feat(config): expose sampling temperature and document reproducibility

Adds a cross-provider temperature config (and TRADINGAGENTS_TEMPERATURE),
forwarded to every LLM client when set, so runs can be made less variable
on models that honor it. Adds a README "Reproducibility" section that
separates the sources of run-to-run variation, what users can control
(temperature, non-reasoning model, pinned date), and what is inherent to
LLM-driven analysis, and notes that the identity and verified-data fixes
already removed the "different companies / fabricated prices" variance.

#178 #168

tradingagents/default_config.py

@@ -17,6 +17,7 @@ _ENV_OVERRIDES = {
     "TRADINGAGENTS_MAX_RISK_ROUNDS":      "max_risk_discuss_rounds",
     "TRADINGAGENTS_CHECKPOINT_ENABLED":   "checkpoint_enabled",
     "TRADINGAGENTS_BENCHMARK_TICKER":     "benchmark_ticker",
+    "TRADINGAGENTS_TEMPERATURE":          "temperature",
 }
 
 
@@ -64,6 +65,11 @@ DEFAULT_CONFIG = _apply_env_overrides({
     "google_thinking_level": None,      # "high", "minimal", etc.
     "openai_reasoning_effort": None,    # "medium", "high", "low"
     "anthropic_effort": None,           # "high", "medium", "low"
+    # Sampling temperature, forwarded to every provider when set. None leaves
+    # each provider at its own default. Lower values reduce run-to-run
+    # variation on models that honor it; reasoning models largely ignore it
+    # and no setting makes LLM output bit-identical across runs (see README).
+    "temperature": None,
     # Checkpoint/resume: when True, LangGraph saves state after each node
     # so a crashed run can resume from the last successful step.
     "checkpoint_enabled": False,

tradingagents/graph/trading_graph.py

@@ -155,6 +155,13 @@ class TradingAgentsGraph:
             if effort:
                 kwargs["effort"] = effort
 
+        # Sampling temperature is cross-provider: forward it whenever set.
+        # float() here so a value coming from a TRADINGAGENTS_TEMPERATURE env
+        # string ("0.2") works the same as a programmatic float.
+        temperature = self.config.get("temperature")
+        if temperature is not None and temperature != "":
+            kwargs["temperature"] = float(temperature)
+
         return kwargs
 
     def _create_tool_nodes(self) -> Dict[str, ToolNode]:

tradingagents/llm_clients/anthropic_client.py

@@ -7,7 +7,7 @@ from .base_client import BaseLLMClient, normalize_content
 from .validators import validate_model
 
 _PASSTHROUGH_KWARGS = (
-    "timeout", "max_retries", "api_key", "max_tokens",
+    "timeout", "max_retries", "api_key", "max_tokens", "temperature",
     "callbacks", "http_client", "http_async_client", "effort",
 )
 

tradingagents/llm_clients/azure_client.py

@@ -7,7 +7,7 @@ from .base_client import BaseLLMClient, normalize_content
 from .validators import validate_model
 
 _PASSTHROUGH_KWARGS = (
-    "timeout", "max_retries", "api_key", "reasoning_effort",
+    "timeout", "max_retries", "api_key", "reasoning_effort", "temperature",
     "callbacks", "http_client", "http_async_client",
 )
 

tradingagents/llm_clients/google_client.py

@@ -31,7 +31,7 @@ class GoogleClient(BaseLLMClient):
         if self.base_url:
             llm_kwargs["base_url"] = self.base_url
 
-        for key in ("timeout", "max_retries", "callbacks", "http_client", "http_async_client"):
+        for key in ("timeout", "max_retries", "temperature", "callbacks", "http_client", "http_async_client"):
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 

tradingagents/llm_clients/openai_client.py

@@ -138,7 +138,7 @@ class MinimaxChatOpenAI(NormalizedChatOpenAI):
 
 # Kwargs forwarded from user config to ChatOpenAI
 _PASSTHROUGH_KWARGS = (
-    "timeout", "max_retries", "reasoning_effort",
+    "timeout", "max_retries", "reasoning_effort", "temperature",
     "api_key", "callbacks", "http_client", "http_async_client",
 )