chore: bump version to 1.3.1

- Add claude-opus-4-7 capability entry (1M ctx, 128K output, adaptive
  thinking, supports_temperature=False, thinking_display=summarized)
- Suppress temperature param for Opus 4.7 (API returns 400)
- Add thinking display opt-in via new ModelCapabilities.thinking_display
  field - Opus 4.7 omits thinking by default, always send summarized
- Add xhigh effort level to mapping and Opus 4.7 effort_levels
- Add xhigh/max options to skill template dropdowns in admin console
- Align reasoning effort label capitalization across all console dropdowns
- Update example config to reference claude-opus-4-7
- 10 new tests with regression guards for Opus 4.6 backward compat

Verified against live API: streaming and completion calls succeed.

(cherry picked from commit 30c89f46c6)

.github/workflows/vendor-js.yml

@@ -48,7 +48,7 @@ jobs:
         id: detect
         run: |
           updates=()
-          for lib in katex hljs mermaid hls; do
+          for lib in katex hljs mermaid; do
             version=$(grep -oE "${lib}-[0-9.]+" pyproject.toml | head -1 | sed "s/${lib}-//")
             [[ -z "$version" ]] && continue
             [[ -d "turnstone/shared_static/${lib}-${version}" ]] && continue

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "turnstone"
-version = "1.4.0a2"
+version = "1.3.1"
 description = "Multi-node AI orchestration platform with tool use, agent routing, and cluster simulation."
 readme = "README.md"
 license = "BUSL-1.1"
@@ -80,7 +80,7 @@ include = [
   "turnstone/shared_static/katex-0.16.45/**/*",
   "turnstone/shared_static/hljs-11.11.1/**/*",
   "turnstone/shared_static/mermaid-11.14.0/**/*",
-  "turnstone/shared_static/hls-1.6.16/**/*",
+  "turnstone/shared_static/hls-1.6.15/**/*",
   "turnstone/sdk/py.typed",
   "turnstone/deploy/*.yaml",
 ]

tests/test_providers.py

@@ -152,52 +152,6 @@ class TestOpenAIProvider:
     def test_provider_name(self) -> None:
         assert self.provider.provider_name == "openai-compatible"
 
-    # -- _apply_thinking_mode -------------------------------------------------
-
-    def test_thinking_mode_none_does_nothing(self) -> None:
-        """No thinking params injected when thinking_mode is 'none'."""
-        caps = ModelCapabilities(thinking_mode="none")
-        extra_body: dict[str, Any] = {"chat_template_kwargs": {"reasoning_effort": "medium"}}
-        OpenAIProvider._apply_thinking_mode(extra_body, caps)
-        assert "enable_thinking" not in extra_body["chat_template_kwargs"]
-
-    def test_thinking_mode_manual_injects_param(self) -> None:
-        """Manual thinking mode injects enable_thinking into chat_template_kwargs."""
-        caps = ModelCapabilities(thinking_mode="manual")
-        extra_body: dict[str, Any] = {"chat_template_kwargs": {"reasoning_effort": "medium"}}
-        OpenAIProvider._apply_thinking_mode(extra_body, caps)
-        assert extra_body["chat_template_kwargs"]["enable_thinking"] is True
-        assert extra_body["chat_template_kwargs"]["reasoning_effort"] == "medium"
-
-    def test_thinking_mode_custom_param(self) -> None:
-        """Custom thinking_param (e.g. Granite's 'thinking') is used."""
-        caps = ModelCapabilities(thinking_mode="manual", thinking_param="thinking")
-        extra_body: dict[str, Any] = {"chat_template_kwargs": {}}
-        OpenAIProvider._apply_thinking_mode(extra_body, caps)
-        assert extra_body["chat_template_kwargs"]["thinking"] is True
-        assert "enable_thinking" not in extra_body["chat_template_kwargs"]
-
-    def test_thinking_mode_does_not_override_explicit(self) -> None:
-        """If operator explicitly set the param to False, provider respects it."""
-        caps = ModelCapabilities(thinking_mode="manual")
-        extra_body: dict[str, Any] = {"chat_template_kwargs": {"enable_thinking": False}}
-        OpenAIProvider._apply_thinking_mode(extra_body, caps)
-        assert extra_body["chat_template_kwargs"]["enable_thinking"] is False
-
-    def test_thinking_mode_creates_ctk_if_missing(self) -> None:
-        """Creates chat_template_kwargs dict if not present in extra_body."""
-        caps = ModelCapabilities(thinking_mode="manual")
-        extra_body: dict[str, Any] = {}
-        OpenAIProvider._apply_thinking_mode(extra_body, caps)
-        assert extra_body["chat_template_kwargs"]["enable_thinking"] is True
-
-    def test_thinking_mode_adaptive(self) -> None:
-        """Adaptive thinking mode also injects the param."""
-        caps = ModelCapabilities(thinking_mode="adaptive")
-        extra_body: dict[str, Any] = {"chat_template_kwargs": {}}
-        OpenAIProvider._apply_thinking_mode(extra_body, caps)
-        assert extra_body["chat_template_kwargs"]["enable_thinking"] is True
-
     # -- _sanitize_messages ---------------------------------------------------
 
     def test_sanitize_messages_none_content_no_tool_calls(self) -> None:
@@ -1246,6 +1200,31 @@ class TestAnthropicHelpers:
         assert caps.token_param == "max_tokens"
         assert caps.thinking_mode == "adaptive"
 
+    def test_capabilities_opus_4_7(self) -> None:
+        from turnstone.core.providers._anthropic import AnthropicProvider
+
+        provider = AnthropicProvider()
+        caps = provider.get_capabilities("claude-opus-4-7")
+        assert caps.context_window == 1000000
+        assert caps.max_output_tokens == 128000
+        assert caps.thinking_mode == "adaptive"
+        assert caps.supports_effort is True
+        assert "xhigh" in caps.effort_levels
+        assert caps.supports_temperature is False
+        assert caps.thinking_display == "summarized"
+        assert caps.supports_web_search is True
+        assert caps.supports_tool_search is True
+        assert caps.supports_vision is True
+
+    def test_capabilities_opus_4_7_dated(self) -> None:
+        from turnstone.core.providers._anthropic import AnthropicProvider
+
+        provider = AnthropicProvider()
+        caps = provider.get_capabilities("claude-opus-4-7-20260416")
+        assert caps.context_window == 1000000
+        assert caps.supports_temperature is False
+        assert caps.thinking_display == "summarized"
+
     def test_capabilities_lookup_unknown(self) -> None:
         from turnstone.core.providers._anthropic import AnthropicProvider
 
@@ -1956,6 +1935,18 @@ class TestAnthropicReasoningNone:
         assert "thinking" in result
         assert result["thinking"]["budget_tokens"] == 1024
 
+    def test_map_xhigh_effort(self) -> None:
+        from turnstone.core.providers._anthropic import _map_reasoning_to_effort
+
+        result = _map_reasoning_to_effort("xhigh", ("low", "medium", "high", "xhigh", "max"))
+        assert result == "xhigh"
+
+    def test_map_xhigh_rejected_by_model_without_it(self) -> None:
+        from turnstone.core.providers._anthropic import _map_reasoning_to_effort
+
+        result = _map_reasoning_to_effort("xhigh", ("low", "medium", "high", "max"))
+        assert result is None
+
 
 # ===========================================================================
 # TestWebSearch — provider-native web search
@@ -3114,6 +3105,103 @@ class TestAnthropicPromptCaching:
         assert "cache_control" in kwargs
         assert kwargs["cache_control"] == {"type": "ephemeral"}
 
