Hermes-agent
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Zakaria
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"""Anthropic Messages API transport.
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Delegates to the existing adapter functions in agent/anthropic_adapter.py.
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This transport owns format conversion and normalization — NOT client lifecycle.
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"""
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from typing import Any, Dict, List, Optional
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from agent.transports.base import ProviderTransport
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from agent.transports.types import NormalizedResponse
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class AnthropicTransport(ProviderTransport):
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"""Transport for api_mode='anthropic_messages'.
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Wraps the existing functions in anthropic_adapter.py behind the
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ProviderTransport ABC. Each method delegates — no logic is duplicated.
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"""
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@property
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def api_mode(self) -> str:
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return "anthropic_messages"
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def convert_messages(self, messages: List[Dict[str, Any]], **kwargs) -> Any:
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"""Convert OpenAI messages to Anthropic (system, messages) tuple.
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kwargs:
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base_url: Optional[str] — affects thinking signature handling.
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"""
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from agent.anthropic_adapter import convert_messages_to_anthropic
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base_url = kwargs.get("base_url")
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return convert_messages_to_anthropic(messages, base_url=base_url)
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def convert_tools(self, tools: List[Dict[str, Any]]) -> Any:
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"""Convert OpenAI tool schemas to Anthropic input_schema format."""
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from agent.anthropic_adapter import convert_tools_to_anthropic
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return convert_tools_to_anthropic(tools)
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def build_kwargs(
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self,
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model: str,
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messages: List[Dict[str, Any]],
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tools: Optional[List[Dict[str, Any]]] = None,
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**params,
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) -> Dict[str, Any]:
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"""Build Anthropic messages.create() kwargs.
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Calls convert_messages and convert_tools internally.
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params (all optional):
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max_tokens: int
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reasoning_config: dict | None
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tool_choice: str | None
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is_oauth: bool
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preserve_dots: bool
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context_length: int | None
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base_url: str | None
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fast_mode: bool
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drop_context_1m_beta: bool
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"""
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from agent.anthropic_adapter import build_anthropic_kwargs
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return build_anthropic_kwargs(
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model=model,
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messages=messages,
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tools=tools,
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max_tokens=params.get("max_tokens", 16384),
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reasoning_config=params.get("reasoning_config"),
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tool_choice=params.get("tool_choice"),
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is_oauth=params.get("is_oauth", False),
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preserve_dots=params.get("preserve_dots", False),
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context_length=params.get("context_length"),
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base_url=params.get("base_url"),
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fast_mode=params.get("fast_mode", False),
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drop_context_1m_beta=params.get("drop_context_1m_beta", False),
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)
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def normalize_response(self, response: Any, **kwargs) -> NormalizedResponse:
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"""Normalize Anthropic response to NormalizedResponse.
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Parses content blocks (text, thinking, tool_use), maps stop_reason
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to OpenAI finish_reason, and collects reasoning_details in provider_data.
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"""
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import json
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from agent.anthropic_adapter import _to_plain_data, _sanitize_replay_block
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from agent.transports.types import ToolCall
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strip_tool_prefix = kwargs.get("strip_tool_prefix", False)
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_MCP_PREFIX = "mcp_"
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text_parts = []
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reasoning_parts = []
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reasoning_details = []
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tool_calls = []
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# Verbatim, order-preserving copy of every content block in the turn.
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# Anthropic signs each thinking block against the turn content that
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# PRECEDES it at its position; when a turn interleaves thinking and
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# tool_use (adaptive/interleaved thinking, Claude 4.6+), the parallel
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# reasoning_details + tool_calls lists below lose that cross-type
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# ordering. Replaying the latest assistant message in the wrong order
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# invalidates the signatures -> HTTP 400 "thinking ... blocks in the
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# latest assistant message cannot be modified". Preserve the exact
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# block sequence here so the adapter can replay it unchanged. See
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# tests/agent/test_anthropic_thinking_block_order.py.
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ordered_blocks = []
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for block in response.content:
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block_dict = _to_plain_data(block)
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clean_block = None
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if isinstance(block_dict, dict):
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# Sanitize at capture so output-only SDK fields (parsed_output,
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# caller, citations=None, …) never persist to state.db and leak
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# back as request input on replay → HTTP 400 "Extra inputs are
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# not permitted". Defence-in-depth with the replay-side sanitize.
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clean_block = _sanitize_replay_block(block_dict)
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if clean_block is not None:
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ordered_blocks.append(clean_block)
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if block.type == "text":
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text_parts.append(block.text)
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elif block.type in ("thinking", "redacted_thinking"):
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if block.type == "thinking":
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reasoning_parts.append(block.thinking)
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# Use the sanitized block (clean_block) for reasoning_details too,
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# since _extract_preserved_thinking_blocks replays these on the
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# non-ordered path. Falls back to raw only if sanitize dropped it.
