feat: add Anthropic transport abstraction slice (#951)

- add transport registry, shared transport dataclasses, and AnthropicTransport
- add normalize_anthropic_response_v2 as the bridge from existing Anthropic normalization to shared transport types
- extend Anthropic stop-reason mapping for refusal and model_context_window_exceeded
- add targeted transport and v2 normalization regression tests

Closes #951
Refs #949

agent/anthropic_adapter.py

@@ -1396,6 +1396,8 @@ def normalize_anthropic_response(
         "tool_use": "tool_calls",
         "max_tokens": "length",
         "stop_sequence": "stop",
+        "refusal": "content_filter",
+        "model_context_window_exceeded": "length",
     }
     finish_reason = stop_reason_map.get(response.stop_reason, "stop")
 
@@ -1409,3 +1411,42 @@ def normalize_anthropic_response(
         ),
         finish_reason,
     )
+
+
+def normalize_anthropic_response_v2(
+    response,
+    strip_tool_prefix: bool = False,
+) -> "NormalizedResponse":
+    """Normalize Anthropic response to NormalizedResponse.
+
+    Wraps the existing normalize_anthropic_response() and maps its output
+    to the shared transport types. This allows incremental migration
+    without disturbing the legacy call sites.
+    """
+    from agent.transports.types import NormalizedResponse, build_tool_call
+
+    assistant_msg, finish_reason = normalize_anthropic_response(response, strip_tool_prefix)
+
+    tool_calls = None
+    if assistant_msg.tool_calls:
+        tool_calls = [
+            build_tool_call(
+                id=tc.id,
+                name=tc.function.name,
+                arguments=tc.function.arguments,
+            )
+            for tc in assistant_msg.tool_calls
+        ]
+
+    provider_data = {}
+    if getattr(assistant_msg, "reasoning_details", None):
+        provider_data["reasoning_details"] = assistant_msg.reasoning_details
+
+    return NormalizedResponse(
+        content=assistant_msg.content,
+        tool_calls=tool_calls,
+        finish_reason=finish_reason,
+        reasoning=getattr(assistant_msg, "reasoning", None),
+        usage=None,
+        provider_data=provider_data or None,
+    )

agent/transports/__init__.py

@@ -0,0 +1,57 @@
+"""Transport layer types and registry for provider response normalization.
+
+Usage:
+    from agent.transports import get_transport
+    transport = get_transport("anthropic_messages")
+    result = transport.normalize_response(raw_response)
+"""
+
+from agent.transports.types import (  # noqa: F401
+    NormalizedResponse,
+    ToolCall,
+    Usage,
+    build_tool_call,
+    map_finish_reason,
+)
+
+_REGISTRY: dict = {}
+
+
+def register_transport(api_mode: str, transport_cls: type) -> None:
+    """Register a transport class for an api_mode string."""
+    _REGISTRY[api_mode] = transport_cls
+
+
+def get_transport(api_mode: str):
+    """Get a transport instance for the given api_mode.
+
+    Returns None if no transport is registered for this api_mode.
+    This allows gradual migration — call sites can check for None
+    and fall back to the legacy code path.
+    """
+    if not _REGISTRY:
+        _discover_transports()
+    cls = _REGISTRY.get(api_mode)
+    if cls is None:
+        return None
+    return cls()
+
+
+def _discover_transports() -> None:
+    """Import all transport modules to trigger auto-registration."""
+    try:
+        import agent.transports.anthropic  # noqa: F401
+    except ImportError:
+        pass
+    try:
+        import agent.transports.codex  # noqa: F401
+    except ImportError:
+        pass
+    try:
+        import agent.transports.chat_completions  # noqa: F401
+    except ImportError:
+        pass
+    try:
+        import agent.transports.bedrock  # noqa: F401
+    except ImportError:
+        pass

agent/transports/anthropic.py

@@ -0,0 +1,95 @@
+"""Anthropic Messages API transport.
+
+Delegates to the existing adapter functions in agent/anthropic_adapter.py.
+This transport owns format conversion and normalization — NOT client lifecycle.
+"""
+
+from typing import Any, Dict, List, Optional
+
+from agent.transports.base import ProviderTransport
+from agent.transports.types import NormalizedResponse
+
+
+class AnthropicTransport(ProviderTransport):
+    """Transport for api_mode='anthropic_messages'."""
+
+    @property
+    def api_mode(self) -> str:
+        return "anthropic_messages"
+
+    def convert_messages(self, messages: List[Dict[str, Any]], **kwargs) -> Any:
+        from agent.anthropic_adapter import convert_messages_to_anthropic
+
+        base_url = kwargs.get("base_url")
+        return convert_messages_to_anthropic(messages, base_url=base_url)
+
+    def convert_tools(self, tools: List[Dict[str, Any]]) -> Any:
+        from agent.anthropic_adapter import convert_tools_to_anthropic
+
+        return convert_tools_to_anthropic(tools)
+
+    def build_kwargs(
+        self,
+        model: str,
+        messages: List[Dict[str, Any]],
+        tools: Optional[List[Dict[str, Any]]] = None,
+        **params,
+    ) -> Dict[str, Any]:
+        from agent.anthropic_adapter import build_anthropic_kwargs
+
+        return build_anthropic_kwargs(
+            model=model,
+            messages=messages,
+            tools=tools,
+            max_tokens=params.get("max_tokens", 16384),
+            reasoning_config=params.get("reasoning_config"),
+            tool_choice=params.get("tool_choice"),
+            is_oauth=params.get("is_oauth", False),
+            preserve_dots=params.get("preserve_dots", False),
+            context_length=params.get("context_length"),
+            base_url=params.get("base_url"),
+            fast_mode=params.get("fast_mode", False),
+        )
+
+    def normalize_response(self, response: Any, **kwargs) -> NormalizedResponse:
+        from agent.anthropic_adapter import normalize_anthropic_response_v2
+
+        strip_tool_prefix = kwargs.get("strip_tool_prefix", False)
+        return normalize_anthropic_response_v2(response, strip_tool_prefix=strip_tool_prefix)
+
+    def validate_response(self, response: Any) -> bool:
+        if response is None:
+            return False
+        content_blocks = getattr(response, "content", None)
+        if not isinstance(content_blocks, list):
+            return False
+        if not content_blocks:
+            return False
+        return True
+
+    def extract_cache_stats(self, response: Any):
+        usage = getattr(response, "usage", None)
+        if usage is None:
+            return None
+        cached = getattr(usage, "cache_read_input_tokens", 0) or 0
+        written = getattr(usage, "cache_creation_input_tokens", 0) or 0
+        if cached or written:
+            return {"cached_tokens": cached, "creation_tokens": written}
+        return None
+
+    _STOP_REASON_MAP = {
+        "end_turn": "stop",
+        "tool_use": "tool_calls",
+        "max_tokens": "length",
+        "stop_sequence": "stop",
+        "refusal": "content_filter",
+        "model_context_window_exceeded": "length",
+    }
+
+    def map_finish_reason(self, raw_reason: str) -> str:
+        return self._STOP_REASON_MAP.get(raw_reason, "stop")
+
+
+from agent.transports import register_transport  # noqa: E402
+
+register_transport("anthropic_messages", AnthropicTransport)

