fix: use run_tool_loop instead of invoke in analyst agents

This commit updates the `fundamentals_analyst`, `market_analyst`,
`social_media_analyst`, and `news_analyst` files to use `run_tool_loop`
instead of `.invoke()`. Using `.invoke()` resulted in the LLM execution
stopping immediately upon a tool call request without executing the tool,
returning an empty report or raw JSON. The `run_tool_loop` function
ensures tools are executed recursively and the final text content is
returned.

Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>

tradingagents/agents/analysts/fundamentals_analyst.py

@@ -11,6 +11,7 @@ from tradingagents.agents.utils.fundamental_data_tools import (
     get_sector_relative,
 )
 from tradingagents.agents.utils.news_data_tools import get_insider_transactions
+from tradingagents.agents.utils.tool_runner import run_tool_loop
 from tradingagents.dataflows.config import get_config
 
 
@@ -66,12 +67,9 @@ def create_fundamentals_analyst(llm):
 
         chain = prompt | llm.bind_tools(tools)
 
-        result = chain.invoke(state["messages"])
+        result = run_tool_loop(chain, state["messages"], tools)
 
-        report = ""
+        report = result.content or ""
-
-        if len(result.tool_calls) == 0:
-            report = result.content
 
         return {
             "messages": [result],

tradingagents/agents/analysts/market_analyst.py

@@ -3,6 +3,7 @@ import time
 from tradingagents.agents.utils.core_stock_tools import get_stock_data
 from tradingagents.agents.utils.technical_indicators_tools import get_indicators
 from tradingagents.agents.utils.fundamental_data_tools import get_macro_regime
+from tradingagents.agents.utils.tool_runner import run_tool_loop
 from tradingagents.dataflows.config import get_config
 
 
@@ -73,15 +74,13 @@ Volume-Based Indicators:
 
         chain = prompt | llm.bind_tools(tools)
 
-        result = chain.invoke(state["messages"])
+        result = run_tool_loop(chain, state["messages"], tools)
 
-        report = ""
+        report = result.content or ""
         macro_regime_report = ""
 
-        if len(result.tool_calls) == 0:
-            report = result.content
         # Extract macro regime section if present
-            if "Macro Regime Classification" in report or "RISK-ON" in report.upper() or "RISK-OFF" in report.upper() or "TRANSITION" in report.upper():
+        if report and ("Macro Regime Classification" in report or "RISK-ON" in report.upper() or "RISK-OFF" in report.upper() or "TRANSITION" in report.upper()):
             macro_regime_report = report
 
         return {

tradingagents/agents/analysts/news_analyst.py

@@ -1,6 +1,7 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 import json
 from tradingagents.agents.utils.news_data_tools import get_news, get_global_news
+from tradingagents.agents.utils.tool_runner import run_tool_loop
 from tradingagents.dataflows.config import get_config
 
 
@@ -42,12 +43,9 @@ def create_news_analyst(llm):
         prompt = prompt.partial(instrument_context=instrument_context)
 
         chain = prompt | llm.bind_tools(tools)
-        result = chain.invoke(state["messages"])
+        result = run_tool_loop(chain, state["messages"], tools)
 
-        report = ""
+        report = result.content or ""
-
-        if len(result.tool_calls) == 0:
-            report = result.content
 
         return {
             "messages": [result],

tradingagents/agents/analysts/social_media_analyst.py

@@ -2,6 +2,7 @@ from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 import time
 import json
 from tradingagents.agents.utils.news_data_tools import get_news
+from tradingagents.agents.utils.tool_runner import run_tool_loop
 from tradingagents.dataflows.config import get_config
 
 
@@ -43,12 +44,9 @@ def create_social_media_analyst(llm):
 
         chain = prompt | llm.bind_tools(tools)
 
-        result = chain.invoke(state["messages"])
+        result = run_tool_loop(chain, state["messages"], tools)
 
-        report = ""
+        report = result.content or ""
-
-        if len(result.tool_calls) == 0:
-            report = result.content
 
         return {
             "messages": [result],