diff --git a/python/third_party/README.md b/python/third_party/README.md
new file mode 100644
index 000000000..a67442827
--- /dev/null
+++ b/python/third_party/README.md
@@ -0,0 +1,14 @@
+# Deploy Third Party Agents
+
+## ai-hedge-fund
+
+```bash
+# ... install 
+cd ./ai-hedge-fund
+echo "uv: $(which uv)"
+echo "python: $(which python)"
+
+uv run -m adapter --env-file ${path_to_dotenv}
+# or simply
+bash launch_adapter.sh
+```
\ No newline at end of file
diff --git a/python/third_party/ai-hedge-fund/adapter/__main__.py b/python/third_party/ai-hedge-fund/adapter/__main__.py
index ab269d4c4..a69475ed8 100644
--- a/python/third_party/ai-hedge-fund/adapter/__main__.py
+++ b/python/third_party/ai-hedge-fund/adapter/__main__.py
@@ -1,28 +1,32 @@
 import asyncio
-import json
+import logging
 from datetime import datetime
 from typing import List
 
 from agno.agent import Agent
 from agno.models.openrouter import OpenRouter
 from dateutil.relativedelta import relativedelta
+from langchain_core.messages import HumanMessage
 from pydantic import BaseModel, Field, field_validator
 from valuecell.core.agent.decorator import create_wrapped_agent
 from valuecell.core.agent.types import BaseAgent
 
-from src.main import run_hedge_fund
+from src.main import create_workflow
 from src.utils.analysts import ANALYST_ORDER
+from src.utils.progress import progress
 
 allowed_analysts = set(
     key for display_name, key in sorted(ANALYST_ORDER, key=lambda x: x[1])
 )
 allowed_tickers = {"AAPL", "GOOGL", "MSFT", "NVDA", "TSLA"}
 
+logger = logging.getLogger(__name__)
+
 
 class HedgeFundRequest(BaseModel):
     tickers: List[str] = Field(
         ...,
-        description=f"List of stock tickers to analyze. Must be from: {allowed_tickers}",
+        description=f"List of stock tickers to analyze. Must be from: {allowed_tickers}. Otherwise, empty.",
     )
     selected_analysts: List[str] = Field(
         default=[],
@@ -32,6 +36,8 @@ class HedgeFundRequest(BaseModel):
     @field_validator("tickers")
     @classmethod
     def validate_tickers(cls, v):
+        if not v:
+            raise ValueError("No valid tickers are recognized.")
         invalid_tickers = set(v) - allowed_tickers
         if invalid_tickers:
             raise ValueError(
@@ -61,10 +67,13 @@ def __init__(self):
         )
 
     async def stream(self, query, session_id, task_id):
+        logger.info(f"Parsing query: {query}. Task ID: {task_id}, Session ID: {session_id}")
         run_response = self.agno_agent.run(
             f"Parse the following hedge fund analysis request and extract the parameters: {query}"
         )
         hedge_fund_request = run_response.content
+        if not isinstance(hedge_fund_request, HedgeFundRequest):
+            raise ValueError(f"Unable to parse query: {query}")
 
         end_date = datetime.now().strftime("%Y-%m-%d")
         end_date_obj = datetime.strptime(end_date, "%Y-%m-%d")
@@ -94,7 +103,10 @@ async def stream(self, query, session_id, task_id):
             },
         }
 
-        result = run_hedge_fund(
+        logger.info(
+            f"Start analyzing. Task ID: {task_id}, Session ID: {session_id}"
+        )
+        for stream_type, chunk in run_hedge_fund_stream(
             tickers=hedge_fund_request.tickers,
             start_date=start_date,
             end_date=end_date,
@@ -102,14 +114,63 @@ async def stream(self, query, session_id, task_id):
             model_name="openai/gpt-4o-mini",
             model_provider="OpenRouter",
             selected_analysts=hedge_fund_request.selected_analysts,
-        )
+        ):
+            if not isinstance(chunk, str):
+                continue
+            yield {
+                "content": chunk,
+                "is_task_complete": False,
+            }
 
         yield {
-            "content": json.dumps(result),
+            "content": "",
             "is_task_complete": True,
         }
 
 
+def run_hedge_fund_stream(
+    tickers: list[str],
+    start_date: str,
+    end_date: str,
+    portfolio: dict,
+    selected_analysts: list[str],
+    show_reasoning: bool = False,
+    model_name: str = "gpt-4.1",
+    model_provider: str = "OpenAI",
+):
+    # Start progress tracking
+    progress.start()
+
+    try:
+        # Create a new workflow if analysts are customized
+        workflow = create_workflow(selected_analysts)
+        _agent = workflow.compile()
+
+        inputs = {
+            "messages": [
+                HumanMessage(
+                    content="Make trading decisions based on the provided data.",
+                )
+            ],
+            "data": {
+                "tickers": tickers,
+                "portfolio": portfolio,
+                "start_date": start_date,
+                "end_date": end_date,
+                "analyst_signals": {},
+            },
+            "metadata": {
+                "show_reasoning": show_reasoning,
+                "model_name": model_name,
+                "model_provider": model_provider,
+            },
+        }
+        yield from _agent.stream(inputs, stream_mode=["custom", "messages"])
+    finally:
+        # Stop progress tracking
+        progress.stop()
+
+
 if __name__ == "__main__":
     agent = create_wrapped_agent(AIHedgeFundAgent)
     asyncio.run(agent.serve())
diff --git a/python/third_party/ai-hedge-fund/pyproject.toml b/python/third_party/ai-hedge-fund/pyproject.toml
index 9179f95ca..1a983579f 100644
--- a/python/third_party/ai-hedge-fund/pyproject.toml
+++ b/python/third_party/ai-hedge-fund/pyproject.toml
@@ -12,7 +12,7 @@ dependencies = [
     "langchain-openai>=0.3.5,<0.4",
     "langchain-deepseek>=0.1.2,<0.2",
     "langchain-ollama==0.3.6",
-    "langgraph==0.2.56",
+    "langgraph==0.3.34",
     "pandas>=2.1.0,<3",
     "numpy>=1.24.0,<2",
     "python-dotenv==1.0.0",
diff --git a/python/third_party/ai-hedge-fund/src/agents/aswath_damodaran.py b/python/third_party/ai-hedge-fund/src/agents/aswath_damodaran.py
index a1efbd268..b53a33e1c 100644
--- a/python/third_party/ai-hedge-fund/src/agents/aswath_damodaran.py
+++ b/python/third_party/ai-hedge-fund/src/agents/aswath_damodaran.py
@@ -7,6 +7,7 @@
 from src.graph.state import AgentState, show_agent_reasoning
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 
 from src.tools.api import (
     get_financial_metrics,
@@ -40,13 +41,16 @@ def aswath_damodaran_agent(state: AgentState, agent_id: str = "aswath_damodaran_
 
     analysis_data: dict[str, dict] = {}
     damodaran_signals: dict[str, dict] = {}
+    writer = get_stream_writer()
 
     for ticker in tickers:
         # ─── Fetch core data ────────────────────────────────────────────────────
         progress.update_status(agent_id, ticker, "Fetching financial metrics")
+        writer(f"Fetching financial metrics for {ticker}...\n")
         metrics = get_financial_metrics(ticker, end_date, period="ttm", limit=5, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Fetching financial line items")
+        writer(f"Fetching financial line items for {ticker}...\n")
         line_items = search_line_items(
             ticker,
             [
@@ -64,19 +68,24 @@ def aswath_damodaran_agent(state: AgentState, agent_id: str = "aswath_damodaran_
         )
 
         progress.update_status(agent_id, ticker, "Getting market cap")
+        writer(f"Getting market cap for {ticker}...\n")
         market_cap = get_market_cap(ticker, end_date, api_key=api_key)
 