agent/transports/base.py

@@ -0,0 +1,61 @@
+"""Abstract base for provider transports.
+
+A transport owns the data path for one api_mode:
+  convert_messages → convert_tools → build_kwargs → normalize_response
+
+It does NOT own: client construction, streaming, credential refresh,
+prompt caching, interrupt handling, or retry logic. Those stay on AIAgent.
+"""
+
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional
+
+from agent.transports.types import NormalizedResponse
+
+
+class ProviderTransport(ABC):
+    """Base class for provider-specific format conversion and normalization."""
+
+    @property
+    @abstractmethod
+    def api_mode(self) -> str:
+        """The api_mode string this transport handles."""
+        ...
+
+    @abstractmethod
+    def convert_messages(self, messages: List[Dict[str, Any]], **kwargs) -> Any:
+        """Convert OpenAI-format messages to provider-native format."""
+        ...
+
+    @abstractmethod
+    def convert_tools(self, tools: List[Dict[str, Any]]) -> Any:
+        """Convert OpenAI-format tool definitions to provider-native format."""
+        ...
+
+    @abstractmethod
+    def build_kwargs(
+        self,
+        model: str,
+        messages: List[Dict[str, Any]],
+        tools: Optional[List[Dict[str, Any]]] = None,
+        **params,
+    ) -> Dict[str, Any]:
+        """Build the complete provider kwargs dict."""
+        ...
+
+    @abstractmethod
+    def normalize_response(self, response: Any, **kwargs) -> NormalizedResponse:
+        """Normalize a raw provider response to the shared NormalizedResponse type."""
+        ...
+
+    def validate_response(self, response: Any) -> bool:
+        """Optional structural validation for raw responses."""
+        return True
+
+    def extract_cache_stats(self, response: Any) -> Optional[Dict[str, int]]:
+        """Optional cache stats extraction."""
+        return None
+
+    def map_finish_reason(self, raw_reason: str) -> str:
+        """Optional stop-reason mapping. Defaults to passthrough."""
+        return raw_reason

agent/transports/types.py

@@ -0,0 +1,58 @@
+"""Shared types for normalized provider responses."""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+
+@dataclass
+class ToolCall:
+    """A normalized tool call from any provider."""
+
+    id: Optional[str]
+    name: str
+    arguments: str
+    provider_data: Optional[Dict[str, Any]] = field(default=None, repr=False)
+
+
+@dataclass
+class Usage:
+    """Token usage from an API response."""
+
+    prompt_tokens: int = 0
+    completion_tokens: int = 0
+    total_tokens: int = 0
+    cached_tokens: int = 0
+
+
+@dataclass
+class NormalizedResponse:
+    """Normalized API response from any provider."""
+
+    content: Optional[str]
+    tool_calls: Optional[List[ToolCall]]
+    finish_reason: str
+    reasoning: Optional[str] = None
+    usage: Optional[Usage] = None
+    provider_data: Optional[Dict[str, Any]] = field(default=None, repr=False)
+
+
+def build_tool_call(
+    id: Optional[str],
+    name: str,
+    arguments: Any,
+    **provider_fields: Any,
+) -> ToolCall:
+    """Build a ToolCall, auto-serialising dict arguments."""
+    args_str = json.dumps(arguments) if isinstance(arguments, dict) else str(arguments)
+    provider_data = dict(provider_fields) if provider_fields else None
+    return ToolCall(id=id, name=name, arguments=args_str, provider_data=provider_data)
+
+
+def map_finish_reason(reason: Optional[str], mapping: Dict[str, str]) -> str:
+    """Translate a provider-specific stop reason to the normalized set."""
+    if reason is None:
+        return "stop"
+    return mapping.get(reason, "stop")

hermes_cli/config.py

@@ -523,7 +523,7 @@ DEFAULT_CONFIG = {
     
     # Text-to-speech configuration
     "tts": {
-        "provider": "edge",  # "edge" (free) | "elevenlabs" (premium) | "openai" | "minimax" | "mistral" | "neutts" (local) | "kittentts" (local)
+        "provider": "edge",  # "edge" (free) | "elevenlabs" (premium) | "openai" | "minimax" | "mistral" | "neutts" (local)
         "edge": {
             "voice": "en-US-AriaNeural",
             # Popular: AriaNeural, JennyNeural, AndrewNeural, BrianNeural, SoniaNeural
@@ -547,12 +547,6 @@ DEFAULT_CONFIG = {
             "model": "neuphonic/neutts-air-q4-gguf",  # HuggingFace model repo
             "device": "cpu",  # cpu, cuda, or mps
         },
-        "kittentts": {
-            "model": "KittenML/kitten-tts-nano-0.8-int8",  # 25MB int8 default
-            "voice": "Jasper",  # Jasper, Bella, Luna, Bruno, Rosie, Hugo, Kiki, Leo
-            "speed": 1.0,
-            "clean_text": True,
-        },
     },
     
     "stt": {

hermes_cli/setup.py

@@ -443,16 +443,6 @@ def _print_setup_summary(config: dict, hermes_home):
             tool_status.append(("Text-to-Speech (NeuTTS local)", True, None))
         else:
             tool_status.append(("Text-to-Speech (NeuTTS — not installed)", False, "run 'hermes setup tts'"))
-    elif tts_provider == "kittentts":
-        try:
-            import importlib.util
-            kittentts_ok = importlib.util.find_spec("kittentts") is not None
-        except Exception:
-            kittentts_ok = False
-        if kittentts_ok:
-            tool_status.append(("Text-to-Speech (KittenTTS local)", True, None))
-        else:
-            tool_status.append(("Text-to-Speech (KittenTTS — not installed)", False, "run 'hermes setup tts'"))
     else:
         tool_status.append(("Text-to-Speech (Edge TTS)", True, None))
 
@@ -901,7 +891,6 @@ def _install_neutts_deps() -> bool:
                 return False
         else:
             print_warning("espeak-ng is required for NeuTTS. Install it manually before using NeuTTS.")
-            return False
 
     # Install neutts Python package
     print()
@@ -921,34 +910,8 @@ def _install_neutts_deps() -> bool:
         return False
 
 
-def _install_kittentts_deps() -> bool:
-    """Install KittenTTS dependencies with user approval. Returns True on success."""
-    import subprocess
-    import sys
-
-    wheel_url = (
-        "https://github.com/KittenML/KittenTTS/releases/download/"
-        "0.8.1/kittentts-0.8.1-py3-none-any.whl"
-    )
-    print()
-    print_info("Installing kittentts Python package (~25-80MB model downloaded on first use)...")
-    print()
-    try:
-        subprocess.run(
-            [sys.executable, "-m", "pip", "install", "-U", wheel_url, "soundfile", "--quiet"],
-            check=True, timeout=300,
-        )
-        print_success("kittentts installed successfully")
-        return True
-    except (subprocess.CalledProcessError, subprocess.TimeoutExpired) as e:
-        print_error(f"Failed to install kittentts: {e}")
-        print_info(f"Try manually: python -m pip install -U '{wheel_url}' soundfile")
-        return False
-
-
 def _setup_tts_provider(config: dict):
-    """Interactive TTS provider selection with install flow for local providers."""
-
+    """Interactive TTS provider selection with install flow for NeuTTS."""
     tts_config = config.get("tts", {})
     current_provider = tts_config.get("provider", "edge")
     subscription_features = get_nous_subscription_features(config)
@@ -960,7 +923,6 @@ def _setup_tts_provider(config: dict):
         "minimax": "MiniMax TTS",
         "mistral": "Mistral Voxtral TTS",
         "neutts": "NeuTTS",
-        "kittentts": "KittenTTS",
     }
     current_label = provider_labels.get(current_provider, current_provider)
 
