release v2.6.0

The cutover's first real lesson ships: tool_choice "required" was a
workaround for a backend that ignored it, and became an unbreakable
tool loop on a backend that obeys it every request (~80 s arithmetic
turns, observed). The health check now learns which server answers
behind OLLAMA_HOST (/props is llama-server's own surface) and only
Ollama gets the advisory nudge. Probed unforced on llama-server: 3/3
tool calls via the --jinja template.

Also carries BACKEND_SLOT_PINNING (default off) for the P3 pilot.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

CHANGELOG.md

@@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [2.6.0] - 2026-09-11
+
 ### Added
 
 - `BACKEND_SLOT_PINNING` (default off): pins each pipeline phase to a
@@ -14,6 +16,14 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   phase's stable prompt prefix stays in that slot's KV cache. No-op on
   Claude; ignored by Ollama.
 
+### Fixed
+
+- Tool-calling no longer loops on llama-server backends: the health
+  check detects the server flavor, and `tool_choice: "required"` (an
+  advisory nudge on Ollama, an every-request mandate on llama-server)
+  is sent only to Ollama. On llama-server the model calls tools
+  unforced via its chat template.
+
 ## [2.5.0] - 2026-09-11
 
 ### Added

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "tatlock"
-version = "2.5.0"
+version = "2.6.0"
 description = "OpenAI-compatible API with Ollama backend"
 requires-python = ">=3.12"
 dependencies = [

src/anthropic/model_selector.py

@@ -28,6 +28,11 @@ logger = get_logger(__name__)
 # Cached health check results (set once at startup)
 _claude_available: bool | None = None
 _ollama_available: bool | None = None
+# Which server answers behind OLLAMA_HOST: "llama-server" or "ollama".
+# They disagree on tool_choice semantics (see get_tool_choice_settings),
+# so the health check probes /props — served by llama-server only — and
+# caches the answer here.
+_local_flavor: str | None = None
 
 
 async def check_ollama_health() -> bool:
@@ -40,7 +45,7 @@ async def check_ollama_health() -> bool:
     Returns:
         True if Ollama is reachable and OLLAMA_DEFAULT_MODEL is pulled.
     """
-    global _ollama_available
+    global _ollama_available, _local_flavor
 
     host = str(config.OLLAMA_HOST).rstrip("/")
     model = config.OLLAMA_DEFAULT_MODEL
@@ -53,12 +58,20 @@ async def check_ollama_health() -> bool:
             response.raise_for_status()
             names = [m.get("id", "") for m in response.json().get("data", [])]
 
+            # /props is llama-server's own surface; Ollama 404s it.
+            try:
+                props = await client.get(f"{host}/props")
+                _local_flavor = "llama-server" if props.status_code == 200 else "ollama"
+            except httpx.HTTPError:
+                _local_flavor = "ollama"
+
         if model in names or f"{model}:latest" in names:
             _ollama_available = True
             logger.info(
                 "ollama_health_check_passed",
                 host=host,
                 model=model,
+                flavor=_local_flavor,
             )
             return True
 
@@ -244,19 +257,30 @@ def get_model(prefer_cloud: bool | None = None) -> AnthropicModel | OpenAIChatMo
 
 def get_tool_choice_settings() -> ModelSettings:
     """
-    Get model_settings for forcing tool calls on the first request.
+    Get model_settings for tool calling on the orchestration phase.
 
-    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.
+    Claude: PydanticAI handles tool_choice natively — no extra_body.
+
+    Ollama: tool_choice="required" via extra_body. Advisory there (Ollama
+    ignores it), but it nudges gemma4 to actually call tools, which the
+    persona-suppression gotcha made necessary.
+
+    llama-server: NO tool_choice. It enforces "required" on every request
+    in the run, so after a tool returns, the next generation is again
+    forced to call a tool — an unbreakable tool loop (~80 s turns,
+    observed at cutover). Its --jinja template renders tool definitions
+    the way gemma4 was trained, and the model calls tools reliably
+    unforced (probed 3/3).
     """
     from pydantic_ai.settings import ModelSettings
 
     if resolve_backend() == "claude":
         # 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"})
+    if _local_flavor == "llama-server":
+        return ModelSettings()
+    # Ollama needs explicit tool_choice via extra_body
+    return ModelSettings(extra_body={"tool_choice": "required"})
 
 
 def with_slot_pinning(settings: ModelSettings | None, slot: int) -> ModelSettings | None:

tests/anthropic/test_model_selector.py

@@ -120,3 +120,20 @@ class TestWithSlotPinning:
         monkeypatch.setattr(config, "PREFER_CLOUD_BACKEND", True)
         base = model_selector.get_tool_choice_settings()
         assert model_selector.with_slot_pinning(base, slot=1) is base
+
+
+class TestLocalFlavorToolChoice:
+    def test_llama_server_flavor_sends_no_tool_choice(self, local_first, monkeypatch):
+        monkeypatch.setattr(model_selector, "_local_flavor", "llama-server")
+        settings = model_selector.get_tool_choice_settings()
+        assert not settings.get("extra_body")
+
+    def test_ollama_flavor_keeps_advisory_required(self, local_first, monkeypatch):
+        monkeypatch.setattr(model_selector, "_local_flavor", "ollama")
+        settings = model_selector.get_tool_choice_settings()
+        assert settings["extra_body"] == {"tool_choice": "required"}
+
+    def test_unknown_flavor_defaults_to_ollama_semantics(self, local_first, monkeypatch):
+        monkeypatch.setattr(model_selector, "_local_flavor", None)
+        settings = model_selector.get_tool_choice_settings()
+        assert settings["extra_body"] == {"tool_choice": "required"}

tests/contracts/test_service_contracts.py

@@ -114,16 +114,18 @@ class TestLocalBackendContract:
         )
 
     async def test_openai_compat_tool_calling(self):
-        # Mirrors the request PydanticAI's OpenAIChatModel sends for the
-        # orchestration phase, including the extra_body tool_choice.
+        # Mirrors the orchestration-phase request on llama-server: tools
+        # attached, NO tool_choice. The backend must call the tool unforced
+        # — "required" is deliberately absent because llama-server enforces
+        # it on every request in a run, which turns the tool loop
+        # unbreakable (observed at cutover: ~80 s turns).
         response = await _post_or_skip(
             f"{OLLAMA}/v1/chat/completions",
-            "ollama",
+            "local-backend",
             {
                 "model": config.OLLAMA_DEFAULT_MODEL,
                 "messages": [{"role": "user", "content": "What is 6 * 7? Use the calculator."}],
                 "tools": [CALCULATOR_TOOL],
-                "tool_choice": "required",
                 "stream": False,
             },
             timeout=config.OLLAMA_TIMEOUT,