         # ─── Analyses ───────────────────────────────────────────────────────────
         progress.update_status(agent_id, ticker, "Analyzing growth and reinvestment")
+        writer(f"Analyzing growth and reinvestment for {ticker}...\n")
         growth_analysis = analyze_growth_and_reinvestment(metrics, line_items)
 
         progress.update_status(agent_id, ticker, "Analyzing risk profile")
+        writer(f"Analyzing risk profile for {ticker}...\n")
         risk_analysis = analyze_risk_profile(metrics, line_items)
 
         progress.update_status(agent_id, ticker, "Calculating intrinsic value (DCF)")
+        writer(f"Calculating intrinsic value (DCF) for {ticker}...\n")
         intrinsic_val_analysis = calculate_intrinsic_value_dcf(metrics, line_items, risk_analysis)
 
         progress.update_status(agent_id, ticker, "Assessing relative valuation")
+        writer(f"Assessing relative valuation for {ticker}...\n")
         relative_val_analysis = analyze_relative_valuation(metrics)
 
         # ─── Score & margin of safety ──────────────────────────────────────────
@@ -114,6 +123,7 @@ def aswath_damodaran_agent(state: AgentState, agent_id: str = "aswath_damodaran_
 
         # ─── LLM: craft Damodaran-style narrative ──────────────────────────────
         progress.update_status(agent_id, ticker, "Generating Damodaran analysis")
+        writer(f"Generating Damodaran analysis for {ticker}...\n")
         damodaran_output = generate_damodaran_output(
             ticker=ticker,
             analysis_data=analysis_data,
@@ -124,6 +134,10 @@ def aswath_damodaran_agent(state: AgentState, agent_id: str = "aswath_damodaran_
         damodaran_signals[ticker] = damodaran_output.model_dump()
 
         progress.update_status(agent_id, ticker, "Done", analysis=damodaran_output.reasoning)
+        writer(
+            f"Analysis output: {damodaran_output.signal} with confidence {damodaran_output.confidence:.1%}\n"
+            f"{damodaran_output.reasoning}\n\n"
+        )
 
     # ─── Push message back to graph state ──────────────────────────────────────
     message = HumanMessage(content=json.dumps(damodaran_signals), name=agent_id)
diff --git a/python/third_party/ai-hedge-fund/src/agents/ben_graham.py b/python/third_party/ai-hedge-fund/src/agents/ben_graham.py
index 807175abb..d6b368212 100644
--- a/python/third_party/ai-hedge-fund/src/agents/ben_graham.py
+++ b/python/third_party/ai-hedge-fund/src/agents/ben_graham.py
@@ -2,6 +2,7 @@
 from src.tools.api import get_financial_metrics, get_market_cap, search_line_items
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 from pydantic import BaseModel
 import json
 from typing_extensions import Literal
@@ -32,25 +33,32 @@ def ben_graham_agent(state: AgentState, agent_id: str = "ben_graham_agent"):
     
     analysis_data = {}
     graham_analysis = {}
+    writer = get_stream_writer()
 
     for ticker in tickers:
         progress.update_status(agent_id, ticker, "Fetching financial metrics")
+        writer(f"Fetching financial metrics for {ticker}...\n")
         metrics = get_financial_metrics(ticker, end_date, period="annual", limit=10, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Gathering financial line items")
+        writer(f"Gathering financial line items for {ticker}...\n")
         financial_line_items = search_line_items(ticker, ["earnings_per_share", "revenue", "net_income", "book_value_per_share", "total_assets", "total_liabilities", "current_assets", "current_liabilities", "dividends_and_other_cash_distributions", "outstanding_shares"], end_date, period="annual", limit=10, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Getting market cap")
+        writer(f"Getting market cap for {ticker}...\n")
         market_cap = get_market_cap(ticker, end_date, api_key=api_key)
 
         # Perform sub-analyses
         progress.update_status(agent_id, ticker, "Analyzing earnings stability")
+        writer(f"Analyzing earnings stability for {ticker}...\n")
         earnings_analysis = analyze_earnings_stability(metrics, financial_line_items)
 
         progress.update_status(agent_id, ticker, "Analyzing financial strength")
+        writer(f"Analyzing financial strength for {ticker}...\n")
         strength_analysis = analyze_financial_strength(financial_line_items)
 
         progress.update_status(agent_id, ticker, "Analyzing Graham valuation")
+        writer(f"Analyzing Graham valuation for {ticker}...\n")
         valuation_analysis = analyze_valuation_graham(financial_line_items, market_cap)
 
         # Aggregate scoring
@@ -68,6 +76,7 @@ def ben_graham_agent(state: AgentState, agent_id: str = "ben_graham_agent"):
         analysis_data[ticker] = {"signal": signal, "score": total_score, "max_score": max_possible_score, "earnings_analysis": earnings_analysis, "strength_analysis": strength_analysis, "valuation_analysis": valuation_analysis}
 
         progress.update_status(agent_id, ticker, "Generating Ben Graham analysis")
+        writer(f"Generating Ben Graham analysis for {ticker}...\n")
         graham_output = generate_graham_output(
             ticker=ticker,
             analysis_data=analysis_data,
@@ -78,6 +87,10 @@ def ben_graham_agent(state: AgentState, agent_id: str = "ben_graham_agent"):
         graham_analysis[ticker] = {"signal": graham_output.signal, "confidence": graham_output.confidence, "reasoning": graham_output.reasoning}
 
         progress.update_status(agent_id, ticker, "Done", analysis=graham_output.reasoning)
+        writer(
+            f"Analysis output: {graham_output.signal} with confidence {graham_output.confidence:.1%}\n"
+            f"{graham_output.reasoning}\n\n"
+        )
 
     # Wrap results in a single message for the chain
     message = HumanMessage(content=json.dumps(graham_analysis), name=agent_id)
diff --git a/python/third_party/ai-hedge-fund/src/agents/bill_ackman.py b/python/third_party/ai-hedge-fund/src/agents/bill_ackman.py
index cc7aa27a7..b80f7e767 100644
--- a/python/third_party/ai-hedge-fund/src/agents/bill_ackman.py
+++ b/python/third_party/ai-hedge-fund/src/agents/bill_ackman.py
@@ -2,6 +2,7 @@
 from src.tools.api import get_financial_metrics, get_market_cap, search_line_items
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 from pydantic import BaseModel
 import json
 from typing_extensions import Literal
@@ -28,12 +29,15 @@ def bill_ackman_agent(state: AgentState, agent_id: str = "bill_ackman_agent"):
     api_key = get_api_key_from_state(state, "FINANCIAL_DATASETS_API_KEY")
     analysis_data = {}
     ackman_analysis = {}
+    writer = get_stream_writer()
     
     for ticker in tickers:
         progress.update_status(agent_id, ticker, "Fetching financial metrics")
+        writer(f"Fetching financial metrics for {ticker}...\n")
         metrics = get_financial_metrics(ticker, end_date, period="annual", limit=5, api_key=api_key)
         
         progress.update_status(agent_id, ticker, "Gathering financial line items")
+        writer(f"Gathering financial line items for {ticker}...\n")
         # Request multiple periods of data (annual or TTM) for a more robust long-term view.
         financial_line_items = search_line_items(
             ticker,
@@ -56,18 +60,23 @@ def bill_ackman_agent(state: AgentState, agent_id: str = "bill_ackman_agent"):
         )
         