@@ -982,10 +944,9 @@ def _setup_tts_provider(config: dict):
             "MiniMax TTS (high quality with voice cloning, needs API key)",
             "Mistral Voxtral TTS (multilingual, native Opus, needs API key)",
             "NeuTTS (local on-device, free, ~300MB model download)",
-            "KittenTTS (local on-device, free, lightweight ~25-80MB ONNX)",
         ]
     )
-    providers.extend(["edge", "elevenlabs", "openai", "minimax", "mistral", "neutts", "kittentts"])
+    providers.extend(["edge", "elevenlabs", "openai", "minimax", "mistral", "neutts"])
     choices.append(f"Keep current ({current_label})")
     keep_current_idx = len(choices) - 1
     idx = prompt_choice("Select TTS provider:", choices, keep_current_idx)
@@ -1027,28 +988,6 @@ def _setup_tts_provider(config: dict):
                 print_info("Skipping install. Set tts.provider to 'neutts' after installing manually.")
                 selected = "edge"
 
-    elif selected == "kittentts":
-        try:
-            import importlib.util
-            already_installed = importlib.util.find_spec("kittentts") is not None
-        except Exception:
-            already_installed = False
-
-        if already_installed:
-            print_success("KittenTTS is already installed")
-        else:
-            print()
-            print_info("KittenTTS is lightweight (~25-80MB, CPU-only, no API key required).")
-            print_info("Voices: Jasper, Bella, Luna, Bruno, Rosie, Hugo, Kiki, Leo")
-            print()
-            if prompt_yes_no("Install KittenTTS now?", True):
-                if not _install_kittentts_deps():
-                    print_warning("KittenTTS installation incomplete. Falling back to Edge TTS.")
-                    selected = "edge"
-            else:
-                print_info("Skipping install. Set tts.provider to 'kittentts' after installing manually.")
-                selected = "edge"
-
     elif selected == "elevenlabs":
         existing = get_env_value("ELEVENLABS_API_KEY")
         if not existing:

hermes_cli/tools_config.py

@@ -164,14 +164,6 @@ TOOL_CATEGORIES = {
                 ],
                 "tts_provider": "mistral",
             },
-            {
-                "name": "KittenTTS",
-                "badge": "local · free",
-                "tag": "Lightweight local ONNX TTS (~25MB), no API key",
-                "env_vars": [],
-                "tts_provider": "kittentts",
-                "post_setup": "kittentts",
-            },
         ],
     },
     "web": {
@@ -411,36 +403,6 @@ def _run_post_setup(post_setup_key: str):
             _print_warning("    Node.js not found. Install Camofox via Docker:")
             _print_info("      docker run -p 9377:9377 -e CAMOFOX_PORT=9377 jo-inc/camofox-browser")
 
-    elif post_setup_key == "kittentts":
-        try:
-            __import__("kittentts")
-            _print_success("    kittentts is already installed")
-            return
-        except ImportError:
-            pass
-        import subprocess
-        _print_info("    Installing kittentts (~25-80MB model, CPU-only)...")
-        wheel_url = (
-            "https://github.com/KittenML/KittenTTS/releases/download/"
-            "0.8.1/kittentts-0.8.1-py3-none-any.whl"
-        )
-        try:
-            result = subprocess.run(
-                [sys.executable, "-m", "pip", "install", "-U", wheel_url, "soundfile", "--quiet"],
-                capture_output=True, text=True, timeout=300,
-            )
-            if result.returncode == 0:
-                _print_success("    kittentts installed")
-                _print_info("    Voices: Jasper, Bella, Luna, Bruno, Rosie, Hugo, Kiki, Leo")
-                _print_info("    Models: KittenML/kitten-tts-nano-0.8-int8 (25MB), micro (41MB), mini (80MB)")
-            else:
-                _print_warning("    kittentts install failed:")
-                _print_info(f"      {result.stderr.strip()[:300]}")
-                _print_info(f"    Run manually: python -m pip install -U '{wheel_url}' soundfile")
-        except subprocess.TimeoutExpired:
-            _print_warning("    kittentts install timed out (>5min)")
-            _print_info(f"    Run manually: python -m pip install -U '{wheel_url}' soundfile")
-
     elif post_setup_key == "rl_training":
         try:
             __import__("tinker_atropos")