+    def test_opus_4_7_no_temperature_in_kwargs(self) -> None:
+        """Opus 4.7 rejects temperature — must not appear in kwargs."""
+        caps = self.provider.get_capabilities("claude-opus-4-7")
+        kwargs = self.provider._build_thinking_and_kwargs(
+            caps=caps,
+            reasoning_effort="high",
+            extra_params=None,
+            max_tokens=8192,
+            temperature=0.5,
+            converted_msgs=[{"role": "user", "content": "hi"}],
+            system_prompt="",
+            model="claude-opus-4-7",
+            tools=None,
+        )
+        assert "temperature" not in kwargs
+
+    def test_opus_4_6_still_has_temperature(self) -> None:
+        """Opus 4.6 must still send temperature (regression guard)."""
+        caps = self.provider.get_capabilities("claude-opus-4-6")
+        kwargs = self.provider._build_thinking_and_kwargs(
+            caps=caps,
+            reasoning_effort="high",
+            extra_params=None,
+            max_tokens=8192,
+            temperature=0.5,
+            converted_msgs=[{"role": "user", "content": "hi"}],
+            system_prompt="",
+            model="claude-opus-4-6",
+            tools=None,
+        )
+        assert "temperature" in kwargs
+        assert kwargs["temperature"] == 1.0  # forced for adaptive thinking
+
+    def test_opus_4_7_thinking_display_summarized(self) -> None:
+        """Opus 4.7 must opt in to thinking display with 'summarized'."""
+        caps = self.provider.get_capabilities("claude-opus-4-7")
+        kwargs = self.provider._build_thinking_and_kwargs(
+            caps=caps,
+            reasoning_effort="high",
+            extra_params=None,
+            max_tokens=8192,
+            temperature=0.5,
+            converted_msgs=[{"role": "user", "content": "hi"}],
+            system_prompt="",
+            model="claude-opus-4-7",
+            tools=None,
+        )
+        assert kwargs["thinking"] == {"type": "adaptive", "display": "summarized"}
+
+    def test_opus_4_6_thinking_no_display(self) -> None:
+        """Opus 4.6 adaptive thinking should not include display key."""
+        caps = self.provider.get_capabilities("claude-opus-4-6")
+        kwargs = self.provider._build_thinking_and_kwargs(
+            caps=caps,
+            reasoning_effort="high",
+            extra_params=None,
+            max_tokens=8192,
+            temperature=0.5,
+            converted_msgs=[{"role": "user", "content": "hi"}],
+            system_prompt="",
+            model="claude-opus-4-6",
+            tools=None,
+        )
+        assert kwargs["thinking"] == {"type": "adaptive"}
+
+    def test_opus_4_7_xhigh_effort(self) -> None:
+        """Opus 4.7 xhigh effort passes through to output_config."""
+        caps = self.provider.get_capabilities("claude-opus-4-7")
+        kwargs = self.provider._build_thinking_and_kwargs(
+            caps=caps,
+            reasoning_effort="xhigh",
+            extra_params=None,
+            max_tokens=8192,
+            temperature=0.5,
+            converted_msgs=[{"role": "user", "content": "hi"}],
+            system_prompt="",
+            model="claude-opus-4-7",
+            tools=None,
+        )
+        assert kwargs["output_config"] == {"effort": "xhigh"}
+
+    def test_xhigh_effort_not_applied_to_opus_4_6(self) -> None:
+        """xhigh is not a valid effort level for Opus 4.6 — should be ignored."""
+        caps = self.provider.get_capabilities("claude-opus-4-6")
+        kwargs = self.provider._build_thinking_and_kwargs(
+            caps=caps,
+            reasoning_effort="xhigh",
+            extra_params=None,
+            max_tokens=8192,
+            temperature=0.5,
+            converted_msgs=[{"role": "user", "content": "hi"}],
+            system_prompt="",
+            model="claude-opus-4-6",
+            tools=None,
+        )
+        assert "output_config" not in kwargs
+
     @patch("turnstone.core.providers._anthropic._ensure_anthropic")
     def test_streaming_message_start_cache_metrics(self, mock_ensure: MagicMock) -> None:
         """Cache metrics from message_start flow into UsageInfo."""

