Initial import of NousResearch/hermes-agent

agent/transports/anthropic.py

@@ -0,0 +1,189 @@
+"""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'.
+
+    Wraps the existing functions in anthropic_adapter.py behind the
+    ProviderTransport ABC.  Each method delegates — no logic is duplicated.
+    """
+
+    @property
+    def api_mode(self) -> str:
+        return "anthropic_messages"
+
+    def convert_messages(self, messages: List[Dict[str, Any]], **kwargs) -> Any:
+        """Convert OpenAI messages to Anthropic (system, messages) tuple.
+
+        kwargs:
+            base_url: Optional[str] — affects thinking signature handling.
+        """
+        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:
+        """Convert OpenAI tool schemas to Anthropic input_schema format."""
+        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]:
+        """Build Anthropic messages.create() kwargs.
+
+        Calls convert_messages and convert_tools internally.
+
+        params (all optional):
+            max_tokens: int
+            reasoning_config: dict | None
+            tool_choice: str | None
+            is_oauth: bool
+            preserve_dots: bool
+            context_length: int | None
+            base_url: str | None
+            fast_mode: bool
+            drop_context_1m_beta: bool
+        """
+        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),
+            drop_context_1m_beta=params.get("drop_context_1m_beta", False),
+        )
+
+    def normalize_response(self, response: Any, **kwargs) -> NormalizedResponse:
+        """Normalize Anthropic response to NormalizedResponse.
+
+        Parses content blocks (text, thinking, tool_use), maps stop_reason
+        to OpenAI finish_reason, and collects reasoning_details in provider_data.
+        """
+        import json
+        from agent.anthropic_adapter import _to_plain_data
+        from agent.transports.types import ToolCall
+
+        strip_tool_prefix = kwargs.get("strip_tool_prefix", False)
+        _MCP_PREFIX = "mcp_"
+
+        text_parts = []
+        reasoning_parts = []
+        reasoning_details = []
+        tool_calls = []
+
+        for block in response.content:
+            if block.type == "text":
+                text_parts.append(block.text)
+            elif block.type == "thinking":
+                reasoning_parts.append(block.thinking)
+                block_dict = _to_plain_data(block)
+                if isinstance(block_dict, dict):
+                    reasoning_details.append(block_dict)
+            elif block.type == "tool_use":
+                name = block.name
+                if strip_tool_prefix and name.startswith(_MCP_PREFIX):
+                    stripped = name[len(_MCP_PREFIX):]
+                    # Only strip the mcp_ prefix for OAuth-injected tools
+                    # (where Hermes adds the prefix when sending to Anthropic
+                    # and must remove it on the way back).  Native MCP server
+                    # tools (from mcp_servers: in config.yaml) are registered
+                    # in the tool registry under their FULL mcp_<server>_<tool>
+                    # name and must NOT be stripped.  GH-25255.
+                    from tools.registry import registry as _tool_registry
+                    if (_tool_registry.get_entry(stripped)
+                            and not _tool_registry.get_entry(name)):
+                        name = stripped
+                tool_calls.append(
+                    ToolCall(
+                        id=block.id,
+                        name=name,
+                        arguments=json.dumps(block.input),
+                    )
+                )
+
+        finish_reason = self._STOP_REASON_MAP.get(response.stop_reason, "stop")
+
+        provider_data = {}
+        if reasoning_details:
+            provider_data["reasoning_details"] = reasoning_details
+
+        return NormalizedResponse(
+            content="\n".join(text_parts) if text_parts else None,
+            tool_calls=tool_calls or None,
+            finish_reason=finish_reason,
+            reasoning="\n\n".join(reasoning_parts) if reasoning_parts else None,
+            usage=None,
+            provider_data=provider_data or None,
+        )
+
+    def validate_response(self, response: Any) -> bool:
+        """Check Anthropic response structure is valid.
+
+        An empty content list is legitimate when ``stop_reason == "end_turn"``
+        — the model's canonical way of signalling "nothing more to add" after
+        a tool turn that already delivered the user-facing text. Treating it
+        as invalid falsely retries a completed response.
+        """
+        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 getattr(response, "stop_reason", None) == "end_turn"
+        return True
+
+    def extract_cache_stats(self, response: Any) -> Optional[Dict[str, int]]:
+        """Extract Anthropic cache_read and cache_creation token counts."""
+        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
+
+    # Promote the adapter's canonical mapping to module level so it's shared
+    _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:
+        """Map Anthropic stop_reason to OpenAI finish_reason."""
+        return self._STOP_REASON_MAP.get(raw_reason, "stop")
+
+
+# Auto-register on import
+from agent.transports import register_transport  # noqa: E402
+
+register_transport("anthropic_messages", AnthropicTransport)