tests/agent/test_anthropic_normalize_v2.py

@@ -0,0 +1,213 @@
+"""Regression tests: normalize_anthropic_response_v2 vs v1.
+
+Constructs mock Anthropic responses and asserts that the v2 function
+(returning NormalizedResponse) produces identical field values to the
+original v1 function (returning SimpleNamespace + finish_reason).
+"""
+
+from types import SimpleNamespace
+
+import pytest
+
+from agent.anthropic_adapter import (
+    normalize_anthropic_response,
+    normalize_anthropic_response_v2,
+)
+from agent.transports.types import NormalizedResponse
+
+
+def _text_block(text: str):
+    return SimpleNamespace(type="text", text=text)
+
+
+def _thinking_block(thinking: str, signature: str = "sig_abc"):
+    return SimpleNamespace(type="thinking", thinking=thinking, signature=signature)
+
+
+def _tool_use_block(id: str, name: str, input: dict):
+    return SimpleNamespace(type="tool_use", id=id, name=name, input=input)
+
+
+def _response(content_blocks, stop_reason="end_turn"):
+    return SimpleNamespace(
+        content=content_blocks,
+        stop_reason=stop_reason,
+        usage=SimpleNamespace(input_tokens=10, output_tokens=5),
+    )
+
+
+class TestTextOnly:
+    def setup_method(self):
+        self.resp = _response([_text_block("Hello world")])
+        self.v1_msg, self.v1_finish = normalize_anthropic_response(self.resp)
+        self.v2 = normalize_anthropic_response_v2(self.resp)
+
+    def test_type(self):
+        assert isinstance(self.v2, NormalizedResponse)
+
+    def test_content_matches(self):
+        assert self.v2.content == self.v1_msg.content
+
+    def test_finish_reason_matches(self):
+        assert self.v2.finish_reason == self.v1_finish
+
+    def test_no_tool_calls(self):
+        assert self.v2.tool_calls is None
+        assert self.v1_msg.tool_calls is None
+
+    def test_no_reasoning(self):
+        assert self.v2.reasoning is None
+        assert self.v1_msg.reasoning is None
+
+
+class TestWithToolCalls:
+    def setup_method(self):
+        self.resp = _response(
+            [
+                _text_block("I'll check that"),
+                _tool_use_block("toolu_abc", "terminal", {"command": "ls"}),
+                _tool_use_block("toolu_def", "read_file", {"path": "/tmp"}),
+            ],
+            stop_reason="tool_use",
+        )
+        self.v1_msg, self.v1_finish = normalize_anthropic_response(self.resp)
+        self.v2 = normalize_anthropic_response_v2(self.resp)
+
+    def test_finish_reason(self):
+        assert self.v2.finish_reason == "tool_calls"
+        assert self.v1_finish == "tool_calls"
+
+    def test_tool_call_count(self):
+        assert len(self.v2.tool_calls) == 2
+        assert len(self.v1_msg.tool_calls) == 2
+
+    def test_tool_call_ids_match(self):
+        for i in range(2):
+            assert self.v2.tool_calls[i].id == self.v1_msg.tool_calls[i].id
+
+    def test_tool_call_names_match(self):
+        assert self.v2.tool_calls[0].name == "terminal"
+        assert self.v2.tool_calls[1].name == "read_file"
+        for i in range(2):
+            assert self.v2.tool_calls[i].name == self.v1_msg.tool_calls[i].function.name
+
+    def test_tool_call_arguments_match(self):
+        for i in range(2):
+            assert self.v2.tool_calls[i].arguments == self.v1_msg.tool_calls[i].function.arguments
+
+    def test_content_preserved(self):
+        assert self.v2.content == self.v1_msg.content
+        assert "check that" in self.v2.content
+
+
+class TestWithThinking:
+    def setup_method(self):
+        self.resp = _response([
+            _thinking_block("Let me think about this carefully..."),
+            _text_block("The answer is 42."),
+        ])
+        self.v1_msg, self.v1_finish = normalize_anthropic_response(self.resp)
+        self.v2 = normalize_anthropic_response_v2(self.resp)
+
+    def test_reasoning_matches(self):
+        assert self.v2.reasoning == self.v1_msg.reasoning
+        assert "think about this" in self.v2.reasoning
+
+    def test_reasoning_details_in_provider_data(self):
+        v1_details = self.v1_msg.reasoning_details
+        v2_details = self.v2.provider_data.get("reasoning_details") if self.v2.provider_data else None
+        assert v1_details is not None
+        assert v2_details is not None
+        assert len(v2_details) == len(v1_details)
+
+    def test_content_excludes_thinking(self):
+        assert self.v2.content == "The answer is 42."
+
+
+class TestMixed:
+    def setup_method(self):
+        self.resp = _response(
+            [
+                _thinking_block("Planning my approach..."),
+                _text_block("I'll run the command"),
+                _tool_use_block("toolu_xyz", "terminal", {"command": "pwd"}),
+            ],
+            stop_reason="tool_use",
+        )
+        self.v1_msg, self.v1_finish = normalize_anthropic_response(self.resp)
+        self.v2 = normalize_anthropic_response_v2(self.resp)
+
+    def test_all_fields_present(self):
+        assert self.v2.content is not None
+        assert self.v2.tool_calls is not None
+        assert self.v2.reasoning is not None
+        assert self.v2.finish_reason == "tool_calls"
+
+    def test_content_matches(self):
+        assert self.v2.content == self.v1_msg.content
+
+    def test_reasoning_matches(self):
+        assert self.v2.reasoning == self.v1_msg.reasoning
+
+    def test_tool_call_matches(self):
+        assert self.v2.tool_calls[0].id == self.v1_msg.tool_calls[0].id
+        assert self.v2.tool_calls[0].name == self.v1_msg.tool_calls[0].function.name
+
+
+class TestStopReasons:
+    @pytest.mark.parametrize("stop_reason,expected", [
+        ("end_turn", "stop"),
+        ("tool_use", "tool_calls"),
+        ("max_tokens", "length"),
+        ("stop_sequence", "stop"),
+        ("refusal", "content_filter"),
+        ("model_context_window_exceeded", "length"),
+        ("unknown_future_reason", "stop"),
+    ])
+    def test_stop_reason_mapping(self, stop_reason, expected):
+        resp = _response([_text_block("x")], stop_reason=stop_reason)
+        _v1_msg, v1_finish = normalize_anthropic_response(resp)
+        v2 = normalize_anthropic_response_v2(resp)
+        assert v2.finish_reason == v1_finish == expected
+
+
+class TestStripToolPrefix:
+    def test_prefix_stripped(self):
+        resp = _response(
+            [_tool_use_block("toolu_1", "mcp_terminal", {"cmd": "ls"})],
+            stop_reason="tool_use",
+        )
+        v1_msg, _ = normalize_anthropic_response(resp, strip_tool_prefix=True)
+        v2 = normalize_anthropic_response_v2(resp, strip_tool_prefix=True)
+        assert v1_msg.tool_calls[0].function.name == "terminal"
+        assert v2.tool_calls[0].name == "terminal"
+
+    def test_prefix_kept(self):
+        resp = _response(
+            [_tool_use_block("toolu_1", "mcp_terminal", {"cmd": "ls"})],
+            stop_reason="tool_use",
+        )
+        v1_msg, _ = normalize_anthropic_response(resp, strip_tool_prefix=False)
+        v2 = normalize_anthropic_response_v2(resp, strip_tool_prefix=False)
+        assert v1_msg.tool_calls[0].function.name == "mcp_terminal"
+        assert v2.tool_calls[0].name == "mcp_terminal"
+
+
+class TestEdgeCases:
+    def test_empty_content_blocks(self):
+        resp = _response([])
+        v1_msg, _v1_finish = normalize_anthropic_response(resp)
+        v2 = normalize_anthropic_response_v2(resp)
+        assert v2.content == v1_msg.content
+        assert v2.content is None
+
+    def test_no_reasoning_details_means_none_provider_data(self):
+        resp = _response([_text_block("hi")])
+        v2 = normalize_anthropic_response_v2(resp)
+        assert v2.provider_data is None
+
+    def test_v2_returns_dataclass_not_namespace(self):
+        resp = _response([_text_block("hi")])
+        v2 = normalize_anthropic_response_v2(resp)
+        assert isinstance(v2, NormalizedResponse)
+        assert not isinstance(v2, SimpleNamespace)