         progress.update_status(agent_id, ticker, "Getting market cap")
+        writer(f"Getting market cap for {ticker}...\n")
         market_cap = get_market_cap(ticker, end_date, api_key=api_key)
         
         progress.update_status(agent_id, ticker, "Analyzing business quality")
+        writer(f"Analyzing business quality for {ticker}...\n")
         quality_analysis = analyze_business_quality(metrics, financial_line_items)
         
         progress.update_status(agent_id, ticker, "Analyzing balance sheet and capital structure")
+        writer(f"Analyzing balance sheet and capital structure for {ticker}...\n")
         balance_sheet_analysis = analyze_financial_discipline(metrics, financial_line_items)
         
         progress.update_status(agent_id, ticker, "Analyzing activism potential")
+        writer(f"Analyzing activism potential for {ticker}...\n")
         activism_analysis = analyze_activism_potential(financial_line_items)
         
         progress.update_status(agent_id, ticker, "Calculating intrinsic value & margin of safety")
+        writer(f"Calculating intrinsic value & margin of safety for {ticker}...\n")
         valuation_analysis = analyze_valuation(financial_line_items, market_cap)
         
         # Combine partial scores or signals
diff --git a/python/third_party/ai-hedge-fund/src/agents/cathie_wood.py b/python/third_party/ai-hedge-fund/src/agents/cathie_wood.py
index 1e6913900..e4589ee37 100644
--- a/python/third_party/ai-hedge-fund/src/agents/cathie_wood.py
+++ b/python/third_party/ai-hedge-fund/src/agents/cathie_wood.py
@@ -2,6 +2,7 @@
 from src.tools.api import get_financial_metrics, get_market_cap, search_line_items
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 from pydantic import BaseModel
 import json
 from typing_extensions import Literal
@@ -31,11 +32,18 @@ def cathie_wood_agent(state: AgentState, agent_id: str = "cathie_wood_agent"):
     analysis_data = {}
     cw_analysis = {}
 
+    writer = get_stream_writer()
+
     for ticker in tickers:
         progress.update_status(agent_id, ticker, "Fetching financial metrics")
+
+        writer(f"Fetching financial metrics for {ticker}...\n")
         metrics = get_financial_metrics(ticker, end_date, period="annual", limit=5, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Gathering financial line items")
+
+
+        writer(f"Gathering financial line items for {ticker}...\n")
         # Request multiple periods of data (annual or TTM) for a more robust view.
         financial_line_items = search_line_items(
             ticker,
@@ -60,15 +68,27 @@ def cathie_wood_agent(state: AgentState, agent_id: str = "cathie_wood_agent"):
         )
 
         progress.update_status(agent_id, ticker, "Getting market cap")
+
+
+        writer(f"Getting market cap for {ticker}...\n")
         market_cap = get_market_cap(ticker, end_date, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Analyzing disruptive potential")
+
+
+        writer(f"Analyzing for {ticker}...\n")
         disruptive_analysis = analyze_disruptive_potential(metrics, financial_line_items)
 
         progress.update_status(agent_id, ticker, "Analyzing innovation-driven growth")
+
+
+        writer(f"Analyzing for {ticker}...\n")
         innovation_analysis = analyze_innovation_growth(metrics, financial_line_items)
 
         progress.update_status(agent_id, ticker, "Calculating valuation & high-growth scenario")
+
+
+        writer(f"Calculating for {ticker}...\n")
         valuation_analysis = analyze_cathie_wood_valuation(financial_line_items, market_cap)
 
         # Combine partial scores or signals
@@ -85,6 +105,9 @@ def cathie_wood_agent(state: AgentState, agent_id: str = "cathie_wood_agent"):
         analysis_data[ticker] = {"signal": signal, "score": total_score, "max_score": max_possible_score, "disruptive_analysis": disruptive_analysis, "innovation_analysis": innovation_analysis, "valuation_analysis": valuation_analysis}
 
         progress.update_status(agent_id, ticker, "Generating Cathie Wood analysis")
+
+
+        writer(f"Generating for {ticker}...\n")
         cw_output = generate_cathie_wood_output(
             ticker=ticker,
             analysis_data=analysis_data,
diff --git a/python/third_party/ai-hedge-fund/src/agents/charlie_munger.py b/python/third_party/ai-hedge-fund/src/agents/charlie_munger.py
index 444f9cb56..0deab41b9 100644
--- a/python/third_party/ai-hedge-fund/src/agents/charlie_munger.py
+++ b/python/third_party/ai-hedge-fund/src/agents/charlie_munger.py
@@ -2,6 +2,7 @@
 from src.tools.api import get_financial_metrics, get_market_cap, search_line_items, get_insider_trades, get_company_news
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 from pydantic import BaseModel
 import json
 from typing_extensions import Literal
@@ -26,12 +27,15 @@ def charlie_munger_agent(state: AgentState, agent_id: str = "charlie_munger_agen
     api_key = get_api_key_from_state(state, "FINANCIAL_DATASETS_API_KEY")
     analysis_data = {}
     munger_analysis = {}
+    writer = get_stream_writer()
     
     for ticker in tickers:
         progress.update_status(agent_id, ticker, "Fetching financial metrics")
+        writer(f"Fetching financial metrics for {ticker}...\n")
         metrics = get_financial_metrics(ticker, end_date, period="annual", limit=10, api_key=api_key)  # Munger looks at longer periods
         
         progress.update_status(agent_id, ticker, "Gathering financial line items")
+        writer(f"Gathering financial line items for {ticker}...\n")
         financial_line_items = search_line_items(
             ticker,
             [
@@ -57,9 +61,11 @@ def charlie_munger_agent(state: AgentState, agent_id: str = "charlie_munger_agen
         )
         
         progress.update_status(agent_id, ticker, "Getting market cap")
+        writer(f"Getting market cap for {ticker}...\n")
         market_cap = get_market_cap(ticker, end_date, api_key=api_key)
         
         progress.update_status(agent_id, ticker, "Fetching insider trades")
+        writer(f"Fetching insider trades for {ticker}...\n")
         # Munger values management with skin in the game
         insider_trades = get_insider_trades(
             ticker,
@@ -71,6 +77,7 @@ def charlie_munger_agent(state: AgentState, agent_id: str = "charlie_munger_agen
         )
         
         progress.update_status(agent_id, ticker, "Fetching company news")
+        writer(f"Fetching company news for {ticker}...\n")
         # Munger avoids businesses with frequent negative press
         company_news = get_company_news(
             ticker,
@@ -82,15 +89,19 @@ def charlie_munger_agent(state: AgentState, agent_id: str = "charlie_munger_agen
         )
         
         progress.update_status(agent_id, ticker, "Analyzing moat strength")
+        writer(f"Analyzing moat strength for {ticker}...\n")
         moat_analysis = analyze_moat_strength(metrics, financial_line_items)
         
         progress.update_status(agent_id, ticker, "Analyzing management quality")
+        writer(f"Analyzing management quality for {ticker}...\n")
         management_analysis = analyze_management_quality(financial_line_items, insider_trades)
         
         progress.update_status(agent_id, ticker, "Analyzing business predictability")
+        writer(f"Analyzing business predictability for {ticker}...\n")
         predictability_analysis = analyze_predictability(financial_line_items)
         
         progress.update_status(agent_id, ticker, "Calculating Munger-style valuation")
+        writer(f"Calculating Munger-style valuation for {ticker}...\n")
         valuation_analysis = calculate_munger_valuation(financial_line_items, market_cap)
         
