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docs: drop retired model IDs from the reproducibility note and smoke script

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The README reproducibility example named gpt-4.1 and the structured-output smoke
script listed gemini-2.5-flash / deepseek-chat / qwen-plus / grok-4 — all retired
from the catalog. Generalize the note and refresh the smoke defaults.
This commit is contained in:
Yijia-Xiao
2026-06-21 23:50:33 +00:00
parent 8ab24f30af
commit cbd17ac3e0
2 changed files with 7 additions and 7 deletions
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6
README.md
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@@ -275,14 +275,14 @@ Language model sampling is non-deterministic. Even at a fixed temperature, provi
Live data moves. News, StockTwits, and Reddit return different content as time passes, so a run today sees different inputs than a run last week even for the same historical trade date. Pin the analysis date to hold the price and indicator window fixed, but the social and news sources still reflect "now".
To reduce variation you can lower the sampling temperature. Set `temperature` in your config (or `TRADINGAGENTS_TEMPERATURE` in `.env`); lower values make models that honor it more repeatable. Reasoning models largely ignore temperature, so for tighter reproducibility pair a low temperature with a non-reasoning model such as `gpt-4.1`.
To reduce variation you can lower the sampling temperature. Set `temperature` in your config (or `TRADINGAGENTS_TEMPERATURE` in `.env`); lower values make models that honor it more repeatable. The current curated models are reasoning-first and largely ignore temperature, so for tighter reproducibility use a non-reasoning model, which you can set explicitly via the Custom model ID option.
```python
config = DEFAULT_CONFIG.copy()
config["llm_provider"] = "openai"
config["deep_think_llm"] = "gpt-4.1" # non-reasoning model honors temperature
config["quick_think_llm"] = "gpt-4.1"
config["temperature"] = 0.0
# Reasoning models ignore temperature. For tighter reproducibility, set a
# non-reasoning deep/quick model explicitly (e.g. via the Custom model ID option).
```
What does not vary anymore: the analyzed company identity is resolved deterministically from the ticker before any agent runs, and the market analyst grounds exact price and indicator claims in a verified data snapshot. Earlier reports of "different companies" or fabricated price levels across runs are addressed by these two mechanisms.

8
scripts/smoke_structured_output.py
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@@ -31,12 +31,12 @@ from tradingagents.llm_clients import create_llm_client
PROVIDER_DEFAULTS = {
"openai": ("gpt-5.4-mini", None),
"google": ("gemini-2.5-flash", None),
"google": ("gemini-3.5-flash", None),
"anthropic": ("claude-sonnet-4-6", None),
"deepseek": ("deepseek-chat", None),
"qwen": ("qwen-plus", None),
"deepseek": ("deepseek-v4-flash", None),
"qwen": ("qwen3.7-plus", None),
"glm": ("glm-5", None),
"xai": ("grok-4", None),
"xai": ("grok-4.3", None),
}