tests/agent/transports/test_transport.py

@@ -0,0 +1,208 @@
+"""Tests for the transport ABC, registry, and AnthropicTransport."""
+
+from types import SimpleNamespace
+
+import pytest
+
+from agent.transports import _REGISTRY, get_transport, register_transport
+from agent.transports.base import ProviderTransport
+from agent.transports.types import NormalizedResponse
+
+
+class TestProviderTransportABC:
+    def test_cannot_instantiate_abc(self):
+        with pytest.raises(TypeError):
+            ProviderTransport()
+
+    def test_concrete_must_implement_all_abstract(self):
+        class Incomplete(ProviderTransport):
+            @property
+            def api_mode(self):
+                return "test"
+
+        with pytest.raises(TypeError):
+            Incomplete()
+
+    def test_minimal_concrete(self):
+        class Minimal(ProviderTransport):
+            @property
+            def api_mode(self):
+                return "test_minimal"
+
+            def convert_messages(self, messages, **kw):
+                return messages
+
+            def convert_tools(self, tools):
+                return tools
+
+            def build_kwargs(self, model, messages, tools=None, **params):
+                return {"model": model, "messages": messages}
+
+            def normalize_response(self, response, **kw):
+                return NormalizedResponse(content="ok", tool_calls=None, finish_reason="stop")
+
+        t = Minimal()
+        assert t.api_mode == "test_minimal"
+        assert t.validate_response(None) is True
+        assert t.extract_cache_stats(None) is None
+        assert t.map_finish_reason("end_turn") == "end_turn"
+
+
+class TestTransportRegistry:
+    def test_get_unregistered_returns_none(self):
+        assert get_transport("nonexistent_mode") is None
+
+    def test_anthropic_registered_on_import(self):
+        import agent.transports.anthropic  # noqa: F401
+
+        t = get_transport("anthropic_messages")
+        assert t is not None
+        assert t.api_mode == "anthropic_messages"
+
+    def test_register_and_get(self):
+        class DummyTransport(ProviderTransport):
+            @property
+            def api_mode(self):
+                return "dummy_test"
+
+            def convert_messages(self, messages, **kw):
+                return messages
+
+            def convert_tools(self, tools):
+                return tools
+
+            def build_kwargs(self, model, messages, tools=None, **params):
+                return {}
+
+            def normalize_response(self, response, **kw):
+                return NormalizedResponse(content=None, tool_calls=None, finish_reason="stop")
+
+        register_transport("dummy_test", DummyTransport)
+        t = get_transport("dummy_test")
+        assert t.api_mode == "dummy_test"
+        _REGISTRY.pop("dummy_test", None)
+
+
+class TestAnthropicTransport:
+    @pytest.fixture
+    def transport(self):
+        import agent.transports.anthropic  # noqa: F401
+
+        return get_transport("anthropic_messages")
+
+    def test_api_mode(self, transport):
+        assert transport.api_mode == "anthropic_messages"
+
+    def test_convert_tools_simple(self, transport):
+        tools = [{
+            "type": "function",
+            "function": {
+                "name": "test_tool",
+                "description": "A test",
+                "parameters": {"type": "object", "properties": {}},
+            },
+        }]
+        result = transport.convert_tools(tools)
+        assert len(result) == 1
+        assert result[0]["name"] == "test_tool"
+        assert "input_schema" in result[0]
+
+    def test_validate_response_none(self, transport):
+        assert transport.validate_response(None) is False
+
+    def test_validate_response_empty_content(self, transport):
+        r = SimpleNamespace(content=[])
+        assert transport.validate_response(r) is False
+
+    def test_validate_response_valid(self, transport):
+        r = SimpleNamespace(content=[SimpleNamespace(type="text", text="hello")])
+        assert transport.validate_response(r) is True
+
+    def test_map_finish_reason(self, transport):
+        assert transport.map_finish_reason("end_turn") == "stop"
+        assert transport.map_finish_reason("tool_use") == "tool_calls"
+        assert transport.map_finish_reason("max_tokens") == "length"
+        assert transport.map_finish_reason("stop_sequence") == "stop"
+        assert transport.map_finish_reason("refusal") == "content_filter"
+        assert transport.map_finish_reason("model_context_window_exceeded") == "length"
+        assert transport.map_finish_reason("unknown") == "stop"
+
+    def test_extract_cache_stats_none_usage(self, transport):
+        r = SimpleNamespace(usage=None)
+        assert transport.extract_cache_stats(r) is None
+
+    def test_extract_cache_stats_with_cache(self, transport):
+        usage = SimpleNamespace(cache_read_input_tokens=100, cache_creation_input_tokens=50)
+        r = SimpleNamespace(usage=usage)
+        result = transport.extract_cache_stats(r)
+        assert result == {"cached_tokens": 100, "creation_tokens": 50}
+
+    def test_extract_cache_stats_zero(self, transport):
+        usage = SimpleNamespace(cache_read_input_tokens=0, cache_creation_input_tokens=0)
+        r = SimpleNamespace(usage=usage)
+        assert transport.extract_cache_stats(r) is None
+
+    def test_normalize_response_text(self, transport):
+        r = SimpleNamespace(
+            content=[SimpleNamespace(type="text", text="Hello world")],
+            stop_reason="end_turn",
+            usage=SimpleNamespace(input_tokens=10, output_tokens=5),
+            model="claude-sonnet-4-6",
+        )
+        nr = transport.normalize_response(r)
+        assert isinstance(nr, NormalizedResponse)
+        assert nr.content == "Hello world"
+        assert nr.tool_calls is None or nr.tool_calls == []
+        assert nr.finish_reason == "stop"
+
+    def test_normalize_response_tool_calls(self, transport):
+        r = SimpleNamespace(
+            content=[
+                SimpleNamespace(type="tool_use", id="toolu_123", name="terminal", input={"command": "ls"}),
+            ],
+            stop_reason="tool_use",
+            usage=SimpleNamespace(input_tokens=10, output_tokens=20),
+            model="claude-sonnet-4-6",
+        )
+        nr = transport.normalize_response(r)
+        assert nr.finish_reason == "tool_calls"
+        assert len(nr.tool_calls) == 1
+        tc = nr.tool_calls[0]
+        assert tc.name == "terminal"
+        assert tc.id == "toolu_123"
+        assert '"command"' in tc.arguments
+
+    def test_normalize_response_thinking(self, transport):
+        r = SimpleNamespace(
+            content=[
+                SimpleNamespace(type="thinking", thinking="Let me think..."),
+                SimpleNamespace(type="text", text="The answer is 42"),
+            ],
+            stop_reason="end_turn",
+            usage=SimpleNamespace(input_tokens=10, output_tokens=15),
+            model="claude-sonnet-4-6",
+        )
+        nr = transport.normalize_response(r)
+        assert nr.content == "The answer is 42"
+        assert nr.reasoning == "Let me think..."
+
+    def test_build_kwargs_returns_dict(self, transport):
+        messages = [{"role": "user", "content": "Hello"}]
+        kw = transport.build_kwargs(
+            model="claude-sonnet-4-6",
+            messages=messages,
+            max_tokens=1024,
+        )
+        assert isinstance(kw, dict)
+        assert "model" in kw
+        assert "max_tokens" in kw
+        assert "messages" in kw
+
+    def test_convert_messages_extracts_system(self, transport):
+        messages = [
+            {"role": "system", "content": "You are helpful."},
+            {"role": "user", "content": "Hi"},
+        ]
+        system, msgs = transport.convert_messages(messages)
+        assert system is not None
+        assert len(msgs) >= 1