         # Combine partial scores with Munger's weighting preferences
@@ -125,6 +136,7 @@ def charlie_munger_agent(state: AgentState, agent_id: str = "charlie_munger_agen
         }
         
         progress.update_status(agent_id, ticker, "Generating Charlie Munger analysis")
+        writer(f"Generating Charlie Munger analysis for {ticker}...\n")
         munger_output = generate_munger_output(
             ticker=ticker, 
             analysis_data=analysis_data,
@@ -139,6 +151,10 @@ def charlie_munger_agent(state: AgentState, agent_id: str = "charlie_munger_agen
         }
         
         progress.update_status(agent_id, ticker, "Done", analysis=munger_output.reasoning)
+        writer(
+            f"Analysis output: {munger_output.signal} with confidence {munger_output.confidence:.1%}\n"
+            f"{munger_output.reasoning}\n\n"
+        )
     
     # Wrap results in a single message for the chain
     message = HumanMessage(
diff --git a/python/third_party/ai-hedge-fund/src/agents/fundamentals.py b/python/third_party/ai-hedge-fund/src/agents/fundamentals.py
index ab658eb44..789da58c5 100644
--- a/python/third_party/ai-hedge-fund/src/agents/fundamentals.py
+++ b/python/third_party/ai-hedge-fund/src/agents/fundamentals.py
@@ -1,4 +1,5 @@
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 from src.graph.state import AgentState, show_agent_reasoning
 from src.utils.api_key import get_api_key_from_state
 from src.utils.progress import progress
@@ -16,9 +17,11 @@ def fundamentals_analyst_agent(state: AgentState, agent_id: str = "fundamentals_
     api_key = get_api_key_from_state(state, "FINANCIAL_DATASETS_API_KEY")
     # Initialize fundamental analysis for each ticker
     fundamental_analysis = {}
+    writer = get_stream_writer()
 
     for ticker in tickers:
         progress.update_status(agent_id, ticker, "Fetching financial metrics")
+        writer(f"Fetching financial metrics for {ticker}...\n")
 
         # Get the financial metrics
         financial_metrics = get_financial_metrics(
@@ -31,6 +34,7 @@ def fundamentals_analyst_agent(state: AgentState, agent_id: str = "fundamentals_
 
         if not financial_metrics:
             progress.update_status(agent_id, ticker, "Failed: No financial metrics found")
+            writer(f"Failed to fetch financial metrics for {ticker}. Skipping...\n")
             continue
 
         # Pull the most recent financial metrics
@@ -41,6 +45,7 @@ def fundamentals_analyst_agent(state: AgentState, agent_id: str = "fundamentals_
         reasoning = {}
 
         progress.update_status(agent_id, ticker, "Analyzing profitability")
+        writer(f"Analyzing profitability for {ticker}...\n")
         # 1. Profitability Analysis
         return_on_equity = metrics.return_on_equity
         net_margin = metrics.net_margin
@@ -79,6 +84,7 @@ def fundamentals_analyst_agent(state: AgentState, agent_id: str = "fundamentals_
         }
 
         progress.update_status(agent_id, ticker, "Analyzing financial health")
+        writer(f"Analyzing financial health for {ticker}...\n")
         # 3. Financial Health
         current_ratio = metrics.current_ratio
         debt_to_equity = metrics.debt_to_equity
@@ -119,6 +125,7 @@ def fundamentals_analyst_agent(state: AgentState, agent_id: str = "fundamentals_
         }
 
         progress.update_status(agent_id, ticker, "Calculating final signal")
+        writer(f"Calculating final signal for {ticker}...\n")
         # Determine overall signal
         bullish_signals = signals.count("bullish")
         bearish_signals = signals.count("bearish")
@@ -141,6 +148,7 @@ def fundamentals_analyst_agent(state: AgentState, agent_id: str = "fundamentals_
         }
 
         progress.update_status(agent_id, ticker, "Done", analysis=json.dumps(reasoning, indent=4))
+        writer(f"Completed analysis for {ticker} with signal: {overall_signal} and confidence: {confidence:.1f}%\n\n")
 
     # Create the fundamental analysis message
     message = HumanMessage(
diff --git a/python/third_party/ai-hedge-fund/src/agents/michael_burry.py b/python/third_party/ai-hedge-fund/src/agents/michael_burry.py
index 80f6e6953..2da3a1a8c 100644
--- a/python/third_party/ai-hedge-fund/src/agents/michael_burry.py
+++ b/python/third_party/ai-hedge-fund/src/agents/michael_burry.py
@@ -7,6 +7,7 @@
 from src.graph.state import AgentState, show_agent_reasoning
 from langchain_core.messages import HumanMessage
 from langchain_core.prompts import ChatPromptTemplate
+from langgraph.config import get_stream_writer
 from pydantic import BaseModel
 
 from src.tools.api import (
@@ -41,15 +42,18 @@ def michael_burry_agent(state: AgentState, agent_id: str = "michael_burry_agent"
 
     analysis_data: dict[str, dict] = {}
     burry_analysis: dict[str, dict] = {}
+    writer = get_stream_writer()
 
     for ticker in tickers:
         # ------------------------------------------------------------------
         # Fetch raw data
         # ------------------------------------------------------------------
         progress.update_status(agent_id, ticker, "Fetching financial metrics")
+        writer(f"Fetching financial metrics for {ticker}...\n")
         metrics = get_financial_metrics(ticker, end_date, period="ttm", limit=5, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Fetching line items")
+        writer(f"Fetching line items for {ticker}...\n")
         line_items = search_line_items(
             ticker,
             [
@@ -67,12 +71,15 @@ def michael_burry_agent(state: AgentState, agent_id: str = "michael_burry_agent"
         )
 
         progress.update_status(agent_id, ticker, "Fetching insider trades")
+        writer(f"Fetching insider trades for {ticker}...\n")
         insider_trades = get_insider_trades(ticker, end_date=end_date, start_date=start_date)
 
         progress.update_status(agent_id, ticker, "Fetching company news")
+        writer(f"Fetching company news for {ticker}...\n")
         news = get_company_news(ticker, end_date=end_date, start_date=start_date, limit=250)
 
         progress.update_status(agent_id, ticker, "Fetching market cap")
+        writer(f"Fetching market cap for {ticker}...\n")
         market_cap = get_market_cap(ticker, end_date, api_key=api_key)
 
         # ------------------------------------------------------------------
diff --git a/python/third_party/ai-hedge-fund/src/agents/mohnish_pabrai.py b/python/third_party/ai-hedge-fund/src/agents/mohnish_pabrai.py
index a24d60874..7ebf6f3fd 100644
--- a/python/third_party/ai-hedge-fund/src/agents/mohnish_pabrai.py
+++ b/python/third_party/ai-hedge-fund/src/agents/mohnish_pabrai.py
@@ -2,6 +2,7 @@
 from src.tools.api import get_financial_metrics, get_market_cap, search_line_items
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 from pydantic import BaseModel
 import json
 from typing_extensions import Literal
@@ -25,14 +26,17 @@ def mohnish_pabrai_agent(state: AgentState, agent_id: str = "mohnish_pabrai_agen
 
     analysis_data: dict[str, any] = {}
     pabrai_analysis: dict[str, any] = {}
+    writer = get_stream_writer()
 