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if isinstance(clean_block, dict):
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reasoning_details.append(clean_block)
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elif isinstance(block_dict, dict):
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reasoning_details.append(block_dict)
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elif block.type == "tool_use":
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name = block.name
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if strip_tool_prefix and name.startswith(_MCP_PREFIX):
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stripped = name[len(_MCP_PREFIX):]
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# Only strip the mcp_ prefix for OAuth-injected tools
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# (where Hermes adds the prefix when sending to Anthropic
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# and must remove it on the way back). Native MCP server
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# tools (from mcp_servers: in config.yaml) are registered
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# in the tool registry under their FULL mcp_<server>_<tool>
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# name and must NOT be stripped. GH-25255.
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from tools.registry import registry as _tool_registry
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if (_tool_registry.get_entry(stripped)
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and not _tool_registry.get_entry(name)):
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name = stripped
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tool_calls.append(
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ToolCall(
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id=block.id,
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name=name,
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arguments=json.dumps(block.input),
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)
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)
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finish_reason = self._STOP_REASON_MAP.get(response.stop_reason, "stop")
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provider_data = {}
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if reasoning_details:
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provider_data["reasoning_details"] = reasoning_details
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# Only worth carrying the ordered-blocks channel when the turn
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# actually interleaves signed thinking with tool_use — that's the
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# only shape the parallel lists reconstruct incorrectly. A turn that
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# is purely text, or thinking-then-tools with a single leading
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# thinking block, replays correctly without it.
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_has_signed_thinking = any(
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isinstance(b, dict)
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and b.get("type") in ("thinking", "redacted_thinking")
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and (b.get("signature") or b.get("data"))
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for b in ordered_blocks
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)
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_has_tool_use = any(
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isinstance(b, dict) and b.get("type") == "tool_use"
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for b in ordered_blocks
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)
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if _has_signed_thinking and _has_tool_use:
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provider_data["anthropic_content_blocks"] = ordered_blocks
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return NormalizedResponse(
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content="\n".join(text_parts) if text_parts else None,
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tool_calls=tool_calls or None,
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finish_reason=finish_reason,
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reasoning="\n\n".join(reasoning_parts) if reasoning_parts else None,
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usage=None,
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provider_data=provider_data or None,
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)
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def validate_response(self, response: Any) -> bool:
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"""Check Anthropic response structure is valid.
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An empty content list is legitimate for terminal stop reasons that
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carry no text payload:
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- ``end_turn`` — the model's canonical "nothing more to add" after a
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tool turn that already delivered the user-facing text.
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- ``refusal`` — the model declined to respond (Claude 4.5+). The
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Messages API returns an empty ``content`` list with this stop
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reason. Treating it as invalid sends a deterministic refusal into
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the invalid-response retry loop, which reproduces the refusal on
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every attempt and surfaces a misleading "rate limited / invalid
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response" error instead of the refusal. ``normalize_response`` maps
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``refusal`` → ``content_filter`` so the agent loop's refusal handler
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can surface it.
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Treating either as invalid falsely retries a completed response.
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"""
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if response is None:
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return False
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content_blocks = getattr(response, "content", None)
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if not isinstance(content_blocks, list):
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return False
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if not content_blocks:
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return getattr(response, "stop_reason", None) in {"end_turn", "refusal"}
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return True
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def extract_cache_stats(self, response: Any) -> Optional[Dict[str, int]]:
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"""Extract Anthropic cache_read and cache_creation token counts."""
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usage = getattr(response, "usage", None)
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if usage is None:
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return None
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cached = getattr(usage, "cache_read_input_tokens", 0) or 0
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written = getattr(usage, "cache_creation_input_tokens", 0) or 0
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if cached or written:
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return {"cached_tokens": cached, "creation_tokens": written}
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return None
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# Promote the adapter's canonical mapping to module level so it's shared
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_STOP_REASON_MAP = {
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"end_turn": "stop",
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"tool_use": "tool_calls",
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"max_tokens": "length",
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"stop_sequence": "stop",
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"refusal": "content_filter",
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"model_context_window_exceeded": "length",
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}
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def map_finish_reason(self, raw_reason: str) -> str:
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"""Map Anthropic stop_reason to OpenAI finish_reason."""
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return self._STOP_REASON_MAP.get(raw_reason, "stop")
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# Auto-register on import
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from agent.transports import register_transport # noqa: E402
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register_transport("anthropic_messages", AnthropicTransport)
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