tests/test_server_compat.py

@@ -1,273 +0,0 @@
-"""Tests for turnstone.core.server_compat — profile suggestion and merging."""
-
-from __future__ import annotations
-
-from typing import Any
-from unittest.mock import MagicMock
-
-from turnstone.core.providers._openai_chat import OpenAIChatCompletionsProvider
-from turnstone.core.providers._protocol import ModelCapabilities
-from turnstone.core.server_compat import merge_server_compat, suggest_profile
-
-# ---------------------------------------------------------------------------
-# suggest_profile
-# ---------------------------------------------------------------------------
-
-
-class TestSuggestProfile:
-    def test_vllm_gemma4(self) -> None:
-        p = suggest_profile("vllm", "google/gemma-4-31B-it")
-        assert p["capabilities"]["thinking_mode"] == "manual"
-        assert p["capabilities"]["thinking_param"] == "enable_thinking"
-        assert p["server_compat"]["extra_body"]["skip_special_tokens"] is False
-
-    def test_vllm_gemma3(self) -> None:
-        p = suggest_profile("vllm", "google/gemma-3-27b-it")
-        assert p["capabilities"]["thinking_mode"] == "manual"
-
-    def test_vllm_qwen3(self) -> None:
-        p = suggest_profile("vllm", "Qwen/Qwen3-8B")
-        assert p["capabilities"]["thinking_mode"] == "manual"
-        assert p["capabilities"]["thinking_param"] == "enable_thinking"
-        # Qwen doesn't need skip_special_tokens workaround
-        assert "extra_body" not in p.get("server_compat", {})
-
-    def test_vllm_qwq(self) -> None:
-        p = suggest_profile("vllm", "Qwen/QwQ-32B")
-        assert p["capabilities"]["thinking_mode"] == "manual"
-
-    def test_vllm_granite(self) -> None:
-        p = suggest_profile("vllm", "ibm-granite/granite-3.2-2b-instruct")
-        assert p["capabilities"]["thinking_param"] == "thinking"
-
-    def test_vllm_deepseek_r1(self) -> None:
-        p = suggest_profile("vllm", "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
-        assert p["capabilities"]["thinking_param"] == "thinking"
-
-    def test_vllm_deepseek_v3_no_thinking(self) -> None:
-        """DeepSeek-V3 is a chat model, not a reasoning model — no thinking profile."""
-        p = suggest_profile("vllm", "deepseek-ai/DeepSeek-V3-0324")
-        assert "capabilities" not in p
-        assert p["server_compat"]["server_type"] == "vllm"
-
-    def test_vllm_non_thinking_model(self) -> None:
-        p = suggest_profile("vllm", "meta-llama/Llama-3-70B-Instruct")
-        assert "capabilities" not in p
-        assert p["server_compat"]["server_type"] == "vllm"
-
-    def test_llama_cpp_non_thinking(self) -> None:
-        p = suggest_profile("llama.cpp", "some-model")
-        assert p["server_compat"]["server_type"] == "llama.cpp"
-        assert "capabilities" not in p
-
-    def test_llama_cpp_gemma_thinking(self) -> None:
-        """llama.cpp with Gemma model gets thinking profile with reasoning_format."""
-        p = suggest_profile("llama.cpp", "gemma-4-E4B-it.gguf")
-        assert p["capabilities"]["thinking_mode"] == "manual"
-        assert p["server_compat"]["extra_body"]["reasoning_format"] == "auto"
-
-    def test_llama_cpp_qwen_thinking(self) -> None:
-        p = suggest_profile("llama.cpp", "Qwen3-8B-Q4_K_M.gguf")
-        assert p["capabilities"]["thinking_mode"] == "manual"
-
-    def test_sglang(self) -> None:
-        p = suggest_profile("sglang", "some-model")
-        assert p["server_compat"]["server_type"] == "sglang"
-
-    def test_unknown_server(self) -> None:
-        assert suggest_profile("unknown", "foo") == {}
-
-    def test_empty_inputs(self) -> None:
-        assert suggest_profile("", "") == {}
-
-    def test_openai_compatible_fallback(self) -> None:
-        """Generic openai-compatible without a specific profile."""
-        assert suggest_profile("openai-compatible", "some-local-model") == {}
-
-    def test_case_insensitive_model_match(self) -> None:
-        """Model matching should be case-insensitive."""
-        p = suggest_profile("vllm", "Google/GEMMA-4-31B-IT")
-        assert p["capabilities"]["thinking_mode"] == "manual"
-
-    def test_holo_requires_holo2(self) -> None:
-        """Short 'holo' prefix shouldn't false-match; 'holo2' should match."""
-        p_short = suggest_profile("vllm", "some-org/hologram-7b")
-        assert "capabilities" not in p_short
-        p_long = suggest_profile("vllm", "some-org/Holo2-14B")
-        assert p_long["capabilities"]["thinking_mode"] == "manual"
-
-    def test_suggest_returns_deep_copy(self) -> None:
-        """Mutating the returned profile should not affect future calls."""
-        p1 = suggest_profile("vllm", "google/gemma-4-31B-it")
-        p1["capabilities"]["thinking_mode"] = "none"
-        p2 = suggest_profile("vllm", "google/gemma-4-31B-it")
-        assert p2["capabilities"]["thinking_mode"] == "manual"
-
-
-# ---------------------------------------------------------------------------
-# merge_server_compat
-# ---------------------------------------------------------------------------
-
-
-class TestMergeServerCompat:
-    def test_empty_compat_returns_base_only(self) -> None:
-        base = {"reasoning_effort": "medium"}
-        result = merge_server_compat(base, {})
-        assert result == {"chat_template_kwargs": {"reasoning_effort": "medium"}}
-
-    def test_extra_body_merged_top_level(self) -> None:
-        base = {"reasoning_effort": "medium"}
-        compat = {"extra_body": {"skip_special_tokens": False}}
-        result = merge_server_compat(base, compat)
-        assert result["skip_special_tokens"] is False
-        assert "chat_template_kwargs" in result
-
-    def test_full_vllm_gemma_compat(self) -> None:
-        base = {"reasoning_effort": "medium"}
-        compat = {
-            "server_type": "vllm",
-            "extra_body": {"skip_special_tokens": False},
-        }
-        result = merge_server_compat(base, compat)
-        assert result == {
-            "chat_template_kwargs": {"reasoning_effort": "medium"},
-            "skip_special_tokens": False,
-        }
-
-    def test_extra_body_chat_template_kwargs_deep_merged(self) -> None:
-        """chat_template_kwargs in extra_body is deep-merged, operator wins."""
-        base = {"reasoning_effort": "medium"}
-        compat = {
-            "extra_body": {
-                "chat_template_kwargs": {"custom_flag": True, "reasoning_effort": "high"},
-                "skip_special_tokens": False,
-            },
-        }
-        result = merge_server_compat(base, compat)
-        # Operator values win over base
-        assert result["chat_template_kwargs"]["custom_flag"] is True
-        assert result["chat_template_kwargs"]["reasoning_effort"] == "high"
-        assert result["skip_special_tokens"] is False
-
-    def test_extra_body_chat_template_kwargs_non_dict_ignored(self) -> None:
-        """Non-dict chat_template_kwargs in extra_body is safely ignored."""
-        base = {"reasoning_effort": "medium"}
-        compat = {"extra_body": {"chat_template_kwargs": "bad"}}
-        result = merge_server_compat(base, compat)
-        assert result["chat_template_kwargs"] == {"reasoning_effort": "medium"}
-
-    def test_base_not_mutated(self) -> None:
-        base = {"reasoning_effort": "medium"}
-        compat = {"extra_body": {"skip_special_tokens": False}}
-        merge_server_compat(base, compat)
-        assert "skip_special_tokens" not in base
-
-    def test_non_dict_extra_body_ignored(self) -> None:
-        """Gracefully handle malformed server_compat."""
-        base = {"reasoning_effort": "medium"}
-        result = merge_server_compat(base, {"extra_body": 42})
-        assert result == {"chat_template_kwargs": {"reasoning_effort": "medium"}}
-
-
-# ---------------------------------------------------------------------------
-# End-to-end: session merge + provider thinking mode
-# ---------------------------------------------------------------------------
-
-
-class TestEndToEndRequestShaping:
-    """Compose both layers — session builds extra_params, provider applies thinking."""
-
-    def test_vllm_gemma_full_flow(self) -> None:
-        """Session merges server workarounds, provider adds thinking param."""
-        caps = ModelCapabilities(thinking_mode="manual", thinking_param="enable_thinking")
-        base_ctk = {"reasoning_effort": "medium"}
-        server_compat = {
-            "server_type": "vllm",
-            "extra_body": {"skip_special_tokens": False},
-        }
-        # Step 1: session merges
-        extra_params = merge_server_compat(base_ctk, server_compat)
-        # Step 2: provider finalises
-        extra_body = dict(extra_params)
-        OpenAIChatCompletionsProvider._apply_thinking_mode(extra_body, caps)
-
-        assert extra_body == {
-            "chat_template_kwargs": {
-                "reasoning_effort": "medium",
-                "enable_thinking": True,
-            },
-            "skip_special_tokens": False,
-        }
-
-    def test_granite_thinking_key(self) -> None:
-        """Granite uses 'thinking' instead of 'enable_thinking'."""
-        caps = ModelCapabilities(thinking_mode="manual", thinking_param="thinking")
-        extra_params = merge_server_compat({"reasoning_effort": "low"}, {})
-        extra_body = dict(extra_params)
-        OpenAIChatCompletionsProvider._apply_thinking_mode(extra_body, caps)
-
-        assert extra_body["chat_template_kwargs"]["thinking"] is True
-        assert "enable_thinking" not in extra_body["chat_template_kwargs"]
-
-    def test_non_thinking_model_no_injection(self) -> None:
-        """Non-thinking model gets no thinking params."""
-        caps = ModelCapabilities()  # thinking_mode="none"
-        extra_params = merge_server_compat({"reasoning_effort": "medium"}, {})
-        extra_body = dict(extra_params)
-        OpenAIChatCompletionsProvider._apply_thinking_mode(extra_body, caps)
-
-        assert extra_body == {"chat_template_kwargs": {"reasoning_effort": "medium"}}
-
-
-# ---------------------------------------------------------------------------
-# Probe integration: suggest_profile called from _detect_openai_compat
-# ---------------------------------------------------------------------------
-
-
-class TestProbeIntegration:
-    def test_detect_vllm_gemma_suggests_profile(self) -> None:
-        """_detect_openai_compat returns suggested_capabilities and suggested_server_compat."""
-        from turnstone.core.model_registry import _detect_openai_compat
-
-        result: dict[str, Any] = {
-            "reachable": True,
-            "model_found": True,
-            "available_models": ["google/gemma-4-31B-it"],
-            "context_window": None,
-            "server_type": None,
-            "error": None,
-        }
-        model_obj = MagicMock()
-        model_obj.model_dump.return_value = {"owned_by": "vllm"}
-
-        _detect_openai_compat(
-            result, model_obj, "google/gemma-4-31B-it", "http://localhost:8000/v1"
-        )
-
-        assert result["server_type"] == "vllm"
-        assert result["suggested_capabilities"]["thinking_mode"] == "manual"
-        assert result["suggested_capabilities"]["thinking_param"] == "enable_thinking"
-        assert result["suggested_server_compat"]["extra_body"]["skip_special_tokens"] is False
-
-    def test_detect_non_thinking_no_suggested_capabilities(self) -> None:
-        """Non-thinking vLLM model gets server_compat but no capabilities suggestion."""
-        from turnstone.core.model_registry import _detect_openai_compat
-
-        result: dict[str, Any] = {
-            "reachable": True,
-            "model_found": True,
-            "available_models": ["meta-llama/Llama-3-70B"],
-            "context_window": None,
-            "server_type": None,
-            "error": None,
-        }
-        model_obj = MagicMock()
-        model_obj.model_dump.return_value = {"owned_by": "vllm"}
-
-        _detect_openai_compat(
-            result, model_obj, "meta-llama/Llama-3-70B", "http://localhost:8000/v1"
-        )
-
-        assert result["server_type"] == "vllm"
-        assert "suggested_capabilities" not in result
-        assert result["suggested_server_compat"]["server_type"] == "vllm"

tests/test_session.py

@@ -1080,85 +1080,3 @@ class TestProviderExtraParams:
         openai_prov = create_provider("openai")
         result = session._provider_extra_params(provider=openai_prov)
         assert result is None
-
-    def test_server_compat_extra_body_merged(self, tmp_db):
-        """server_compat.extra_body workarounds are merged into extra_params."""
-        from turnstone.core.model_registry import ModelConfig, ModelRegistry
-
-        session = self._session_with_provider("openai-compatible", tmp_db)
-        cfg = ModelConfig(
-            alias="test",
-            base_url="http://localhost:8000/v1",
-            api_key="none",
-            model="google/gemma-4-31B-it",
-            server_compat={
-                "extra_body": {"skip_special_tokens": False},
-            },
-        )
-        session._registry = ModelRegistry(models={"test": cfg}, default="test")
-        session._model_alias = "test"
-        result = session._provider_extra_params()
-        assert result is not None
-        assert result["chat_template_kwargs"]["reasoning_effort"] == "medium"
-        assert result["skip_special_tokens"] is False
-
-    def test_empty_server_compat_backwards_compatible(self, tmp_db):
-        """Empty server_compat produces same output as before."""
-        session = self._session_with_provider("openai-compatible", tmp_db)
-        result = session._provider_extra_params()
-        assert result == {"chat_template_kwargs": {"reasoning_effort": "medium"}}
-
-    def test_server_compat_with_reasoning_effort_override(self, tmp_db):
-        """reasoning_effort override works alongside server_compat."""
-        from turnstone.core.model_registry import ModelConfig, ModelRegistry
-
-        session = self._session_with_provider("openai-compatible", tmp_db)
-        cfg = ModelConfig(
-            alias="test",
-            base_url="http://localhost:8000/v1",
-            api_key="none",
-            model="google/gemma-4-31B-it",
-            server_compat={"extra_body": {"skip_special_tokens": False}},
-        )
-        session._registry = ModelRegistry(models={"test": cfg}, default="test")
-        session._model_alias = "test"
-        result = session._provider_extra_params(reasoning_effort="high")
-        assert result is not None
-        assert result["chat_template_kwargs"]["reasoning_effort"] == "high"
-        assert result["skip_special_tokens"] is False
-
-    def test_model_alias_resolves_target_compat(self, tmp_db):
-        """model_alias parameter selects compat from the target, not the primary."""
-        from turnstone.core.model_registry import ModelConfig, ModelRegistry
-
-        session = self._session_with_provider("openai-compatible", tmp_db)
-        primary = ModelConfig(
-            alias="primary",
-            base_url="http://localhost:8000/v1",
-            api_key="none",
-            model="google/gemma-4-31B-it",
-            server_compat={"extra_body": {"skip_special_tokens": False}},
-        )
-        fallback = ModelConfig(
-            alias="fallback",
-            base_url="http://localhost:9000/v1",
-            api_key="none",
-            model="meta-llama/Llama-3-70B",
-        )
-        reg = ModelRegistry(
-            models={"primary": primary, "fallback": fallback},
-            default="primary",
-            fallback=["fallback"],
-        )
-        session._registry = reg
-        session._model_alias = "primary"
-
-        # Primary alias → gets Gemma workaround
-        result_primary = session._provider_extra_params()
-        assert result_primary is not None
-        assert result_primary["skip_special_tokens"] is False
-
-        # Fallback alias → no compat, just base kwargs
-        result_fallback = session._provider_extra_params(model_alias="fallback")
-        assert result_fallback == {"chat_template_kwargs": {"reasoning_effort": "medium"}}
-        assert "skip_special_tokens" not in result_fallback