     # Pabrai focuses on: downside protection, simple business, moat via unit economics, FCF yield vs alternatives,
     # and potential for doubling in 2-3 years at low risk.
     for ticker in tickers:
         progress.update_status(agent_id, ticker, "Fetching financial metrics")
+        writer(f"Fetching financial metrics for {ticker}...\n")
         metrics = get_financial_metrics(ticker, end_date, period="annual", limit=8, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Gathering financial line items")
+        writer(f"Gathering financial line items for {ticker}...\n")
         line_items = search_line_items(
             ticker,
             [
@@ -63,15 +67,19 @@ def mohnish_pabrai_agent(state: AgentState, agent_id: str = "mohnish_pabrai_agen
         )
 
         progress.update_status(agent_id, ticker, "Getting market cap")
+        writer(f"Getting market cap for {ticker}...\n")
         market_cap = get_market_cap(ticker, end_date, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Analyzing downside protection")
+        writer(f"Analyzing downside protection for {ticker}...\n")
         downside = analyze_downside_protection(line_items)
 
         progress.update_status(agent_id, ticker, "Analyzing cash yield and valuation")
+        writer(f"Analyzing cash yield and valuation for {ticker}...\n")
         valuation = analyze_pabrai_valuation(line_items, market_cap)
 
         progress.update_status(agent_id, ticker, "Assessing potential to double")
+        writer(f"Assessing potential to double for {ticker}...\n")
         double_potential = analyze_double_potential(line_items, market_cap)
 
         # Combine to an overall score in spirit of Pabrai: heavily weight downside and cash yield
@@ -100,6 +108,7 @@ def mohnish_pabrai_agent(state: AgentState, agent_id: str = "mohnish_pabrai_agen
         }
 
         progress.update_status(agent_id, ticker, "Generating Pabrai analysis")
+        writer(f"Generating Pabrai analysis for {ticker}...\n")
         pabrai_output = generate_pabrai_output(
             ticker=ticker,
             analysis_data=analysis_data,
@@ -114,6 +123,7 @@ def mohnish_pabrai_agent(state: AgentState, agent_id: str = "mohnish_pabrai_agen
         }
 
         progress.update_status(agent_id, ticker, "Done", analysis=pabrai_output.reasoning)
+        writer(f"Analysis output: {pabrai_output.signal} with confidence {pabrai_output.confidence:.1%}\n{pabrai_output.reasoning}\n\n")
 
     message = HumanMessage(content=json.dumps(pabrai_analysis), name=agent_id)
 
diff --git a/python/third_party/ai-hedge-fund/src/agents/peter_lynch.py b/python/third_party/ai-hedge-fund/src/agents/peter_lynch.py
index e936a650a..8af889144 100644
--- a/python/third_party/ai-hedge-fund/src/agents/peter_lynch.py
+++ b/python/third_party/ai-hedge-fund/src/agents/peter_lynch.py
@@ -7,6 +7,7 @@
 )
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 from pydantic import BaseModel
 import json
 from typing_extensions import Literal
@@ -45,9 +46,11 @@ def peter_lynch_agent(state: AgentState, agent_id: str = "peter_lynch_agent"):
     api_key = get_api_key_from_state(state, "FINANCIAL_DATASETS_API_KEY")
     analysis_data = {}
     lynch_analysis = {}
+    writer = get_stream_writer()
 
     for ticker in tickers:
         progress.update_status(agent_id, ticker, "Gathering financial line items")
+        writer(f"Gathering financial line items for {ticker}...\n")
         # Relevant line items for Peter Lynch's approach
         financial_line_items = search_line_items(
             ticker,
@@ -72,28 +75,36 @@ def peter_lynch_agent(state: AgentState, agent_id: str = "peter_lynch_agent"):
         )
 
         progress.update_status(agent_id, ticker, "Getting market cap")
+        writer(f"Getting market cap for {ticker}...\n")
         market_cap = get_market_cap(ticker, end_date, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Fetching insider trades")
+        writer(f"Fetching insider trades for {ticker}...\n")
         insider_trades = get_insider_trades(ticker, end_date, limit=50, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Fetching company news")
+        writer(f"Fetching company news for {ticker}...\n")
         company_news = get_company_news(ticker, end_date, limit=50, api_key=api_key)
 
         # Perform sub-analyses:
         progress.update_status(agent_id, ticker, "Analyzing growth")
+        writer(f"Analyzing growth for {ticker}...\n")
         growth_analysis = analyze_lynch_growth(financial_line_items)
 
         progress.update_status(agent_id, ticker, "Analyzing fundamentals")
+        writer(f"Analyzing fundamentals for {ticker}...\n")
         fundamentals_analysis = analyze_lynch_fundamentals(financial_line_items)
 
         progress.update_status(agent_id, ticker, "Analyzing valuation (focus on PEG)")
+        writer(f"Analyzing valuation (focus on PEG) for {ticker}...\n")
         valuation_analysis = analyze_lynch_valuation(financial_line_items, market_cap)
 
         progress.update_status(agent_id, ticker, "Analyzing sentiment")
+        writer(f"Analyzing sentiment for {ticker}...\n")
         sentiment_analysis = analyze_sentiment(company_news)
 
         progress.update_status(agent_id, ticker, "Analyzing insider activity")
+        writer(f"Analyzing insider activity for {ticker}...\n")
         insider_activity = analyze_insider_activity(insider_trades)
 
         # Combine partial scores with weights typical for Peter Lynch:
@@ -129,6 +140,7 @@ def peter_lynch_agent(state: AgentState, agent_id: str = "peter_lynch_agent"):
         }
 
         progress.update_status(agent_id, ticker, "Generating Peter Lynch analysis")
+        writer(f"Generating Peter Lynch analysis for {ticker}...\n")
         lynch_output = generate_lynch_output(
             ticker=ticker,
             analysis_data=analysis_data[ticker],
@@ -143,6 +155,10 @@ def peter_lynch_agent(state: AgentState, agent_id: str = "peter_lynch_agent"):
         }
 
         progress.update_status(agent_id, ticker, "Done", analysis=lynch_output.reasoning)
+        writer(
+            f"Analysis output: {lynch_output.signal} with confidence {lynch_output.confidence:.1%}\n"
+            f"{lynch_output.reasoning}\n\n"
+        )
 
     # Wrap up results
     message = HumanMessage(content=json.dumps(lynch_analysis), name=agent_id)
diff --git a/python/third_party/ai-hedge-fund/src/agents/phil_fisher.py b/python/third_party/ai-hedge-fund/src/agents/phil_fisher.py
index 7dba42916..c42cc0eb3 100644
--- a/python/third_party/ai-hedge-fund/src/agents/phil_fisher.py
+++ b/python/third_party/ai-hedge-fund/src/agents/phil_fisher.py
@@ -7,6 +7,7 @@
 )
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 from pydantic import BaseModel
 import json
 from typing_extensions import Literal
@@ -39,9 +40,11 @@ def phil_fisher_agent(state: AgentState, agent_id: str = "phil_fisher_agent"):
     api_key = get_api_key_from_state(state, "FINANCIAL_DATASETS_API_KEY")
     analysis_data = {}
     fisher_analysis = {}
+    writer = get_stream_writer()
 
     for ticker in tickers:
         progress.update_status(agent_id, ticker, "Gathering financial line items")
+        writer(f"Gathering financial line items for {ticker}...\n")
         # Include relevant line items for Phil Fisher's approach:
         #   - Growth & Quality: revenue, net_income, earnings_per_share, R&D expense
         #   - Margins & Stability: operating_income, operating_margin, gross_margin
@@ -71,30 +74,39 @@ def phil_fisher_agent(state: AgentState, agent_id: str = "phil_fisher_agent"):
         )
 