tests/agent/transports/test_types.py

@@ -0,0 +1,130 @@
+"""Tests for agent/transports/types.py — dataclass construction + helpers."""
+
+import json
+
+from agent.transports.types import (
+    NormalizedResponse,
+    ToolCall,
+    Usage,
+    build_tool_call,
+    map_finish_reason,
+)
+
+
+class TestToolCall:
+    def test_basic_construction(self):
+        tc = ToolCall(id="call_abc", name="terminal", arguments='{"cmd": "ls"}')
+        assert tc.id == "call_abc"
+        assert tc.name == "terminal"
+        assert tc.arguments == '{"cmd": "ls"}'
+        assert tc.provider_data is None
+
+    def test_none_id(self):
+        tc = ToolCall(id=None, name="read_file", arguments="{}")
+        assert tc.id is None
+
+    def test_provider_data(self):
+        tc = ToolCall(
+            id="call_x",
+            name="t",
+            arguments="{}",
+            provider_data={"call_id": "call_x", "response_item_id": "fc_x"},
+        )
+        assert tc.provider_data["call_id"] == "call_x"
+        assert tc.provider_data["response_item_id"] == "fc_x"
+
+
+class TestUsage:
+    def test_defaults(self):
+        u = Usage()
+        assert u.prompt_tokens == 0
+        assert u.completion_tokens == 0
+        assert u.total_tokens == 0
+        assert u.cached_tokens == 0
+
+    def test_explicit(self):
+        u = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150, cached_tokens=80)
+        assert u.total_tokens == 150
+
+
+class TestNormalizedResponse:
+    def test_text_only(self):
+        r = NormalizedResponse(content="hello", tool_calls=None, finish_reason="stop")
+        assert r.content == "hello"
+        assert r.tool_calls is None
+        assert r.finish_reason == "stop"
+        assert r.reasoning is None
+        assert r.usage is None
+        assert r.provider_data is None
+
+    def test_with_tool_calls(self):
+        tcs = [ToolCall(id="call_1", name="terminal", arguments='{"cmd":"pwd"}')]
+        r = NormalizedResponse(content=None, tool_calls=tcs, finish_reason="tool_calls")
+        assert r.finish_reason == "tool_calls"
+        assert len(r.tool_calls) == 1
+        assert r.tool_calls[0].name == "terminal"
+
+    def test_with_reasoning(self):
+        r = NormalizedResponse(
+            content="answer",
+            tool_calls=None,
+            finish_reason="stop",
+            reasoning="I thought about it",
+        )
+        assert r.reasoning == "I thought about it"
+
+    def test_with_provider_data(self):
+        r = NormalizedResponse(
+            content=None,
+            tool_calls=None,
+            finish_reason="stop",
+            provider_data={"reasoning_details": [{"type": "thinking", "thinking": "hmm"}]},
+        )
+        assert r.provider_data["reasoning_details"][0]["type"] == "thinking"
+
+
+class TestBuildToolCall:
+    def test_dict_arguments_serialized(self):
+        tc = build_tool_call(id="call_1", name="terminal", arguments={"cmd": "ls"})
+        assert tc.arguments == json.dumps({"cmd": "ls"})
+        assert tc.provider_data is None
+
+    def test_string_arguments_passthrough(self):
+        tc = build_tool_call(id="call_2", name="read_file", arguments='{"path": "/tmp"}')
+        assert tc.arguments == '{"path": "/tmp"}'
+
+    def test_provider_fields(self):
+        tc = build_tool_call(
+            id="call_3",
+            name="terminal",
+            arguments="{}",
+            call_id="call_3",
+            response_item_id="fc_3",
+        )
+        assert tc.provider_data == {"call_id": "call_3", "response_item_id": "fc_3"}
+
+    def test_none_id(self):
+        tc = build_tool_call(id=None, name="t", arguments="{}")
+        assert tc.id is None
+
+
+class TestMapFinishReason:
+    ANTHROPIC_MAP = {
+        "end_turn": "stop",
+        "tool_use": "tool_calls",
+        "max_tokens": "length",
+        "stop_sequence": "stop",
+        "refusal": "content_filter",
+    }
+
+    def test_known_reason(self):
+        assert map_finish_reason("end_turn", self.ANTHROPIC_MAP) == "stop"
+        assert map_finish_reason("tool_use", self.ANTHROPIC_MAP) == "tool_calls"
+        assert map_finish_reason("max_tokens", self.ANTHROPIC_MAP) == "length"
+        assert map_finish_reason("refusal", self.ANTHROPIC_MAP) == "content_filter"
+
+    def test_unknown_reason_defaults_to_stop(self):
+        assert map_finish_reason("something_new", self.ANTHROPIC_MAP) == "stop"
+
+    def test_none_reason(self):
+        assert map_finish_reason(None, self.ANTHROPIC_MAP) == "stop"