turnstone.example.toml

@@ -39,7 +39,7 @@
 # supports_web_search = false
 #
 # [models.claude]
-# name = "claude-opus-4-6"
+# name = "claude-opus-4-7"
 # provider = "anthropic"
 
 # --- Database (turnstone, node, console) ---

turnstone/__init__.py

@@ -1,3 +1,3 @@
 """turnstone - Multi-node AI orchestration platform with tool use, agent routing, and cluster simulation."""
 
-__version__ = "1.4.0a2"
+__version__ = "1.3.1"

turnstone/console/static/admin.js

@@ -4596,19 +4596,6 @@ function _renderModels(items) {
   });
 }
 
-function _isPlainObject(v) {
-  return v !== null && typeof v === "object" && !Array.isArray(v);
-}
-
-function _toggleThinkingParam() {
-  var mode = document.getElementById("model-thinking-mode").value;
-  var row = document.getElementById("model-thinking-param-row");
-  row.style.display = mode ? "" : "none";
-  // Set default when first enabling
-  var paramEl = document.getElementById("model-thinking-param");
-  if (mode && !paramEl.value) paramEl.value = "enable_thinking";
-}
-
 function showCreateModelModal() {
   _modelCreateTrigger = document.activeElement;
   var ov = document.getElementById("model-create-overlay");
@@ -4627,18 +4614,7 @@ function showCreateModelModal() {
   document.getElementById("model-temperature").value = "";
   document.getElementById("model-max-tokens").value = "";
   document.getElementById("model-reasoning-effort").value = "";
-  document.getElementById("model-server-type").value = "";
-  document.getElementById("model-thinking-mode").value = "";
-  document.getElementById("model-thinking-param").value = "";
-  document.getElementById("model-thinking-param-row").style.display = "none";
-  document.getElementById("model-extra-body").value = "";
   document.getElementById("model-capabilities").value = "";
-  // Clear validation error styling from prior submit attempts
-  ["model-extra-body", "model-capabilities"].forEach(function (id) {
-    var el = document.getElementById(id);
-    el.removeAttribute("aria-invalid");
-    el.style.borderColor = "";
-  });
   document.getElementById("model-enabled").checked = true;
   document.getElementById("model-detect-result").style.display = "none";
   document.getElementById("model-detect-btn").disabled = false;
@@ -4677,49 +4653,15 @@ function showEditModelModal(definitionId) {
         m.max_tokens != null ? m.max_tokens : "";
       document.getElementById("model-reasoning-effort").value =
         m.reasoning_effort != null ? m.reasoning_effort : "";
-      // Parse capabilities JSON and extract server_compat for structured fields
-      var capsObj = {};
+      // Parse capabilities JSON for display
+      var caps = m.capabilities || "{}";
       try {
-        capsObj = JSON.parse(m.capabilities || "{}");
+        caps = JSON.stringify(JSON.parse(caps), null, 2);
       } catch (e) {
-        /* keep empty */
+        /* keep raw */
       }
-      // Defend against null/array/primitive values in the DB
-      if (!_isPlainObject(capsObj)) capsObj = {};
-      var sc = _isPlainObject(capsObj.server_compat)
-        ? capsObj.server_compat
-        : {};
-      // Only extract thinking_mode into the dropdown when the UI can
-      // represent it ("manual" or "").  Values like "adaptive" (Anthropic-
-      // only) stay in the raw capabilities JSON so they aren't silently
-      // lost on save.
-      var tmVal = capsObj.thinking_mode || "";
-      var tmRepresentable = tmVal === "" || tmVal === "manual";
-      if (tmRepresentable) {
-        document.getElementById("model-thinking-mode").value = tmVal;
-        document.getElementById("model-thinking-param").value =
-          capsObj.thinking_param || "";
-      } else {
-        document.getElementById("model-thinking-mode").value = "";
-        document.getElementById("model-thinking-param").value = "";
-      }
-      _toggleThinkingParam();
-      // Server compat: server_type and extra_body workarounds
-      document.getElementById("model-server-type").value = sc.server_type || "";
-      var eb = sc.extra_body || {};
-      var ebText = JSON.stringify(eb, null, 2);
-      document.getElementById("model-extra-body").value =
-        ebText === "{}" ? "" : ebText;
-      // Remove structured fields from capabilities display — only delete
-      // thinking_mode/thinking_param when the UI successfully captured them.
-      delete capsObj.server_compat;
-      if (tmRepresentable) {
-        delete capsObj.thinking_mode;
-        delete capsObj.thinking_param;
-      }
-      var capsText = JSON.stringify(capsObj, null, 2);
-      document.getElementById("model-capabilities").value =
-        capsText === "{}" ? "" : capsText;
+      if (caps === "{}") caps = "";
+      document.getElementById("model-capabilities").value = caps;
       document.getElementById("model-enabled").checked = m.enabled !== false;
       _applyProviderDefaults();
     })
@@ -4752,65 +4694,15 @@ function submitCreateModel() {
     return;
   }
 
-  var capsEl = document.getElementById("model-capabilities");
-  var capsText = capsEl.value.trim();
+  var capsText = document.getElementById("model-capabilities").value.trim();
   var caps = {};
-  capsEl.removeAttribute("aria-invalid");
-  capsEl.style.borderColor = "";
   if (capsText) {
     try {
       caps = JSON.parse(capsText);
     } catch (e) {
-      capsEl.setAttribute("aria-invalid", "true");
-      capsEl.style.borderColor = "var(--red)";
       _showModelError("Invalid JSON in capabilities");
       return;
     }
-    if (!_isPlainObject(caps)) {
-      capsEl.setAttribute("aria-invalid", "true");
-      capsEl.style.borderColor = "var(--red)";
-      _showModelError(
-        "Capabilities must be a JSON object (not array or primitive)",
-      );
-      return;
-    }
-  }
-
-  // Thinking mode → capabilities (provider uses this to inject
-  // the correct chat_template_kwargs param automatically).
-  var thinkingMode = document.getElementById("model-thinking-mode").value;
-  if (thinkingMode) {
-    caps.thinking_mode = thinkingMode;
-    // Preserve thinking_param so Granite/DeepSeek "thinking" key
-    // isn't silently reverted to the default "enable_thinking".
-    var savedParam = document.getElementById("model-thinking-param").value;
-    if (savedParam) caps.thinking_param = savedParam;
-  }
-
-  // Build server_compat from structured fields
-  var serverCompat = {};
-  var serverType = document.getElementById("model-server-type").value;
-  if (serverType) serverCompat.server_type = serverType;
-  var ebEl = document.getElementById("model-extra-body");
-  var ebText = ebEl.value.trim();
-  ebEl.removeAttribute("aria-invalid");
-  ebEl.style.borderColor = "";
-  if (ebText) {
-    try {
-      var ebParsed = JSON.parse(ebText);
-      if (!_isPlainObject(ebParsed)) {
-        throw new Error("not an object");
-      }
-      serverCompat.extra_body = ebParsed;
-    } catch (e) {
-      ebEl.setAttribute("aria-invalid", "true");
-      ebEl.style.borderColor = "var(--red)";
-      _showModelError("Extra body params must be a JSON object");
-      return;
-    }
-  }
-  if (Object.keys(serverCompat).length > 0) {
-    caps.server_compat = serverCompat;
   }
 
