Render one assistant message per turn, and make the request timeout a setting
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Two setup failures found while calibrating the new benchmark tasks, both of which would also fail real milestones. Devstral's chat template (Mistral) rejected any turn in which the model wrote some text before a tool call: the OpenAI renderer made one message per event, so the turn became a text message and a tool-call message in a row, and llama.cpp answered 500. The renderer now folds consecutive user or assistant messages into one, which is the OpenAI format for a turn anyway. Checked against the live server: the captured failing request no longer occurs, over six real runs. LiteLLMClient gave up on any request after a fixed 120 s. A dense 27B model writing a whole module in one turn takes longer, so the executor escalated work that was going well. llm.request_timeout_s now sets it for both clients, default 600. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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@@ -5,6 +5,7 @@ from typing import Any
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import pytest
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from nisse_dev.config import AgentConfig, ConfigError
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from nisse_dev.llm import DEFAULT_REQUEST_TIMEOUT_S
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# A complete config as the YAML file would give it, with secrets by reference.
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COMPLETE: dict[str, Any] = {
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@@ -122,6 +123,25 @@ def test_max_output_tokens_is_configurable_and_checked() -> None:
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build(llm__max_output_tokens=0)
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def test_request_timeout_is_configurable_and_checked() -> None:
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assert build().request_timeout_s == DEFAULT_REQUEST_TIMEOUT_S
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assert build(llm__request_timeout_s=900).request_timeout_s == 900.0
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for bad in (0, -5, "soon"):
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with pytest.raises(ConfigError, match="llm.request_timeout_s"):
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build(llm__request_timeout_s=bad)
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def test_the_request_timeout_reaches_both_clients() -> None:
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from nisse_dev.agent import make_llm_clients
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from nisse_dev.catalog import ModelCatalog
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normalized, passthrough = make_llm_clients(
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build(llm__request_timeout_s=42), ModelCatalog({})
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)
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for client in (normalized, passthrough):
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assert client._inner._http.timeout.read == 42.0
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def test_redacted_summary_carries_no_secrets() -> None:
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summary = build().redacted()
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flat = " ".join(summary.values())
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