming/implementation/15_PHASE_OPENALEX.md

@@ -305,7 +305,7 @@ class OpenAlexTool:
         Args:
             email: Optional email for polite pool (faster responses)
         """
-        self.email = email or "deepcritical@example.com"
+        self.email = email or "deepboner@example.com"
 
     @property
     def name(self) -> str:

docs/guides/deployment.md

@@ -69,7 +69,7 @@ def predict(message, history):
    ```json
    {
      "mcpServers": {
-       "deepcritical": {
+       "deepboner": {
          "command": "uv",
          "args": ["run", "fastmcp", "run", "src/mcp_servers/pubmed_server.py"],
          "cwd": "/absolute/path/to/DeepBoner"
@@ -111,7 +111,7 @@ Instead of calling Anthropic API, we call a Modal function:
 # src/llm/modal_client.py
 import modal
 
-stub = modal.Stub("deepcritical-inference")
+stub = modal.Stub("deepboner-inference")
 
 @stub.function(gpu="A100")
 def generate_text(prompt: str):

docs/implementation/01_phase_foundation.md

@@ -23,7 +23,7 @@ uv --version  # Should be >= 0.4.0
 
 ```bash
 # From project root
-uv init --name deepcritical
+uv init --name deepboner
 uv python install 3.11  # Pin Python version
 ```
 