tests/tools/test_tts_kittentts.py

@@ -1,236 +0,0 @@
-"""Tests for the KittenTTS local provider in tools/tts_tool.py."""
-
-import json
-from unittest.mock import MagicMock, patch
-
-import numpy as np
-import pytest
-
-
-@pytest.fixture(autouse=True)
-def clean_env(monkeypatch):
-    for key in ("HERMES_SESSION_PLATFORM",):
-        monkeypatch.delenv(key, raising=False)
-
-
-@pytest.fixture(autouse=True)
-def clear_kittentts_cache():
-    """Reset the module-level model cache between tests."""
-    from tools import tts_tool as _tt
-    _tt._kittentts_model_cache.clear()
-    yield
-    _tt._kittentts_model_cache.clear()
-
-
-@pytest.fixture
-def mock_kittentts_module():
-    """Inject a fake kittentts + soundfile module that return stub objects."""
-    fake_model = MagicMock()
-    # 24kHz float32 PCM at ~2s of silence
-    fake_model.generate.return_value = np.zeros(48000, dtype=np.float32)
-    fake_cls = MagicMock(return_value=fake_model)
-    fake_kittentts = MagicMock()
-    fake_kittentts.KittenTTS = fake_cls
-
-    # Stub soundfile — the real package isn't installed in CI venv, and
-    # _generate_kittentts does `import soundfile as sf` at runtime.
-    fake_sf = MagicMock()
-
-    def _fake_write(path, audio, samplerate):
-        # Emulate writing a real file so downstream path checks succeed.
-        import pathlib
-
-        pathlib.Path(path).write_bytes(b"RIFF\x00\x00\x00\x00WAVEfmt fake")
-
-    fake_sf.write = _fake_write
-
-    with patch.dict(
-        "sys.modules",
-        {"kittentts": fake_kittentts, "soundfile": fake_sf},
-    ):
-        yield fake_model, fake_cls
-
-
-class TestGenerateKittenTts:
-    def test_successful_wav_generation(self, tmp_path, mock_kittentts_module):
-        from tools.tts_tool import _generate_kittentts
-
-        fake_model, fake_cls = mock_kittentts_module
-        output_path = str(tmp_path / "test.wav")
-        result = _generate_kittentts("Hello world", output_path, {})
-
-        assert result == output_path
-        assert (tmp_path / "test.wav").exists()
-        fake_cls.assert_called_once()
-        fake_model.generate.assert_called_once()
-
-    def test_config_passes_voice_speed_cleantext(self, tmp_path, mock_kittentts_module):
-        from tools.tts_tool import _generate_kittentts
-
-        fake_model, _ = mock_kittentts_module
-        config = {
-            "kittentts": {
-                "model": "KittenML/kitten-tts-mini-0.8",
-                "voice": "Luna",
-                "speed": 1.25,
-                "clean_text": False,
-            }
-        }
-        _generate_kittentts("Hi there", str(tmp_path / "out.wav"), config)
-
-        call_kwargs = fake_model.generate.call_args.kwargs
-        assert call_kwargs["voice"] == "Luna"
-        assert call_kwargs["speed"] == 1.25
-        assert call_kwargs["clean_text"] is False
-
-    def test_default_model_and_voice(self, tmp_path, mock_kittentts_module):
-        from tools.tts_tool import (
-            DEFAULT_KITTENTTS_MODEL,
-            DEFAULT_KITTENTTS_VOICE,
-            _generate_kittentts,
-        )
-
-        fake_model, fake_cls = mock_kittentts_module
-        _generate_kittentts("Hi", str(tmp_path / "out.wav"), {})
-
-        fake_cls.assert_called_once_with(DEFAULT_KITTENTTS_MODEL)
-        assert fake_model.generate.call_args.kwargs["voice"] == DEFAULT_KITTENTTS_VOICE
-
-    def test_model_is_cached_across_calls(self, tmp_path, mock_kittentts_module):
-        from tools.tts_tool import _generate_kittentts
-
-        _, fake_cls = mock_kittentts_module
-        _generate_kittentts("One", str(tmp_path / "a.wav"), {})
-        _generate_kittentts("Two", str(tmp_path / "b.wav"), {})
-
-        # Same model name → class instantiated exactly once
-        assert fake_cls.call_count == 1
-
-    def test_different_models_are_cached_separately(self, tmp_path, mock_kittentts_module):
-        from tools.tts_tool import _generate_kittentts
-
-        _, fake_cls = mock_kittentts_module
-        _generate_kittentts(
-            "A",
-            str(tmp_path / "a.wav"),
-            {"kittentts": {"model": "KittenML/kitten-tts-nano-0.8-int8"}},
-        )
-        _generate_kittentts(
-            "B",
-            str(tmp_path / "b.wav"),
-            {"kittentts": {"model": "KittenML/kitten-tts-mini-0.8"}},
-        )
-
-        assert fake_cls.call_count == 2
-
-    def test_non_wav_extension_triggers_ffmpeg_conversion(
-        self, tmp_path, mock_kittentts_module, monkeypatch
-    ):
-        """Non-.wav output path causes WAV → target ffmpeg conversion."""
-        from tools import tts_tool as _tt
-
-        calls = []
-
-        def fake_shutil_which(cmd):
-            return "/usr/bin/ffmpeg" if cmd == "ffmpeg" else None
-
-        def fake_run(cmd, check=False, timeout=None, **kw):
-            calls.append(cmd)
-            # Emulate ffmpeg writing the output file
-            import pathlib
-
-            out_path = cmd[-1]
-            pathlib.Path(out_path).write_bytes(b"fake-mp3-data")
-            return MagicMock(returncode=0)
-
-        monkeypatch.setattr(_tt.shutil, "which", fake_shutil_which)
-        monkeypatch.setattr(_tt.subprocess, "run", fake_run)
-
-        output_path = str(tmp_path / "test.mp3")
-        result = _tt._generate_kittentts("Hi", output_path, {})
-
-        assert result == output_path
-        assert len(calls) == 1
-        assert calls[0][0] == "/usr/bin/ffmpeg"
-
-    def test_missing_kittentts_raises_import_error(self, tmp_path, monkeypatch):
-        """When kittentts package is not installed, _import_kittentts raises."""
-        import sys
-
-        monkeypatch.setitem(sys.modules, "kittentts", None)
-        from tools.tts_tool import _generate_kittentts
-
-        with pytest.raises((ImportError, TypeError)):
-            _generate_kittentts("Hi", str(tmp_path / "out.wav"), {})
-
-
-class TestCheckKittenttsAvailable:
-    def test_reports_available_when_package_present(self, monkeypatch):
-        import importlib.util
-        from tools.tts_tool import _check_kittentts_available
-
-        fake_spec = MagicMock()
-        monkeypatch.setattr(
-            importlib.util,
-            "find_spec",
-            lambda name: fake_spec if name == "kittentts" else None,
-        )
-        assert _check_kittentts_available() is True
-
-    def test_reports_unavailable_when_package_missing(self, monkeypatch):
-        import importlib.util
-        from tools.tts_tool import _check_kittentts_available
-
-        monkeypatch.setattr(importlib.util, "find_spec", lambda name: None)
-        assert _check_kittentts_available() is False
-
-
-class TestDispatcherBranch:
-    def test_kittentts_not_installed_returns_helpful_error(self, monkeypatch, tmp_path):
-        """When provider=kittentts but package missing, return JSON error with setup hint."""
-        import sys
-
-        monkeypatch.setitem(sys.modules, "kittentts", None)
-        monkeypatch.setenv("HERMES_HOME", str(tmp_path))
-
-        from tools.tts_tool import text_to_speech_tool
-
-        # Write a config telling it to use kittentts
-        import yaml
-
-        (tmp_path / "config.yaml").write_text(
-            yaml.safe_dump({"tts": {"provider": "kittentts"}})
-        )
-
-        result = json.loads(text_to_speech_tool(text="Hello"))
-        assert result["success"] is False
-        assert "kittentts" in result["error"].lower()
-        assert "hermes setup tts" in result["error"].lower()
-
-    def test_non_telegram_explicit_wav_path_is_preserved(
-        self, monkeypatch, tmp_path, mock_kittentts_module
-    ):
-        """Explicit WAV outputs should stay WAV outside Telegram sessions."""
-        import yaml
-        from tools import tts_tool as _tt
-
-        monkeypatch.setenv("HERMES_HOME", str(tmp_path))
-        (tmp_path / "config.yaml").write_text(
-            yaml.safe_dump({"tts": {"provider": "kittentts"}})
-        )
-
-        def fail_convert(_path):
-            raise AssertionError("_convert_to_opus should not run outside Telegram")
-
-        monkeypatch.setattr(_tt, "_convert_to_opus", fail_convert)
-
-        result = json.loads(
-            _tt.text_to_speech_tool(
-                text="Hello from KittenTTS",
-                output_path=str(tmp_path / "out.wav"),
-            )
-        )
-
-        assert result["success"] is True
-        assert result["file_path"] == str(tmp_path / "out.wav")
-        assert (tmp_path / "out.wav").exists()

tools/tts_tool.py

@@ -2,14 +2,13 @@
 """
 Text-to-Speech Tool Module
 
-Supports seven TTS providers:
+Supports six TTS providers:
 - Edge TTS (default, free, no API key): Microsoft Edge neural voices
 - ElevenLabs (premium): High-quality voices, needs ELEVENLABS_API_KEY
 - OpenAI TTS: Good quality, needs OPENAI_API_KEY
 - MiniMax TTS: High-quality with voice cloning, needs MINIMAX_API_KEY
 - Mistral (Voxtral TTS): Multilingual, native Opus, needs MISTRAL_API_KEY
 - NeuTTS (local, free, no API key): On-device TTS via neutts_cli, needs neutts installed
-- KittenTTS (local, free, no API key): Lightweight on-device ONNX TTS via kittentts
 
 Output formats:
 - Opus (.ogg) for Telegram voice bubbles (requires ffmpeg for Edge TTS)
@@ -78,12 +77,6 @@ def _import_sounddevice():
     return sd
 
 
-def _import_kittentts():
-    """Lazy import KittenTTS. Returns the class or raises ImportError."""
-    from kittentts import KittenTTS
-    return KittenTTS
-
-
 # ===========================================================================
 # Defaults
 # ===========================================================================
@@ -93,8 +86,6 @@ DEFAULT_ELEVENLABS_VOICE_ID = "pNInz6obpgDQGcFmaJgB"  # Adam
 DEFAULT_ELEVENLABS_MODEL_ID = "eleven_multilingual_v2"
 DEFAULT_ELEVENLABS_STREAMING_MODEL_ID = "eleven_flash_v2_5"
 DEFAULT_OPENAI_MODEL = "gpt-4o-mini-tts"
-DEFAULT_KITTENTTS_MODEL = "KittenML/kitten-tts-nano-0.8-int8"  # 25MB
-DEFAULT_KITTENTTS_VOICE = "Jasper"
 DEFAULT_OPENAI_VOICE = "alloy"
 DEFAULT_OPENAI_BASE_URL = "https://api.openai.com/v1"
 DEFAULT_MINIMAX_MODEL = "speech-2.8-hd"
@@ -457,15 +448,6 @@ def _check_neutts_available() -> bool:
         return False
 
 
-def _check_kittentts_available() -> bool:
-    """Check if the kittentts engine is importable (installed locally)."""
-    try:
-        import importlib.util
-        return importlib.util.find_spec("kittentts") is not None
-    except Exception:
-        return False
-
-
 def _default_neutts_ref_audio() -> str:
     """Return path to the bundled default voice reference audio."""
     return str(Path(__file__).parent / "neutts_samples" / "jo.wav")
@@ -529,51 +511,6 @@ def _generate_neutts(text: str, output_path: str, tts_config: Dict[str, Any]) ->
     return output_path
 
 
-# ===========================================================================
-# Provider: KittenTTS (local, lightweight)
-# ===========================================================================
-
-# Module-level cache for KittenTTS model instances
-_kittentts_model_cache: Dict[str, Any] = {}
-
-
-def _generate_kittentts(text: str, output_path: str, tts_config: Dict[str, Any]) -> str:
-    """Generate speech using the local KittenTTS ONNX model."""
-    KittenTTS = _import_kittentts()
-    kt_config = tts_config.get("kittentts", {})
-    model_name = kt_config.get("model", DEFAULT_KITTENTTS_MODEL)
-    voice = kt_config.get("voice", DEFAULT_KITTENTTS_VOICE)
-    speed = kt_config.get("speed", 1.0)
-    clean_text = kt_config.get("clean_text", True)
-
-    global _kittentts_model_cache
-    if model_name not in _kittentts_model_cache:
-        logger.info("[KittenTTS] Loading model: %s", model_name)
-        _kittentts_model_cache[model_name] = KittenTTS(model_name)
-
-    model = _kittentts_model_cache[model_name]
-    audio = model.generate(text, voice=voice, speed=speed, clean_text=clean_text)
-
-    import soundfile as sf
-
-    wav_path = output_path
-    if not output_path.endswith(".wav"):
-        wav_path = output_path.rsplit(".", 1)[0] + ".wav"
-
-    sf.write(wav_path, audio, 24000)
-
-    if wav_path != output_path:
-        ffmpeg = shutil.which("ffmpeg")
-        if ffmpeg:
-            conv_cmd = [ffmpeg, "-i", wav_path, "-y", "-loglevel", "error", output_path]
-            subprocess.run(conv_cmd, check=True, timeout=30)
-            os.remove(wav_path)
-        else:
-            os.rename(wav_path, output_path)
-
-    return output_path
-
-
 # ===========================================================================
 # Main tool function
 # ===========================================================================
@@ -685,19 +622,6 @@ def text_to_speech_tool(
             logger.info("Generating speech with NeuTTS (local)...")
             _generate_neutts(text, file_str, tts_config)
 
-        elif provider == "kittentts":
-            try:
-                _import_kittentts()
-            except ImportError:
-                return json.dumps({
-                    "success": False,
-                    "error": "KittenTTS provider selected but 'kittentts' package not installed. "
-                             "Run 'hermes setup tts' and choose KittenTTS, or install manually: "
-                             "pip install https://github.com/KittenML/KittenTTS/releases/download/0.8.1/kittentts-0.8.1-py3-none-any.whl"
-                }, ensure_ascii=False)
-            logger.info("Generating speech with KittenTTS (local, lightweight)...")
-            _generate_kittentts(text, file_str, tts_config)
-
         else:
             # Default: Edge TTS (free), with NeuTTS as local fallback
             edge_available = True
@@ -734,10 +658,10 @@ def text_to_speech_tool(
                 "error": f"TTS generation produced no output (provider: {provider})"
             }, ensure_ascii=False)
 
-        # Try Opus conversion for Telegram compatibility only.
-        # Outside Telegram, preserve the caller's explicit output format.
+        # Try Opus conversion for Telegram compatibility
+        # Edge TTS outputs MP3, NeuTTS outputs WAV — both need ffmpeg conversion
         voice_compatible = False
-        if want_opus and provider in ("edge", "neutts", "minimax", "kittentts") and not file_str.endswith(".ogg"):
+        if provider in ("edge", "neutts", "minimax") and not file_str.endswith(".ogg"):
             opus_path = _convert_to_opus(file_str)
             if opus_path:
                 file_str = opus_path
@@ -818,8 +742,6 @@ def check_tts_requirements() -> bool:
         pass
     if _check_neutts_available():
         return True
-    if _check_kittentts_available():
-        return True
     return False
 
 

website/docs/user-guide/features/tts.md

@@ -10,7 +10,7 @@ Hermes Agent supports both text-to-speech output and voice message transcription
 
 ## Text-to-Speech
 
-Convert text to speech with seven providers:
+Convert text to speech with six providers:
 
 | Provider | Quality | Cost | API Key |
 |----------|---------|------|---------|
@@ -20,7 +20,6 @@ Convert text to speech with seven providers:
 | **MiniMax TTS** | Excellent | Paid | `MINIMAX_API_KEY` |
 | **Mistral (Voxtral TTS)** | Excellent | Paid | `MISTRAL_API_KEY` |
 | **NeuTTS** | Good | Free | None needed |
-| **KittenTTS** | Good | Free (local) | None needed |
 
 ### Platform Delivery
 
@@ -36,7 +35,7 @@ Convert text to speech with seven providers:
 ```yaml
 # In ~/.hermes/config.yaml
 tts:
-  provider: "edge"              # "edge" | "elevenlabs" | "openai" | "minimax" | "mistral" | "neutts" | "kittentts"
+  provider: "edge"              # "edge" | "elevenlabs" | "openai" | "minimax" | "mistral" | "neutts"
   speed: 1.0                    # Global speed multiplier (provider-specific settings override this)
   edge:
     voice: "en-US-AriaNeural"   # 322 voices, 74 languages
@@ -63,11 +62,6 @@ tts:
     ref_text: ''
     model: neuphonic/neutts-air-q4-gguf
     device: cpu
-  kittentts:
-    model: KittenML/kitten-tts-nano-0.8-int8   # 25MB int8 default; also micro and mini variants
-    voice: Jasper                               # Jasper, Bella, Luna, Bruno, Rosie, Hugo, Kiki, Leo
-    speed: 1.0
-    clean_text: true
 ```
 
 **Speed control**: The global `tts.speed` value applies to all providers by default. Each provider can override it with its own `speed` setting (e.g., `tts.openai.speed: 1.5`). Provider-specific speed takes precedence over the global value. Default is `1.0` (normal speed).
@@ -80,7 +74,6 @@ Telegram voice bubbles require Opus/OGG audio format:
 - **Edge TTS** (default) outputs MP3 and needs **ffmpeg** to convert:
 - **MiniMax TTS** outputs MP3 and needs **ffmpeg** to convert for Telegram voice bubbles
 - **NeuTTS** outputs WAV and also needs **ffmpeg** to convert for Telegram voice bubbles
-- **KittenTTS** outputs WAV and also needs **ffmpeg** to convert for Telegram voice bubbles
 
 ```bash
 # Ubuntu/Debian
@@ -93,7 +86,7 @@ brew install ffmpeg
 sudo dnf install ffmpeg
 ```
 
-Without ffmpeg, Edge TTS, MiniMax TTS, NeuTTS, and KittenTTS audio are sent as regular audio files (playable, but shown as a rectangular player instead of a voice bubble).
+Without ffmpeg, Edge TTS, MiniMax TTS, and NeuTTS audio are sent as regular audio files (playable, but shown as a rectangular player instead of a voice bubble).
 
 :::tip
 If you want voice bubbles without installing ffmpeg, switch to the OpenAI, ElevenLabs, or Mistral provider.