   var form = {
@@ -5003,52 +4895,6 @@ function detectModel() {
         resultDiv.appendChild(
           _detectResultLine("Server type: " + d.server_type),
         );
-        // Auto-fill server type if not already set and value is a known option
-        var stEl = document.getElementById("model-server-type");
-        var stOpts = Array.from(stEl.options).map(function (o) {
-          return o.value;
-        });
-        if (!stEl.value && stOpts.indexOf(d.server_type) !== -1)
-          stEl.value = d.server_type;
-      }
-      // Auto-fill capabilities from suggested profile
-      if (d.suggested_capabilities) {
-        var sc2 = d.suggested_capabilities;
-        var tmEl = document.getElementById("model-thinking-mode");
-        if (!tmEl.value && sc2.thinking_mode) {
-          tmEl.value = sc2.thinking_mode;
-        }
-        if (sc2.thinking_param) {
-          var tpEl = document.getElementById("model-thinking-param");
-          if (!tpEl.value) tpEl.value = sc2.thinking_param;
-        }
-        _toggleThinkingParam();
-      }
-      // Auto-fill server compat from suggested profile
-      if (d.suggested_server_compat) {
-        var ssc = d.suggested_server_compat;
-        var stEl2 = document.getElementById("model-server-type");
-        var stOpts2 = Array.from(stEl2.options).map(function (o) {
-          return o.value;
-        });
-        if (
-          !stEl2.value &&
-          ssc.server_type &&
-          stOpts2.indexOf(ssc.server_type) !== -1
-        )
-          stEl2.value = ssc.server_type;
-        if (ssc.extra_body) {
-          var ebEl2 = document.getElementById("model-extra-body");
-          if (!ebEl2.value.trim()) {
-            var ebJson = JSON.stringify(ssc.extra_body, null, 2);
-            if (ebJson !== "{}") ebEl2.value = ebJson;
-          }
-        }
-      }
-      if (d.suggested_capabilities || d.suggested_server_compat) {
-        resultDiv.appendChild(
-          _detectResultLine("\u2713 Compatibility profile suggested", "green"),
-        );
       }
       resultDiv.style.borderColor = "var(--green)";
     })
@@ -5142,11 +4988,6 @@ function _applyProviderDefaults() {
   if (!def) return;
   document.getElementById("model-base-url").placeholder = def.urlPlaceholder;
   document.getElementById("model-name").placeholder = def.modelPlaceholder;
-  // Server compat section only applies to local model servers
-  var scSection = document.getElementById("model-server-compat-section");
-  if (scSection) {
-    scSection.style.display = provider === "openai-compatible" ? "" : "none";
-  }
 }
 
 /* Populate the model name datalist with known model prefixes for the

turnstone/console/static/index.html

@@ -1280,6 +1280,8 @@ window.TURNSTONE_KB_SHORTCUTS = [
             <option value="low">Low</option>
             <option value="medium">Medium</option>
             <option value="high">High</option>
+            <option value="xhigh">Extra High</option>
+            <option value="max">Max</option>
           </select>
         </div>
         <div><label for="csk-max-tokens">Max Tokens</label><input id="csk-max-tokens" type="number" min="1" placeholder="System default"></div>
@@ -1399,6 +1401,8 @@ window.TURNSTONE_KB_SHORTCUTS = [
             <option value="low">Low</option>
             <option value="medium">Medium</option>
             <option value="high">High</option>
+            <option value="xhigh">Extra High</option>
+            <option value="max">Max</option>
           </select>
         </div>
         <div><label for="esk-max-tokens">Max Tokens</label><input id="esk-max-tokens" type="number" min="1" placeholder="System default"></div>
@@ -1559,34 +1563,14 @@ window.TURNSTONE_KB_SHORTCUTS = [
     <label for="model-reasoning-effort">Reasoning Effort <span style="font-weight:400;text-transform:none">(empty = use global default)</span></label>
     <select id="model-reasoning-effort">
       <option value="">Global default</option>
-      <option value="none">none</option>
-      <option value="minimal">minimal</option>
-      <option value="low">low</option>
-      <option value="medium">medium</option>
-      <option value="high">high</option>
-      <option value="xhigh">xhigh</option>
-      <option value="max">max</option>
+      <option value="none">None</option>
+      <option value="minimal">Minimal</option>
+      <option value="low">Low</option>
+      <option value="medium">Medium</option>
+      <option value="high">High</option>
+      <option value="xhigh">Extra High</option>
+      <option value="max">Max</option>
     </select>
-    <div id="model-server-compat-section" style="display:none">
-    <div class="modal-section-divider" role="separator">Server Compatibility</div>
-    <label for="model-server-type">Server Type <span style="font-weight:400;text-transform:none">(auto-detected or manual)</span></label>
-    <select id="model-server-type">
-      <option value="">Auto / Unknown</option>
-      <option value="vllm">vLLM</option>
-      <option value="llama.cpp">llama.cpp</option>
-      <option value="openai-compatible">Other OpenAI-compatible</option>
-    </select>
-    <label for="model-thinking-mode">Thinking Mode <span style="font-weight:400;text-transform:none">(reasoning / chain-of-thought)</span></label>
-    <select id="model-thinking-mode" onchange="_toggleThinkingParam()">
-      <option value="">None</option>
-      <option value="manual">Enabled</option>
-    </select>
-    <div id="model-thinking-param-row" style="display:none">
-      <label for="model-thinking-param" style="font-size:11px">Template param name <span style="font-weight:400;text-transform:none">(Granite/DeepSeek use "thinking")</span></label>
-      <input type="text" id="model-thinking-param" value="enable_thinking" placeholder="enable_thinking" style="font-family:var(--font-mono);font-size:11px"></div>
-    <label for="model-extra-body">Extra body params <span style="font-weight:400;text-transform:none">(JSON, merged into every request)</span></label>
-    <textarea id="model-extra-body" rows="2" placeholder='{"skip_special_tokens": false}' style="font-family:var(--font-mono);font-size:11px"></textarea>
-    </div>
     <label for="model-capabilities">Capabilities <span style="font-weight:400;text-transform:none">(JSON)</span></label>
     <textarea id="model-capabilities" rows="3" placeholder='{"supports_vision": true}' style="font-family:var(--font-mono);font-size:11px"></textarea>
     <div style="display:flex;gap:20px;margin-top:14px">

turnstone/core/model_registry.py

@@ -39,9 +39,6 @@ class ModelConfig:
     temperature: float | None = None
     max_tokens: int | None = None
     reasoning_effort: str | None = None
-    # Server compatibility settings for openai-compatible backends.
-    # Populated from capabilities["server_compat"] during load.
-    server_compat: dict[str, Any] = field(default_factory=dict)
 
 
 # ---------------------------------------------------------------------------
@@ -269,10 +266,6 @@ def load_model_registry(
                             caps = parsed
                     except (_json.JSONDecodeError, TypeError):
                         pass  # falls back to empty capabilities
-                # Extract server_compat from capabilities (namespaced key)
-                row_server_compat = caps.pop("server_compat", {})
-                if not isinstance(row_server_compat, dict):
-                    row_server_compat = {}
                 row_base_url = _resolve_env_vars(row.get("base_url", ""))
                 row_provider = _resolve_openai_provider(row.get("provider", "openai"), row_base_url)
                 row_model = row["model"]
@@ -297,7 +290,6 @@ def load_model_registry(
                     reasoning_effort=row_reasoning_effort
                     if row_reasoning_effort is not None
                     else None,
-                    server_compat=row_server_compat,
                 )
         except Exception:
             log.warning("Failed to load model definitions from storage", exc_info=True)
@@ -341,14 +333,6 @@ def load_model_registry(
         raw_effort = entry.get("reasoning_effort")
         if raw_effort is not None:
             entry_effort = str(raw_effort)
-        entry_caps = (
-            dict(entry.get("capabilities", {}))
-            if isinstance(entry.get("capabilities"), dict)
-            else {}
-        )
-        entry_server_compat = entry_caps.pop("server_compat", {})
-        if not isinstance(entry_server_compat, dict):
-            entry_server_compat = {}
         configs[alias] = ModelConfig(
             alias=alias,
             base_url=entry_base_url,
@@ -356,12 +340,13 @@ def load_model_registry(
             model=model_name,
             context_window=entry.get("context_window", context_window),
             provider=_resolve_openai_provider(entry.get("provider", "openai"), entry_base_url),
-            capabilities=entry_caps,
+            capabilities=entry.get("capabilities", {})
+            if isinstance(entry.get("capabilities"), dict)
+            else {},
             source="config",
             temperature=entry_temp,
             max_tokens=entry_max_tokens,
             reasoning_effort=entry_effort,
-            server_compat=entry_server_compat,
         )
 
