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feat: add transport types + migrate Anthropic normalize path
Add agent/transports/types.py with three shared dataclasses: - NormalizedResponse: content, tool_calls, finish_reason, reasoning, usage, provider_data - ToolCall: id, name, arguments, provider_data (per-tool-call protocol metadata) - Usage: prompt_tokens, completion_tokens, total_tokens, cached_tokens Add normalize_anthropic_response_v2() to anthropic_adapter.py — wraps the existing v1 function and maps its output to NormalizedResponse. One call site in run_agent.py (the main normalize branch) uses v2 with a back-compat shim to SimpleNamespace for downstream code. No ABC, no registry, no streaming, no client lifecycle. Those land in PR 3 with the first concrete transport (AnthropicTransport). 46 new tests: - test_types.py: dataclass construction, build_tool_call, map_finish_reason - test_anthropic_normalize_v2.py: v1-vs-v2 regression tests (text, tools, thinking, mixed, stop reasons, mcp prefix stripping, edge cases) Part of the provider transport refactor (PR 2 of 9).
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7 changed files with 554 additions and 2 deletions
27
run_agent.py
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run_agent.py
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@ -10778,10 +10778,33 @@ class AIAgent:
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if self.api_mode == "codex_responses":
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assistant_message, finish_reason = self._normalize_codex_response(response)
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elif self.api_mode == "anthropic_messages":
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from agent.anthropic_adapter import normalize_anthropic_response
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assistant_message, finish_reason = normalize_anthropic_response(
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from agent.anthropic_adapter import normalize_anthropic_response_v2
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_nr = normalize_anthropic_response_v2(
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response, strip_tool_prefix=self._is_anthropic_oauth
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)
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# Back-compat shim: downstream code expects SimpleNamespace with
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# .content, .tool_calls, .reasoning, .reasoning_content,
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# .reasoning_details attributes. This shim makes the cost of the
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# old interface visible — it vanishes when the full transport
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# wiring lands (PR 3+).
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assistant_message = SimpleNamespace(
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content=_nr.content,
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tool_calls=[
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SimpleNamespace(
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id=tc.id,
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type="function",
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function=SimpleNamespace(name=tc.name, arguments=tc.arguments),
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)
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for tc in (_nr.tool_calls or [])
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] or None,
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reasoning=_nr.reasoning,
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reasoning_content=None,
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reasoning_details=(
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_nr.provider_data.get("reasoning_details")
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if _nr.provider_data else None
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),
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)
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finish_reason = _nr.finish_reason
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else:
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assistant_message = response.choices[0].message
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