@@ -35,9 +35,9 @@ uv python install 3.11  # Pin Python version
 
 ```toml
 [project]
-name = "deepcritical"
+name = "deepboner"
 version = "0.1.0"
-description = "AI-Native Drug Repurposing Research Agent"
+description = "AI-Native Sexual Health Research Agent"
 readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
@@ -558,7 +558,7 @@ uv run pre-commit install
 ## 10. Implementation Checklist
 
 - [ ] Install `uv` and verify version
-- [ ] Run `uv init --name deepcritical`
+- [ ] Run `uv init --name deepboner`
 - [ ] Create `pyproject.toml` (copy from above)
 - [ ] Create directory structure (run mkdir commands)
 - [ ] Create `.env.example` and `.env`

docs/implementation/12_phase_mcp_server.md

@@ -672,7 +672,7 @@ class TestMCPServerIntegration:
 // %APPDATA%\Claude\claude_desktop_config.json (Windows)
 {
   "mcpServers": {
-    "deepcritical": {
+    "deepboner": {
       "url": "http://localhost:7860/gradio_api/mcp/"
     }
   }
@@ -684,8 +684,8 @@ class TestMCPServerIntegration:
 ```json
 {
   "mcpServers": {
-    "deepcritical": {
-      "url": "https://MCP-1st-Birthday-deepcritical.hf.space/gradio_api/mcp/"
+    "deepboner": {
+      "url": "https://your-space.hf.space/gradio_api/mcp/"
     }
   }
 }
@@ -696,7 +696,7 @@ class TestMCPServerIntegration:
 ```json
 {
   "mcpServers": {
-    "deepcritical": {
+    "deepboner": {
       "url": "https://your-space.hf.space/gradio_api/mcp/",
       "headers": {
         "Authorization": "Bearer hf_xxxxxxxxxxxxx"

docs/implementation/14_phase_demo_submission.md

@@ -198,7 +198,7 @@ AI-Powered Drug Repurposing Research Agent
 
 Connect to our MCP server at:
 ```
-https://MCP-1st-Birthday-deepcritical.hf.space/gradio_api/mcp/
+https://your-space.hf.space/gradio_api/mcp/
 ```
 
 Available tools:

pyproject.toml

@@ -126,6 +126,18 @@ markers = [
     "integration: Integration tests (real APIs)",
     "slow: Slow tests",
 ]
+# Filter warnings from unittest.mock introspecting Pydantic models.
+# This is a known upstream issue: https://github.com/pydantic/pydantic/issues/9927
+# When autospec=True, mock.py accesses deprecated Pydantic attributes during introspection.
+# We filter these specifically because it's NOT our code triggering deprecations.
+filterwarnings = [
+    # Pydantic 2.0 deprecations triggered by mock introspection
+    "ignore:The `__fields__` attribute is deprecated:pydantic.warnings.PydanticDeprecatedSince20",
+    "ignore:The `__fields_set__` attribute is deprecated:pydantic.warnings.PydanticDeprecatedSince20",
+    # Pydantic 2.11 deprecations triggered by mock introspection
+    "ignore:Accessing the 'model_computed_fields' attribute on the instance is deprecated:pydantic.warnings.PydanticDeprecatedSince211",
+    "ignore:Accessing the 'model_fields' attribute on the instance is deprecated:pydantic.warnings.PydanticDeprecatedSince211",
+]
 
 # ============== COVERAGE CONFIG ==============
 [tool.coverage.run]

src/agent_factory/judges.py

@@ -9,7 +9,7 @@ from huggingface_hub import InferenceClient
 from pydantic_ai import Agent
 from pydantic_ai.models.anthropic import AnthropicModel
 from pydantic_ai.models.huggingface import HuggingFaceModel
-from pydantic_ai.models.openai import OpenAIModel
+from pydantic_ai.models.openai import OpenAIChatModel
 from pydantic_ai.providers.anthropic import AnthropicProvider
 from pydantic_ai.providers.huggingface import HuggingFaceProvider
 from pydantic_ai.providers.openai import OpenAIProvider
@@ -48,7 +48,7 @@ def get_model() -> Any:
         logger.warning("Unknown LLM provider, defaulting to OpenAI", provider=llm_provider)
 
     openai_provider = OpenAIProvider(api_key=settings.openai_api_key)
-    return OpenAIModel(settings.openai_model, provider=openai_provider)
+    return OpenAIChatModel(settings.openai_model, provider=openai_provider)
 
 
 class JudgeHandler:

src/services/llamaindex_rag.py

@@ -25,7 +25,7 @@ class LlamaIndexRAGService:
 
     def __init__(
         self,
-        collection_name: str = "deepcritical_evidence",
+        collection_name: str = "deepboner_evidence",
         persist_dir: str | None = None,
         embedding_model: str | None = None,
         similarity_top_k: int = 5,
@@ -248,7 +248,7 @@ class LlamaIndexRAGService:
 
 
 def get_rag_service(
-    collection_name: str = "deepcritical_evidence",
+    collection_name: str = "deepboner_evidence",
     **kwargs: Any,
 ) -> LlamaIndexRAGService:
     """

src/tools/clinicaltrials.py

@@ -75,7 +75,7 @@ class ClinicalTrialsTool:
                 requests.get,
                 self.BASE_URL,
                 params=params,
-                headers={"User-Agent": "DeepCritical-Research-Agent/1.0"},
+                headers={"User-Agent": "DeepBoner-Research-Agent/1.0"},
                 timeout=30,
             )
             response.raise_for_status()

tests/unit/agent_factory/test_judges_factory.py

@@ -10,7 +10,7 @@ from pydantic_ai.models.anthropic import AnthropicModel
 # We expect this import to exist after we implement it, or we mock it if it's not there yet
 # For TDD, we assume we will use the library class
 from pydantic_ai.models.huggingface import HuggingFaceModel
-from pydantic_ai.models.openai import OpenAIModel
+from pydantic_ai.models.openai import OpenAIChatModel
 
 from src.agent_factory.judges import get_model
 
@@ -28,7 +28,7 @@ def test_get_model_openai(mock_settings):
     mock_settings.openai_model = "gpt-5.1"
 
     model = get_model()
-    assert isinstance(model, OpenAIModel)
+    assert isinstance(model, OpenAIChatModel)
     assert model.model_name == "gpt-5.1"
 
 
@@ -61,4 +61,4 @@ def test_get_model_default_fallback(mock_settings):
     mock_settings.openai_model = "gpt-5.1"
 
     model = get_model()
-    assert isinstance(model, OpenAIModel)
+    assert isinstance(model, OpenAIChatModel)

tests/unit/agents/test_hypothesis_agent.py

@@ -3,10 +3,19 @@
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
-from agent_framework import AgentRunResponse
 
-from src.agents.hypothesis_agent import HypothesisAgent
-from src.utils.models import Citation, Evidence, HypothesisAssessment, MechanismHypothesis
+# Skip all tests if agent_framework not installed (optional dep)
+pytest.importorskip("agent_framework")
+
+from agent_framework import AgentRunResponse  # noqa: E402
+
+from src.agents.hypothesis_agent import HypothesisAgent  # noqa: E402
+from src.utils.models import (  # noqa: E402
+    Citation,
+    Evidence,
+    HypothesisAssessment,
+    MechanismHypothesis,
+)
 
 
 @pytest.fixture

tests/unit/agents/test_report_agent.py

@@ -5,8 +5,11 @@ from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
-from src.agents.report_agent import ReportAgent
-from src.utils.models import (
+# Skip all tests if agent_framework not installed (optional dep)
+pytest.importorskip("agent_framework")
+
+from src.agents.report_agent import ReportAgent  # noqa: E402
+from src.utils.models import (  # noqa: E402
     Citation,
     Evidence,
     MechanismHypothesis,

tests/unit/test_magentic_fix.py

@@ -3,9 +3,13 @@
 from unittest.mock import MagicMock, patch
 
 import pytest
-from agent_framework import MagenticFinalResultEvent
 
-from src.orchestrator_magentic import MagenticOrchestrator
+# Skip all tests if agent_framework not installed (optional dep)
+pytest.importorskip("agent_framework")
+
+from agent_framework import MagenticFinalResultEvent  # noqa: E402
+
+from src.orchestrator_magentic import MagenticOrchestrator  # noqa: E402
 
 
 class MockChatMessage: