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https://github.com/NousResearch/hermes-agent.git
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refactor: remove _nr_to_assistant_message shim + fix flush_memories guard
NormalizedResponse and ToolCall now have backward-compat properties
so the agent loop can read them directly without the shim:
ToolCall: .type, .function (returns self), .call_id, .response_item_id
NormalizedResponse: .reasoning_content, .reasoning_details,
.codex_reasoning_items
This eliminates the 35-line shim and its 4 call sites in run_agent.py.
Also changes flush_memories guard from hasattr(response, 'choices')
to self.api_mode in ('chat_completions', 'bedrock_converse') so it
works with raw boto3 dicts too.
WS1 items 3+4 of Cycle 2 (#14418).
This commit is contained in:
kshitijk4poor
Teknium
parent
f4612785a4
commit
43de1ca8c2
8 changed files with 233 additions and 157 deletions
95
run_agent.py
95
run_agent.py
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@ -6766,42 +6766,6 @@ class AIAgent:
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cache[mode] = t
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return t
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@staticmethod
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def _nr_to_assistant_message(nr):
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"""Convert a NormalizedResponse to the SimpleNamespace shape downstream expects.
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This is the single back-compat shim between the transport layer
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(NormalizedResponse) and the agent loop (SimpleNamespace with
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.content, .tool_calls, .reasoning, .reasoning_content,
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.reasoning_details, .codex_reasoning_items, and per-tool-call
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.call_id / .response_item_id).
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TODO: Remove when downstream code reads NormalizedResponse directly.
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"""
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tc_list = None
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if nr.tool_calls:
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tc_list = []
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for tc in nr.tool_calls:
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tc_ns = 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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if tc.provider_data:
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for key in ("call_id", "response_item_id"):
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if tc.provider_data.get(key):
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setattr(tc_ns, key, tc.provider_data[key])
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tc_list.append(tc_ns)
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pd = nr.provider_data or {}
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return SimpleNamespace(
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content=nr.content,
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tool_calls=tc_list or None,
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reasoning=nr.reasoning,
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reasoning_content=pd.get("reasoning_content"),
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reasoning_details=pd.get("reasoning_details"),
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codex_reasoning_items=pd.get("codex_reasoning_items"),
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)
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def _prepare_anthropic_messages_for_api(self, api_messages: list) -> list:
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if not any(
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isinstance(msg, dict) and self._content_has_image_parts(msg.get("content"))
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@ -7503,20 +7467,25 @@ class AIAgent:
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]
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elif self.api_mode == "anthropic_messages" and not _aux_available:
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_tfn = self._get_transport()
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_flush_nr = _tfn.normalize_response(response, strip_tool_prefix=self._is_anthropic_oauth)
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if _flush_nr and _flush_nr.tool_calls:
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_flush_result = _tfn.normalize_response(response, strip_tool_prefix=self._is_anthropic_oauth)
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if _flush_result and _flush_result.tool_calls:
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tool_calls = [
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SimpleNamespace(
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id=tc.id, type="function",
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function=SimpleNamespace(name=tc.name, arguments=tc.arguments),
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) for tc in _flush_nr.tool_calls
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) for tc in _flush_result.tool_calls
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]
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elif hasattr(response, "choices") and response.choices:
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elif self.api_mode in ("chat_completions", "bedrock_converse"):
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# chat_completions / bedrock — normalize through transport
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_flush_cc_nr = self._get_transport().normalize_response(response)
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_flush_msg = self._nr_to_assistant_message(_flush_cc_nr)
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if _flush_msg.tool_calls:
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tool_calls = _flush_msg.tool_calls
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_flush_result = self._get_transport().normalize_response(response)
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if _flush_result.tool_calls:
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tool_calls = _flush_result.tool_calls
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elif _aux_available and hasattr(response, "choices") and response.choices:
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# Auxiliary client returned OpenAI-shaped response while main
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# api_mode is codex/anthropic — extract tool_calls from .choices
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_aux_msg = response.choices[0].message
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if hasattr(_aux_msg, "tool_calls") and _aux_msg.tool_calls:
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tool_calls = _aux_msg.tool_calls
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for tc in tool_calls:
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if tc.function.name == "memory":
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@ -8582,12 +8551,12 @@ class AIAgent:
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is_oauth=self._is_anthropic_oauth,
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preserve_dots=self._anthropic_preserve_dots())
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summary_response = self._anthropic_messages_create(_ant_kw)
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_sum_nr = _tsum.normalize_response(summary_response, strip_tool_prefix=self._is_anthropic_oauth)
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final_response = (_sum_nr.content or "").strip()
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_summary_result = _tsum.normalize_response(summary_response, strip_tool_prefix=self._is_anthropic_oauth)
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final_response = (_summary_result.content or "").strip()
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else:
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summary_response = self._ensure_primary_openai_client(reason="iteration_limit_summary").chat.completions.create(**summary_kwargs)
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_sum_cc_nr = self._get_transport().normalize_response(summary_response)
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final_response = (_sum_cc_nr.content or "").strip()
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_summary_result = self._get_transport().normalize_response(summary_response)
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final_response = (_summary_result.content or "").strip()
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if final_response:
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if "<think>" in final_response:
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@ -8612,8 +8581,8 @@ class AIAgent:
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max_tokens=self.max_tokens, reasoning_config=self.reasoning_config,
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preserve_dots=self._anthropic_preserve_dots())
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retry_response = self._anthropic_messages_create(_ant_kw2)
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_retry_nr = _tretry.normalize_response(retry_response, strip_tool_prefix=self._is_anthropic_oauth)
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final_response = (_retry_nr.content or "").strip()
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_retry_result = _tretry.normalize_response(retry_response, strip_tool_prefix=self._is_anthropic_oauth)
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final_response = (_retry_result.content or "").strip()
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else:
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summary_kwargs = {
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"model": self.model,
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@ -8627,8 +8596,8 @@ class AIAgent:
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summary_kwargs["extra_body"] = summary_extra_body
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summary_response = self._ensure_primary_openai_client(reason="iteration_limit_summary_retry").chat.completions.create(**summary_kwargs)
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_retry_cc_nr = self._get_transport().normalize_response(summary_response)
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final_response = (_retry_cc_nr.content or "").strip()
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_retry_result = self._get_transport().normalize_response(summary_response)
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final_response = (_retry_result.content or "").strip()
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if final_response:
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if "<think>" in final_response:
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@ -9657,13 +9626,13 @@ class AIAgent:
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elif self.api_mode == "bedrock_converse":
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# Bedrock response already normalized at dispatch — use transport
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_bt_fr = self._get_transport()
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_bt_fr_nr = _bt_fr.normalize_response(response)
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finish_reason = _bt_fr_nr.finish_reason
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_bedrock_result = _bt_fr.normalize_response(response)
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finish_reason = _bedrock_result.finish_reason
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else:
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_cc_fr = self._get_transport()
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_cc_fr_nr = _cc_fr.normalize_response(response)
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finish_reason = _cc_fr_nr.finish_reason
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assistant_message = self._nr_to_assistant_message(_cc_fr_nr)
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_finish_result = _cc_fr.normalize_response(response)
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finish_reason = _finish_result.finish_reason
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assistant_message = _finish_result
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if self._should_treat_stop_as_truncated(
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finish_reason,
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assistant_message,
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@ -9688,12 +9657,12 @@ class AIAgent:
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_trunc_msg = None
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_trunc_transport = self._get_transport()
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if self.api_mode == "anthropic_messages":
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_trunc_nr = _trunc_transport.normalize_response(
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_trunc_result = _trunc_transport.normalize_response(
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response, strip_tool_prefix=self._is_anthropic_oauth
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)
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else:
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_trunc_nr = _trunc_transport.normalize_response(response)
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_trunc_msg = self._nr_to_assistant_message(_trunc_nr)
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_trunc_result = _trunc_transport.normalize_response(response)
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_trunc_msg = _trunc_result
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_trunc_content = getattr(_trunc_msg, "content", None) if _trunc_msg else None
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_trunc_has_tool_calls = bool(getattr(_trunc_msg, "tool_calls", None)) if _trunc_msg else False
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@ -10928,9 +10897,9 @@ class AIAgent:
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_normalize_kwargs = {}
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if self.api_mode == "anthropic_messages":
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_normalize_kwargs["strip_tool_prefix"] = self._is_anthropic_oauth
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_nr = _transport.normalize_response(response, **_normalize_kwargs)
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assistant_message = self._nr_to_assistant_message(_nr)
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finish_reason = _nr.finish_reason
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normalized = _transport.normalize_response(response, **_normalize_kwargs)
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assistant_message = normalized
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finish_reason = normalized.finish_reason
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# Normalize content to string — some OpenAI-compatible servers
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# (llama-server, etc.) return content as a dict or list instead
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