     # 3. Ensure a "default" entry from CLI args (only if not already defined
@@ -657,12 +642,3 @@ def _detect_openai_compat(
         result["server_type"] = "vllm"
     else:
         result["server_type"] = "openai-compatible"
-
-    # Suggest capabilities and server compat based on detected server_type
-    from turnstone.core.server_compat import suggest_profile
-
-    suggested = suggest_profile(result.get("server_type", ""), model_id)
-    if suggested.get("capabilities"):
-        result["suggested_capabilities"] = suggested["capabilities"]
-    if suggested.get("server_compat"):
-        result["suggested_server_compat"] = suggested["server_compat"]

turnstone/core/providers/_anthropic.py

@@ -83,6 +83,19 @@ _ANTHROPIC_DEFAULT = ModelCapabilities(
 )
 
 _ANTHROPIC_CAPABILITIES: dict[str, ModelCapabilities] = {
+    "claude-opus-4-7": ModelCapabilities(
+        context_window=1000000,
+        max_output_tokens=128000,
+        token_param="max_tokens",
+        thinking_mode="adaptive",
+        supports_effort=True,
+        effort_levels=("low", "medium", "high", "xhigh", "max"),
+        supports_web_search=True,
+        supports_tool_search=True,
+        supports_vision=True,
+        supports_temperature=False,
+        thinking_display="summarized",
+    ),
     "claude-opus-4-6": ModelCapabilities(
         context_window=1000000,
         max_output_tokens=128000,
@@ -139,7 +152,7 @@ def _map_reasoning_to_effort(
     valid_levels: tuple[str, ...],
 ) -> str | None:
     """Map turnstone reasoning_effort to Anthropic effort parameter."""
-    mapping = {"low": "low", "medium": "medium", "high": "high", "max": "max"}
+    mapping = {"low": "low", "medium": "medium", "high": "high", "xhigh": "xhigh", "max": "max"}
     effort = mapping.get(reasoning_effort)
     if effort and effort in valid_levels:
         return effort
@@ -226,8 +239,12 @@ class AnthropicProvider:
         """Build the full kwargs dict with thinking mode and effort params."""
         thinking_params: dict[str, Any] = {}
         if caps.thinking_mode == "adaptive":
-            thinking_params = {"thinking": {"type": "adaptive"}}
-            temperature = 1.0  # Required with thinking
+            thinking_dict: dict[str, Any] = {"type": "adaptive"}
+            if caps.thinking_display:
+                thinking_dict["display"] = caps.thinking_display
+            thinking_params = {"thinking": thinking_dict}
+            if caps.supports_temperature:
+                temperature = 1.0  # Required with thinking
         elif caps.thinking_mode == "manual":
             thinking_params = self._reasoning_params(reasoning_effort, extra_params, max_tokens)
             if thinking_params:
@@ -237,12 +254,13 @@ class AnthropicProvider:
             "model": model,
             "messages": converted_msgs,
             caps.token_param: max_tokens,
-            "temperature": temperature,
             # Automatic prompt caching — the API places the cache breakpoint
             # on the last cacheable block and advances it as conversation grows.
             # 90% input cost reduction on cache hits; 1.25x write on first turn.
             "cache_control": {"type": "ephemeral"},
         }
+        if caps.supports_temperature:
+            kwargs["temperature"] = temperature
         if system_prompt:
             kwargs["system"] = system_prompt
         if tools:
@@ -252,7 +270,7 @@ class AnthropicProvider:
             kwargs["tools"] = anthropic_tools
         kwargs.update(thinking_params)
 
-        # Effort param for models that support it (Opus 4.6, Sonnet 4.6, Opus 4.5)
+        # Effort param for models that support it (Opus 4.7, Opus 4.6, Sonnet 4.6, Opus 4.5)
         if caps.supports_effort and reasoning_effort:
             effort = _map_reasoning_to_effort(reasoning_effort, caps.effort_levels)
             if effort:
@@ -568,10 +586,9 @@ class AnthropicProvider:
         extra_params: dict[str, Any] | None = None,
         deferred_names: frozenset[str] | None = None,
         cancel_ref: list[Any] | None = None,
-        capabilities: ModelCapabilities | None = None,
     ) -> Iterator[StreamChunk]:
         _ensure_anthropic()
-        caps = capabilities or self.get_capabilities(model)
+        caps = self.get_capabilities(model)
         system_prompt, converted_msgs = self._convert_messages(messages)
         kwargs = self._build_thinking_and_kwargs(
             caps,
@@ -772,10 +789,9 @@ class AnthropicProvider:
         reasoning_effort: str = "medium",
         extra_params: dict[str, Any] | None = None,
         deferred_names: frozenset[str] | None = None,
-        capabilities: ModelCapabilities | None = None,
     ) -> CompletionResult:
         _ensure_anthropic()
-        caps = capabilities or self.get_capabilities(model)
+        caps = self.get_capabilities(model)
         system_prompt, converted_msgs = self._convert_messages(messages)
         kwargs = self._build_thinking_and_kwargs(
             caps,

turnstone/core/providers/_openai_chat.py

@@ -108,52 +108,6 @@ class OpenAIChatCompletionsProvider:
         kwargs["web_search_options"] = {}
         return tools
 
-    # -- thinking mode -------------------------------------------------------
-
-    @staticmethod
-    def _apply_thinking_mode(
-        extra_body: dict[str, Any],
-        caps: ModelCapabilities,
-    ) -> None:
-        """Inject thinking-mode params into *extra_body* based on capabilities.
-
-        When ``caps.thinking_mode`` is ``"manual"`` or ``"adaptive"``, sets
-        the model-family-specific key (``caps.thinking_param``, e.g.
-        ``"enable_thinking"`` or ``"thinking"``) to ``True`` inside
-        ``extra_body["chat_template_kwargs"]``.
-
-        Does nothing when thinking mode is ``"none"`` or the key is already
-        present (operator override via ``extra_body`` takes precedence).
-        """
-        if caps.thinking_mode == "none":
-            return
-        ctk = extra_body.get("chat_template_kwargs")
-        if not isinstance(ctk, dict):
-            ctk = {}
-            extra_body["chat_template_kwargs"] = ctk
-        if caps.thinking_param not in ctk:
-            ctk[caps.thinking_param] = True
-
-    def _finalize_extra_body(
-        self,
-        extra_params: dict[str, Any] | None,
-        caps: ModelCapabilities,
-    ) -> dict[str, Any] | None:
-        """Build the final ``extra_body``, injecting thinking params if needed.
-
-        Returns ``None`` when the result would be empty (no extra_body needed).
-        Shallow-copies *extra_params* and its ``chat_template_kwargs`` so the
-        caller's dict is never mutated.
-        """
-        eb: dict[str, Any] = {}
-        if extra_params:
-            eb = dict(extra_params)
-            ctk = eb.get("chat_template_kwargs")
-            if isinstance(ctk, dict):
-                eb["chat_template_kwargs"] = dict(ctk)
-        self._apply_thinking_mode(eb, caps)
-        return eb or None
-
     # -- streaming -----------------------------------------------------------
 
     def create_streaming(
@@ -169,9 +123,8 @@ class OpenAIChatCompletionsProvider:
         extra_params: dict[str, Any] | None = None,
         deferred_names: frozenset[str] | None = None,
         cancel_ref: list[Any] | None = None,
-        capabilities: ModelCapabilities | None = None,
     ) -> Iterator[StreamChunk]:
-        caps = capabilities or self.get_capabilities(model)
+        caps = self.get_capabilities(model)
         messages = self._prepare_messages(messages)
         kwargs: dict[str, Any] = {
             "model": model,
@@ -186,9 +139,8 @@ class OpenAIChatCompletionsProvider:
         tools = apply_tool_search(caps, tools, deferred_names)
         if tools:
             kwargs["tools"] = tools
-        extra_body = self._finalize_extra_body(extra_params, caps)
-        if extra_body:
-            kwargs["extra_body"] = extra_body
+        if extra_params:
+            kwargs["extra_body"] = extra_params
 
         log.debug(
             "openai.chat.request",
@@ -298,9 +250,8 @@ class OpenAIChatCompletionsProvider:
         reasoning_effort: str = "medium",
         extra_params: dict[str, Any] | None = None,
         deferred_names: frozenset[str] | None = None,
-        capabilities: ModelCapabilities | None = None,
     ) -> CompletionResult:
-        caps = capabilities or self.get_capabilities(model)
+        caps = self.get_capabilities(model)
         messages = self._prepare_messages(messages)
         kwargs: dict[str, Any] = {
             "model": model,
@@ -314,9 +265,8 @@ class OpenAIChatCompletionsProvider:
         tools = apply_tool_search(caps, tools, deferred_names)
         if tools:
             kwargs["tools"] = tools
-        extra_body = self._finalize_extra_body(extra_params, caps)
-        if extra_body:
-            kwargs["extra_body"] = extra_body
+        if extra_params:
+            kwargs["extra_body"] = extra_params
 