         progress.update_status(agent_id, ticker, "Getting market cap")
+        writer(f"Getting market cap for {ticker}...\n")
         market_cap = get_market_cap(ticker, end_date, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Fetching insider trades")
+        writer(f"Fetching insider trades for {ticker}...\n")
         insider_trades = get_insider_trades(ticker, end_date, limit=50, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Fetching company news")
+        writer(f"Fetching company news for {ticker}...\n")
         company_news = get_company_news(ticker, end_date, limit=50, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Analyzing growth & quality")
+        writer(f"Analyzing growth & quality for {ticker}...\n")
         growth_quality = analyze_fisher_growth_quality(financial_line_items)
 
         progress.update_status(agent_id, ticker, "Analyzing margins & stability")
+        writer(f"Analyzing margins & stability for {ticker}...\n")
         margins_stability = analyze_margins_stability(financial_line_items)
 
         progress.update_status(agent_id, ticker, "Analyzing management efficiency & leverage")
+        writer(f"Analyzing management efficiency & leverage for {ticker}...\n")
         mgmt_efficiency = analyze_management_efficiency_leverage(financial_line_items)
 
         progress.update_status(agent_id, ticker, "Analyzing valuation (Fisher style)")
+        writer(f"Analyzing valuation (Fisher style) for {ticker}...\n")
         fisher_valuation = analyze_fisher_valuation(financial_line_items, market_cap)
 
         progress.update_status(agent_id, ticker, "Analyzing insider activity")
+        writer(f"Analyzing insider activity for {ticker}...\n")
         insider_activity = analyze_insider_activity(insider_trades)
 
         progress.update_status(agent_id, ticker, "Analyzing sentiment")
+        writer(f"Analyzing sentiment for {ticker}...\n")
         sentiment_analysis = analyze_sentiment(company_news)
 
         # Combine partial scores with weights typical for Fisher:
@@ -136,6 +148,7 @@ def phil_fisher_agent(state: AgentState, agent_id: str = "phil_fisher_agent"):
         }
 
         progress.update_status(agent_id, ticker, "Generating Phil Fisher-style analysis")
+        writer(f"Generating Phil Fisher-style analysis for {ticker}...\n")
         fisher_output = generate_fisher_output(
             ticker=ticker,
             analysis_data=analysis_data,
@@ -150,6 +163,7 @@ def phil_fisher_agent(state: AgentState, agent_id: str = "phil_fisher_agent"):
         }
 
         progress.update_status(agent_id, ticker, "Done", analysis=fisher_output.reasoning)
+        writer(f"Completed analysis for {ticker} with signal: {fisher_output.signal} and confidence: {fisher_output.confidence:.1%}\n\n")
 
     # Wrap results in a single message
     message = HumanMessage(content=json.dumps(fisher_analysis), name=agent_id)
diff --git a/python/third_party/ai-hedge-fund/src/agents/portfolio_manager.py b/python/third_party/ai-hedge-fund/src/agents/portfolio_manager.py
index 56159205a..50af4cf2e 100644
--- a/python/third_party/ai-hedge-fund/src/agents/portfolio_manager.py
+++ b/python/third_party/ai-hedge-fund/src/agents/portfolio_manager.py
@@ -1,5 +1,6 @@
 import json
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 from langchain_core.prompts import ChatPromptTemplate
 
 from src.graph.state import AgentState, show_agent_reasoning
@@ -28,6 +29,7 @@ def portfolio_management_agent(state: AgentState, agent_id: str = "portfolio_man
     portfolio = state["data"]["portfolio"]
     analyst_signals = state["data"]["analyst_signals"]
     tickers = state["data"]["tickers"]
+    writer = get_stream_writer()
 
     # Get position limits, current prices, and signals for every ticker
     position_limits = {}
@@ -67,6 +69,7 @@ def portfolio_management_agent(state: AgentState, agent_id: str = "portfolio_man
     state["data"]["current_prices"] = current_prices
 
     progress.update_status(agent_id, None, "Generating trading decisions")
+    writer("Generating trading decisions...\n")
 
     # Generate the trading decision
     result = generate_trading_decision(
@@ -90,6 +93,8 @@ def portfolio_management_agent(state: AgentState, agent_id: str = "portfolio_man
         show_agent_reasoning({ticker: decision.model_dump() for ticker, decision in result.decisions.items()}, "Portfolio Manager")
 
     progress.update_status(agent_id, None, "Done")
+    for ticker, decision in result.decisions.items():
+        writer(f"Decision for {ticker}:\naction: {decision.action}, quantity: {decision.quantity}, confidence: {decision.confidence}, reason: {decision.reasoning}\n")
 
     return {
         "messages": state["messages"] + [message],
diff --git a/python/third_party/ai-hedge-fund/src/agents/rakesh_jhunjhunwala.py b/python/third_party/ai-hedge-fund/src/agents/rakesh_jhunjhunwala.py
index d73b28191..f856e1ef9 100644
--- a/python/third_party/ai-hedge-fund/src/agents/rakesh_jhunjhunwala.py
+++ b/python/third_party/ai-hedge-fund/src/agents/rakesh_jhunjhunwala.py
@@ -1,6 +1,7 @@
 from src.graph.state import AgentState, show_agent_reasoning
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 from pydantic import BaseModel
 import json
 from typing_extensions import Literal
@@ -24,10 +25,14 @@ def rakesh_jhunjhunwala_agent(state: AgentState, agent_id: str = "rakesh_jhunjhu
     analysis_data = {}
     jhunjhunwala_analysis = {}
 
+    writer = get_stream_writer()
+
     for ticker in tickers:
 
         # Core Data
         progress.update_status(agent_id, ticker, "Fetching financial metrics")
+
+        writer(f"Fetching financial metrics for {ticker}...\n")
         metrics = get_financial_metrics(ticker, end_date, period="ttm", limit=5, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Fetching financial line items")
@@ -53,25 +58,45 @@ def rakesh_jhunjhunwala_agent(state: AgentState, agent_id: str = "rakesh_jhunjhu
         )
 
         progress.update_status(agent_id, ticker, "Getting market cap")
+
+
+        writer(f"Getting market cap for {ticker}...\n")
         market_cap = get_market_cap(ticker, end_date, api_key=api_key)
 
         # ─── Analyses ───────────────────────────────────────────────────────────
         progress.update_status(agent_id, ticker, "Analyzing growth")
+
+        writer(f"Analyzing for {ticker}...\n")
         growth_analysis = analyze_growth(financial_line_items)
 
         progress.update_status(agent_id, ticker, "Analyzing profitability")
+
+
+        writer(f"Analyzing for {ticker}...\n")
         profitability_analysis = analyze_profitability(financial_line_items)
         
         progress.update_status(agent_id, ticker, "Analyzing balance sheet")
+
+        
+        writer(f"Analyzing for {ticker}...\n")
         balancesheet_analysis = analyze_balance_sheet(financial_line_items)
         
         progress.update_status(agent_id, ticker, "Analyzing cash flow")
+
+        
+        writer(f"Analyzing for {ticker}...\n")
         cashflow_analysis = analyze_cash_flow(financial_line_items)
         
         progress.update_status(agent_id, ticker, "Analyzing management actions")
+
+        
+        writer(f"Analyzing for {ticker}...\n")
         management_analysis = analyze_management_actions(financial_line_items)
         
         progress.update_status(agent_id, ticker, "Calculating intrinsic value")
+
+        
+        writer(f"Calculating for {ticker}...\n")
         # Calculate intrinsic value once
         intrinsic_value = calculate_intrinsic_value(financial_line_items, market_cap)
 
@@ -136,6 +161,8 @@ def rakesh_jhunjhunwala_agent(state: AgentState, agent_id: str = "rakesh_jhunjhu
 
         # ─── LLM: craft Jhunjhunwala‑style narrative ──────────────────────────────
         progress.update_status(agent_id, ticker, "Generating Jhunjhunwala analysis")
+
+        writer(f"Generating for {ticker}...\n")
         jhunjhunwala_output = generate_jhunjhunwala_output(
             ticker=ticker,
             analysis_data=analysis_data[ticker],
diff --git a/python/third_party/ai-hedge-fund/src/agents/risk_manager.py b/python/third_party/ai-hedge-fund/src/agents/risk_manager.py
index fa05cd22d..1291d4af3 100644
--- a/python/third_party/ai-hedge-fund/src/agents/risk_manager.py
+++ b/python/third_party/ai-hedge-fund/src/agents/risk_manager.py
@@ -1,4 +1,5 @@
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 from src.graph.state import AgentState, show_agent_reasoning
 from src.utils.progress import progress
 from src.tools.api import get_prices, prices_to_df
@@ -23,9 +24,11 @@ def risk_management_agent(state: AgentState, agent_id: str = "risk_management_ag
 
     # First, fetch prices and calculate volatility for all relevant tickers
     all_tickers = set(tickers) | set(portfolio.get("positions", {}).keys())
+    writer = get_stream_writer()
     
     for ticker in all_tickers:
         progress.update_status(agent_id, ticker, "Fetching price data and calculating volatility")
+        writer(f"Fetching price data and calculating volatility for {ticker}...\n")
         
         prices = get_prices(
             ticker=ticker,
@@ -105,6 +108,7 @@ def risk_management_agent(state: AgentState, agent_id: str = "risk_management_ag
     # Calculate volatility- and correlation-adjusted risk limits for each ticker
     for ticker in tickers:
         progress.update_status(agent_id, ticker, "Calculating volatility- and correlation-adjusted limits")
+        writer(f"Calculating volatility- and correlation-adjusted limits for {ticker}...\n")
         
         if ticker not in current_prices or current_prices[ticker] <= 0:
             progress.update_status(agent_id, ticker, "Failed: No valid price data")
@@ -201,6 +205,7 @@ def risk_management_agent(state: AgentState, agent_id: str = "risk_management_ag
         )
 
     progress.update_status(agent_id, None, "Done")
+    writer("Risk management analysis complete.\n\n")
 
     message = HumanMessage(
         content=json.dumps(risk_analysis),
diff --git a/python/third_party/ai-hedge-fund/src/agents/sentiment.py b/python/third_party/ai-hedge-fund/src/agents/sentiment.py
index c5a6165d3..330f8eab0 100644
--- a/python/third_party/ai-hedge-fund/src/agents/sentiment.py
+++ b/python/third_party/ai-hedge-fund/src/agents/sentiment.py
@@ -1,4 +1,5 @@
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 from src.graph.state import AgentState, show_agent_reasoning
 from src.utils.progress import progress
 import pandas as pd
@@ -17,9 +18,11 @@ def sentiment_analyst_agent(state: AgentState, agent_id: str = "sentiment_analys
     api_key = get_api_key_from_state(state, "FINANCIAL_DATASETS_API_KEY")
     # Initialize sentiment analysis for each ticker
     sentiment_analysis = {}
+    writer = get_stream_writer()
 
     for ticker in tickers:
         progress.update_status(agent_id, ticker, "Fetching insider trades")
+        writer(f"Fetching insider trades for {ticker}...\n")
 
         # Get the insider trades
         insider_trades = get_insider_trades(
@@ -30,12 +33,14 @@ def sentiment_analyst_agent(state: AgentState, agent_id: str = "sentiment_analys
         )
 
         progress.update_status(agent_id, ticker, "Analyzing trading patterns")
+        writer(f"Analyzing trading patterns for {ticker}...\n")
 
         # Get the signals from the insider trades
         transaction_shares = pd.Series([t.transaction_shares for t in insider_trades]).dropna()
         insider_signals = np.where(transaction_shares < 0, "bearish", "bullish").tolist()
 
         progress.update_status(agent_id, ticker, "Fetching company news")
+        writer(f"Fetching company news for {ticker}...\n")
 
         # Get the company news
         company_news = get_company_news(ticker, end_date, limit=100, api_key=api_key)
@@ -46,6 +51,7 @@ def sentiment_analyst_agent(state: AgentState, agent_id: str = "sentiment_analys
                               np.where(sentiment == "positive", "bullish", "neutral")).tolist()
         
         progress.update_status(agent_id, ticker, "Combining signals")
+        writer(f"Combining signals for {ticker}...\n")
         # Combine signals from both sources with weights
         insider_weight = 0.3
         news_weight = 0.7
@@ -116,6 +122,7 @@ def sentiment_analyst_agent(state: AgentState, agent_id: str = "sentiment_analys
         }
 
         progress.update_status(agent_id, ticker, "Done", analysis=json.dumps(reasoning, indent=4))
+        writer(f"Completed sentiment analysis for {ticker} with signal: {overall_signal} and confidence: {confidence:.1f}%\n\n")
 
     # Create the sentiment message
     message = HumanMessage(
diff --git a/python/third_party/ai-hedge-fund/src/agents/stanley_druckenmiller.py b/python/third_party/ai-hedge-fund/src/agents/stanley_druckenmiller.py
index 073ce1d76..e2b19c0b6 100644
--- a/python/third_party/ai-hedge-fund/src/agents/stanley_druckenmiller.py
+++ b/python/third_party/ai-hedge-fund/src/agents/stanley_druckenmiller.py
@@ -9,6 +9,7 @@
 )
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 from pydantic import BaseModel
 import json
 from typing_extensions import Literal
@@ -41,11 +42,18 @@ def stanley_druckenmiller_agent(state: AgentState, agent_id: str = "stanley_druc
     analysis_data = {}
     druck_analysis = {}
 
+    writer = get_stream_writer()
+
     for ticker in tickers:
         progress.update_status(agent_id, ticker, "Fetching financial metrics")
+
+        writer(f"Fetching financial metrics for {ticker}...\n")
         metrics = get_financial_metrics(ticker, end_date, period="annual", limit=5, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Gathering financial line items")
+
+
+        writer(f"Gathering financial line items for {ticker}...\n")
         # Include relevant line items for Stan Druckenmiller's approach:
         #   - Growth & momentum: revenue, EPS, operating_income, ...
         #   - Valuation: net_income, free_cash_flow, ebit, ebitda
@@ -76,27 +84,48 @@ def stanley_druckenmiller_agent(state: AgentState, agent_id: str = "stanley_druc
         )
 
         progress.update_status(agent_id, ticker, "Getting market cap")
+
+
+        writer(f"Getting market cap for {ticker}...\n")
         market_cap = get_market_cap(ticker, end_date, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Fetching insider trades")
+
+
+        writer(f"Fetching insider trades for {ticker}...\n")
         insider_trades = get_insider_trades(ticker, end_date, limit=50, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Fetching company news")
+
+
+        writer(f"Fetching company news for {ticker}...\n")
         company_news = get_company_news(ticker, end_date, limit=50, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Fetching recent price data for momentum")
         prices = get_prices(ticker, start_date=start_date, end_date=end_date, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Analyzing growth & momentum")
+
+
+        writer(f"Analyzing growth & momentum for {ticker}...\n")
         growth_momentum_analysis = analyze_growth_and_momentum(financial_line_items, prices)
 