         log.debug(
             "openai.chat.request",

turnstone/core/providers/_openai_responses.py

@@ -223,10 +223,9 @@ class OpenAIResponsesProvider:
         temperature: float,
         reasoning_effort: str,
         deferred_names: frozenset[str] | None,
-        capabilities: ModelCapabilities | None = None,
     ) -> dict[str, Any]:
         """Build the kwargs dict for ``client.responses.create/stream``."""
-        caps = capabilities or self.get_capabilities(model)
+        caps = self.get_capabilities(model)
 
         instructions, input_items = self._convert_messages(messages)
         tools = apply_tool_search(caps, tools, deferred_names)
@@ -277,7 +276,6 @@ class OpenAIResponsesProvider:
         extra_params: dict[str, Any] | None = None,
         deferred_names: frozenset[str] | None = None,
         cancel_ref: list[Any] | None = None,
-        capabilities: ModelCapabilities | None = None,
     ) -> Iterator[StreamChunk]:
         if extra_params:
             log.debug("openai.responses: extra_params ignored (not supported by Responses API)")
@@ -289,7 +287,6 @@ class OpenAIResponsesProvider:
             temperature,
             reasoning_effort,
             deferred_names,
-            capabilities=capabilities,
         )
         kwargs["stream"] = True
 
@@ -458,7 +455,6 @@ class OpenAIResponsesProvider:
         reasoning_effort: str = "medium",
         extra_params: dict[str, Any] | None = None,
         deferred_names: frozenset[str] | None = None,
-        capabilities: ModelCapabilities | None = None,
     ) -> CompletionResult:
         if extra_params:
             log.debug("openai.responses: extra_params ignored (not supported by Responses API)")
@@ -470,7 +466,6 @@ class OpenAIResponsesProvider:
             temperature,
             reasoning_effort,
             deferred_names,
-            capabilities=capabilities,
         )
 
         log.debug(

turnstone/core/providers/_protocol.py

@@ -74,11 +74,6 @@ class ModelCapabilities:
     supports_tools: bool = True
     token_param: str = "max_completion_tokens"
     thinking_mode: str = "none"  # "none" | "manual" | "adaptive"
-    # For openai-compatible servers: the chat_template_kwargs key that
-    # toggles thinking (e.g. "enable_thinking" for Gemma/Qwen,
-    # "thinking" for Granite/DeepSeek).  Ignored when thinking_mode is
-    # "none" or by providers that handle thinking natively (Anthropic).
-    thinking_param: str = "enable_thinking"
     supports_effort: bool = False
     effort_levels: tuple[str, ...] = ()
     reasoning_effort_values: tuple[str, ...] = ()
@@ -87,6 +82,7 @@ class ModelCapabilities:
     supports_tool_search: bool = False
     supports_vision: bool = False
     supports_tool_advisories: bool = True
+    thinking_display: str = ""  # "summarized" for models that omit thinking by default
 
 
 def _lookup_capabilities(
@@ -132,16 +128,9 @@ class LLMProvider(Protocol):
         extra_params: dict[str, Any] | None = None,
         deferred_names: frozenset[str] | None = None,
         cancel_ref: list[Any] | None = None,
-        capabilities: ModelCapabilities | None = None,
     ) -> Iterator[StreamChunk]:
         """Create a streaming request, yielding normalized StreamChunks.
 
-        If *capabilities* is provided the provider uses it instead of
-        calling ``get_capabilities(model)`` internally.  This lets the
-        session pass config-merged capabilities so that overrides from
-        the model registry (e.g. ``thinking_mode``, ``token_param``)
-        are respected.
-
         If *cancel_ref* is provided the provider appends the underlying SDK
         stream object (which has a ``.close()`` method) before yielding the
         first chunk.  The caller can then close it from another thread to
@@ -161,7 +150,6 @@ class LLMProvider(Protocol):
         reasoning_effort: str = "medium",
         extra_params: dict[str, Any] | None = None,
         deferred_names: frozenset[str] | None = None,
-        capabilities: ModelCapabilities | None = None,
     ) -> CompletionResult:
         """Create a non-streaming request, returning a normalized result."""
         ...

turnstone/core/server_compat.py

@@ -1,207 +0,0 @@
-"""Server compatibility profiles for OpenAI-compatible backends.
-
-Different local model servers (vLLM, llama.cpp, SGLang) need different
-request shaping.  This module separates two concerns:
-
-1. **Model capabilities** — ``thinking_mode`` and ``thinking_param`` are
-   properties of the *model* (Gemma thinks, Llama doesn't).  These go
-   into the ``capabilities`` dict and flow through ``ModelCapabilities``
-   so the provider can act on them (just like Anthropic's thinking mode).
-
-2. **Server workarounds** — ``extra_body`` overrides like
-   ``skip_special_tokens=false`` are properties of the *server* (vLLM
-   bug workaround).  These stay in ``server_compat`` and get merged
-   into the request's ``extra_body`` at call time.
-
-Profiles are *suggestions* only.  The admin UI auto-fills them on
-Detect; the operator has final say, and the stored DB config is what
-actually gets used at request time.
-"""
-
-from __future__ import annotations
-
-import copy
-from typing import Any
-
-# ---------------------------------------------------------------------------
-# Profile suggestions
-# ---------------------------------------------------------------------------
-
-# Each profile has two optional parts:
-#   "capabilities" — merged into the model's capabilities dict (thinking_mode etc.)
-#   "server_compat" — stored as server_compat (extra_body workarounds)
-
-_PROFILES: dict[str, dict[str, Any]] = {
-    "vllm-gemma-thinking": {
-        "capabilities": {
-            "thinking_mode": "manual",
-            "thinking_param": "enable_thinking",
-        },
-        "server_compat": {
-            "server_type": "vllm",
-            # Workaround: vLLM strips special tokens before the Gemma4
-            # reasoning parser sees them.  skip_special_tokens=false
-            # preserves <|channel> / <channel|> markers so reasoning
-            # content is extracted correctly.
-            "extra_body": {"skip_special_tokens": False},
-        },
-    },
-    "vllm-qwen-thinking": {
-        "capabilities": {
-            "thinking_mode": "manual",
-            "thinking_param": "enable_thinking",
-        },
-        "server_compat": {
-            "server_type": "vllm",
-        },
-    },
-    "vllm-granite-thinking": {
-        "capabilities": {
-            "thinking_mode": "manual",
-            "thinking_param": "thinking",
-        },
-        "server_compat": {
-            "server_type": "vllm",
-        },
-    },
-    "vllm-deepseek-thinking": {
-        "capabilities": {
-            "thinking_mode": "manual",
-            "thinking_param": "thinking",
-        },
-        "server_compat": {
-            "server_type": "vllm",
-        },
-    },
-    "vllm-holo-thinking": {
-        "capabilities": {
-            "thinking_mode": "manual",
-            "thinking_param": "enable_thinking",
-        },
-        "server_compat": {
-            "server_type": "vllm",
-        },
-    },
-    "vllm": {
-        "server_compat": {
-            "server_type": "vllm",
-        },
-    },
-    "llama.cpp": {
-        "server_compat": {
-            "server_type": "llama.cpp",
-        },
-    },
-    "llama.cpp-thinking": {
-        "capabilities": {
-            "thinking_mode": "manual",
-            "thinking_param": "enable_thinking",
-        },
-        "server_compat": {
-            "server_type": "llama.cpp",
-            # llama.cpp uses reasoning_format (top-level request param) to
-            # extract thinking into the reasoning_content response field.
-            # "auto" lets the server decide based on the model's template;
-            # "deepseek" forces extraction for all thinking models.
-            "extra_body": {"reasoning_format": "auto"},
-        },
-    },
-    "sglang": {
-        "server_compat": {
-            "server_type": "sglang",
-        },
-    },
-}
-
-# Model-family → profile key mapping.  Checked in order; first match wins.
-_VLLM_MODEL_PROFILES: list[tuple[str, str]] = [
-    ("gemma-4", "vllm-gemma-thinking"),
-    ("gemma-3", "vllm-gemma-thinking"),
-    ("gemma4", "vllm-gemma-thinking"),
-    ("gemma3", "vllm-gemma-thinking"),
-    ("qwen3", "vllm-qwen-thinking"),
-    ("qwq", "vllm-qwen-thinking"),
-    ("granite-3", "vllm-granite-thinking"),
-    ("granite3", "vllm-granite-thinking"),
-    ("deepseek-r1", "vllm-deepseek-thinking"),
-    ("holo2", "vllm-holo-thinking"),
-]
-
-# llama.cpp model-family → profile key mapping.
-_LLAMA_CPP_MODEL_PROFILES: list[tuple[str, str]] = [
-    ("gemma-4", "llama.cpp-thinking"),
-    ("gemma-3", "llama.cpp-thinking"),
-    ("gemma4", "llama.cpp-thinking"),
-    ("gemma3", "llama.cpp-thinking"),
-    ("qwen3", "llama.cpp-thinking"),
-    ("qwq", "llama.cpp-thinking"),
-    ("deepseek-r1", "llama.cpp-thinking"),
-]
-
-
-# ---------------------------------------------------------------------------
-# Public API
-# ---------------------------------------------------------------------------
-
-
-def suggest_profile(server_type: str, model_id: str) -> dict[str, Any]:
-    """Suggest capabilities and server compat based on server type and model.
-
-    Returns a dict with optional ``"capabilities"`` and ``"server_compat"``
-    keys.  Empty dict when no special settings are needed.
-    """
-    profile_key: str | None = None
-    model_lower = (model_id or "").lower()
-    if server_type == "vllm":
-        for substring, key in _VLLM_MODEL_PROFILES:
-            if substring in model_lower:
-                profile_key = key
-                break
-        if profile_key is None:
-            profile_key = "vllm"
-    elif server_type == "llama.cpp":
-        for substring, key in _LLAMA_CPP_MODEL_PROFILES:
-            if substring in model_lower:
-                profile_key = key
-                break
-        if profile_key is None:
-            profile_key = "llama.cpp"
-    elif server_type in _PROFILES:
-        profile_key = server_type
-
-    if profile_key is None:
-        return {}
-    return copy.deepcopy(_PROFILES[profile_key])
-
-
-def merge_server_compat(
-    base_chat_template_kwargs: dict[str, Any],
-    server_compat: dict[str, Any],
-) -> dict[str, Any]:
-    """Build the ``extra_body`` dict by merging server compat into base kwargs.
-
-    *base_chat_template_kwargs* always contains at least ``reasoning_effort``.
-    *server_compat* comes from ``ModelConfig.server_compat``.
-
-    Note: thinking-mode params (``enable_thinking``, ``thinking``) are **not**
-    merged here — the provider handles those via ``ModelCapabilities``.
-    This function only merges server workarounds from ``extra_body``.
-
-    Returns the complete dict to pass as ``extra_body`` to the OpenAI client.
-    """
-    extra: dict[str, Any] = {"chat_template_kwargs": dict(base_chat_template_kwargs)}
-
-    # Merge top-level extra_body overrides (skip_special_tokens, etc.)
-    compat_eb = server_compat.get("extra_body")
-    if isinstance(compat_eb, dict):
-        for key, value in compat_eb.items():
-            if key == "chat_template_kwargs":
-                # Deep-merge: operator values in extra_body win over the
-                # base dict (which has reasoning_effort).  This lets
-                # operators intentionally extend chat_template_kwargs.
-                if isinstance(value, dict):
-                    extra["chat_template_kwargs"].update(value)
-                continue
-            extra[key] = value
-
-    return extra

turnstone/core/session.py

@@ -1448,50 +1448,21 @@ class ChatSession:
         self,
         reasoning_effort: str | None = None,
         provider: LLMProvider | None = None,
-        model_alias: str | None = None,
     ) -> dict[str, Any] | None:
         """Build provider-specific extra parameters.
 
         ``chat_template_kwargs`` is only meaningful for local model servers
         (``openai-compatible``).  Commercial OpenAI rejects it as an unknown
         parameter, and handles ``reasoning_effort`` natively.
-
-        Merges server workarounds (``skip_special_tokens``, etc.) from
-        ``ModelConfig.server_compat`` into the request's ``extra_body``.
-        Thinking-mode params (``enable_thinking``) are handled separately
-        by the provider based on ``ModelCapabilities.thinking_mode``.
-
-        *model_alias* controls which model config supplies server compat
-        settings.  When ``None``, defaults to the session's primary alias.
         """
-        from turnstone.core.server_compat import merge_server_compat
-
         prov = provider or self._provider
         if prov.provider_name == "openai-compatible":
-            ctk_base = dict(self._chat_template_kwargs_base)
+            kwargs = dict(self._chat_template_kwargs_base)
             if reasoning_effort:
-                ctk_base["reasoning_effort"] = reasoning_effort
-            return merge_server_compat(
-                ctk_base,
-                self._get_server_compat(model_alias),
-            )
+                kwargs["reasoning_effort"] = reasoning_effort
+            return {"chat_template_kwargs": kwargs}
         return None
 
-    def _get_server_compat(self, model_alias: str | None = None) -> dict[str, Any]:
-        """Get server compatibility settings from a model config.
-
-        *model_alias* selects the config to read.  Falls back to the
-        session's primary alias when ``None``.
-        """
-        alias = model_alias or self._model_alias
-        if self._registry and alias:
-            try:
-                cfg = self._registry.get_config(alias)
-                return dict(cfg.server_compat)
-            except (ValueError, KeyError):
-                pass
-        return {}
-
     def _utility_completion(
         self,
         messages: list[dict[str, Any]],
@@ -1517,7 +1488,6 @@ class ChatSession:
             temperature=temperature,
             reasoning_effort=reasoning_effort,
             extra_params=self._provider_extra_params(reasoning_effort=reasoning_effort),
-            capabilities=caps,
         )
 
     # -- tool search helpers --------------------------------------------------
@@ -1652,16 +1622,8 @@ class ChatSession:
         try:
             fb_client, fb_model, _ = self._registry.resolve(alias)
             fb_provider = self._registry.get_provider(alias)
-            fb_caps = self._resolve_capabilities(fb_provider, fb_model, alias)
             self.ui.on_info(f"[Primary model failed, falling back to {alias}]")
-            result = self._try_stream(
-                fb_client,
-                fb_model,
-                msgs,
-                provider=fb_provider,
-                capabilities=fb_caps,
-                model_alias=alias,
-            )
+            result = self._try_stream(fb_client, fb_model, msgs, provider=fb_provider)
             if fb_tracker:
                 fb_tracker.record_success()
             return result
@@ -1677,8 +1639,6 @@ class ChatSession:
         model: str,
         msgs: list[dict[str, Any]],
         provider: LLMProvider | None = None,
-        capabilities: ModelCapabilities | None = None,
-        model_alias: str | None = None,
     ) -> Iterator[StreamChunk]:
         """Attempt a streaming API call with retries on transient errors."""
         prov = provider or self._provider
@@ -1710,12 +1670,9 @@ class ChatSession:
                     max_tokens=self.max_tokens,
                     temperature=self.temperature,
                     reasoning_effort=self.reasoning_effort,
-                    extra_params=self._provider_extra_params(
-                        provider=prov, model_alias=model_alias
-                    ),
+                    extra_params=self._provider_extra_params(provider=prov),
                     deferred_names=self._get_deferred_names(),
                     cancel_ref=self._cancel_ref,
-                    capabilities=capabilities or self._get_capabilities(prov, model),
                 )
             except Exception as e:
                 ename = type(e).__name__
@@ -5424,12 +5381,10 @@ class ChatSession:
         if not agent_caps.supports_web_search and not self._resolve_search_client():
             tools = _without_tool(tools, "web_search")
 
-        # Build extra params for agent calls — resolve server compat from the
-        # agent's own model alias, not the session's primary model.
+        # Build extra params for agent calls
         agent_extra = self._provider_extra_params(
             reasoning_effort=reasoning_effort,
             provider=agent_provider,
-            model_alias=agent_alias,
         )
 
         def _api_call(
@@ -5448,7 +5403,6 @@ class ChatSession:
                         temperature=self.temperature,
                         reasoning_effort=reasoning_effort or self.reasoning_effort,
                         extra_params=agent_extra,
-                        capabilities=agent_caps,
                     )
                 except Exception as e:
                     ename = type(e).__name__

turnstone/ui/static/app.js

@@ -4504,7 +4504,7 @@ function _loadHls(callback) {
   if (_hlsState === "loading") return;
   _hlsState = "loading";
   var script = document.createElement("script");
-  script.src = "/shared/hls-1.6.16/hls.min.js";
+  script.src = "/shared/hls-1.6.15/hls.min.js";
   script.onload = function () {
     _hlsState = "ready";
     var q = _hlsQueue;

uv.lock

@@ -2506,7 +2506,7 @@ wheels = [
 
 [[package]]
 name = "turnstone"
-version = "1.4.0a2"
+version = "1.3.1"
 source = { editable = "." }
 dependencies = [
     { name = "alembic" },