         progress.update_status(agent_id, ticker, "Analyzing sentiment")
+
+
+        writer(f"Analyzing sentiment for {ticker}...\n")
         sentiment_analysis = analyze_sentiment(company_news)
 
         progress.update_status(agent_id, ticker, "Analyzing insider activity")
+
+
+        writer(f"Analyzing insider activity for {ticker}...\n")
         insider_activity = analyze_insider_activity(insider_trades)
 
         progress.update_status(agent_id, ticker, "Analyzing risk-reward")
+
+
+        writer(f"Analyzing for {ticker}...\n")
         risk_reward_analysis = analyze_risk_reward(financial_line_items, prices)
 
         progress.update_status(agent_id, ticker, "Performing Druckenmiller-style valuation")
@@ -135,6 +164,9 @@ def stanley_druckenmiller_agent(state: AgentState, agent_id: str = "stanley_druc
         }
 
         progress.update_status(agent_id, ticker, "Generating Stanley Druckenmiller analysis")
+
+
+        writer(f"Generating for {ticker}...\n")
         druck_output = generate_druckenmiller_output(
             ticker=ticker,
             analysis_data=analysis_data,
diff --git a/python/third_party/ai-hedge-fund/src/agents/technicals.py b/python/third_party/ai-hedge-fund/src/agents/technicals.py
index 8bcdbd9ed..c05ebfbce 100644
--- a/python/third_party/ai-hedge-fund/src/agents/technicals.py
+++ b/python/third_party/ai-hedge-fund/src/agents/technicals.py
@@ -1,6 +1,7 @@
 import math
 
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 
 from src.graph.state import AgentState, show_agent_reasoning
 from src.utils.api_key import get_api_key_from_state
@@ -48,9 +49,11 @@ def technical_analyst_agent(state: AgentState, agent_id: str = "technical_analys
     api_key = get_api_key_from_state(state, "FINANCIAL_DATASETS_API_KEY")
     # Initialize analysis for each ticker
     technical_analysis = {}
+    writer = get_stream_writer()
 
     for ticker in tickers:
         progress.update_status(agent_id, ticker, "Analyzing price data")
+        writer(f"Analyzing price data for {ticker}...\n")
 
         # Get the historical price data
         prices = get_prices(
@@ -68,18 +71,23 @@ def technical_analyst_agent(state: AgentState, agent_id: str = "technical_analys
         prices_df = prices_to_df(prices)
 
         progress.update_status(agent_id, ticker, "Calculating trend signals")
+        writer(f"Calculating trend signals for {ticker}...\n")
         trend_signals = calculate_trend_signals(prices_df)
 
         progress.update_status(agent_id, ticker, "Calculating mean reversion")
+        writer(f"Calculating mean reversion for {ticker}...\n")
         mean_reversion_signals = calculate_mean_reversion_signals(prices_df)
 
         progress.update_status(agent_id, ticker, "Calculating momentum")
+        writer(f"Calculating momentum for {ticker}...\n")
         momentum_signals = calculate_momentum_signals(prices_df)
 
         progress.update_status(agent_id, ticker, "Analyzing volatility")
+        writer(f"Analyzing volatility for {ticker}...\n")
         volatility_signals = calculate_volatility_signals(prices_df)
 
         progress.update_status(agent_id, ticker, "Statistical analysis")
+        writer(f"Performing statistical analysis for {ticker}...\n")
         stat_arb_signals = calculate_stat_arb_signals(prices_df)
 
         # Combine all signals using a weighted ensemble approach
@@ -92,6 +100,7 @@ def technical_analyst_agent(state: AgentState, agent_id: str = "technical_analys
         }
 
         progress.update_status(agent_id, ticker, "Combining signals")
+        writer(f"Combining signals for {ticker}...\n")
         combined_signal = weighted_signal_combination(
             {
                 "trend": trend_signals,
@@ -136,6 +145,7 @@ def technical_analyst_agent(state: AgentState, agent_id: str = "technical_analys
             },
         }
         progress.update_status(agent_id, ticker, "Done", analysis=json.dumps(technical_analysis, indent=4))
+        writer(f"Completed analysis for {ticker} with signal: {combined_signal['signal']} and confidence: {combined_signal['confidence']:.1%}\n\n")
 
     # Create the technical analyst message
     message = HumanMessage(
diff --git a/python/third_party/ai-hedge-fund/src/agents/valuation.py b/python/third_party/ai-hedge-fund/src/agents/valuation.py
index 9f5f0fb33..cfbd21949 100644
--- a/python/third_party/ai-hedge-fund/src/agents/valuation.py
+++ b/python/third_party/ai-hedge-fund/src/agents/valuation.py
@@ -9,6 +9,7 @@
 import json
 import statistics
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 from src.graph.state import AgentState, show_agent_reasoning
 from src.utils.progress import progress
 from src.utils.api_key import get_api_key_from_state
@@ -26,9 +27,11 @@ def valuation_analyst_agent(state: AgentState, agent_id: str = "valuation_analys
     tickers = data["tickers"]
     api_key = get_api_key_from_state(state, "FINANCIAL_DATASETS_API_KEY")
     valuation_analysis: dict[str, dict] = {}
+    writer = get_stream_writer()
 
     for ticker in tickers:
         progress.update_status(agent_id, ticker, "Fetching financial data")
+        writer(f"Fetching financial data for {ticker}...\n")
 
         # --- Historical financial metrics ---
         financial_metrics = get_financial_metrics(
@@ -45,6 +48,7 @@ def valuation_analyst_agent(state: AgentState, agent_id: str = "valuation_analys
 
         # --- Enhanced line‑items ---
         progress.update_status(agent_id, ticker, "Gathering comprehensive line items")
+        writer(f"Gathering comprehensive line items for {ticker}...\n")
         line_items = search_line_items(
             ticker=ticker,
             line_items=[
@@ -87,6 +91,7 @@ def valuation_analyst_agent(state: AgentState, agent_id: str = "valuation_analys
 
         # Enhanced Discounted Cash Flow with WACC and scenarios
         progress.update_status(agent_id, ticker, "Calculating WACC and enhanced DCF")
+        writer(f"Calculating WACC and enhanced DCF for {ticker}...\n")
         
         # Calculate WACC
         wacc = calculate_wacc(
@@ -133,8 +138,10 @@ def valuation_analyst_agent(state: AgentState, agent_id: str = "valuation_analys
         # Aggregate & signal
         # ------------------------------------------------------------------
         market_cap = get_market_cap(ticker, end_date, api_key=api_key)
+        writer(f"Fetching market cap for {ticker}...\n")
         if not market_cap:
             progress.update_status(agent_id, ticker, "Failed: Market cap unavailable")
+            writer(f"Failed to fetch market cap for {ticker}\n")
             continue
 
         method_values = {
@@ -202,6 +209,7 @@ def valuation_analyst_agent(state: AgentState, agent_id: str = "valuation_analys
             "reasoning": reasoning,
         }
         progress.update_status(agent_id, ticker, "Done", analysis=json.dumps(reasoning, indent=4))
+        writer(f"Completed analysis for {ticker} with signal: {signal} and confidence: {confidence:.1%}\n")
 
     # ---- Emit message (for LLM tool chain) ----
     msg = HumanMessage(content=json.dumps(valuation_analysis), name=agent_id)
diff --git a/python/third_party/ai-hedge-fund/src/agents/warren_buffett.py b/python/third_party/ai-hedge-fund/src/agents/warren_buffett.py
index 34b11dc3b..8a675d8f8 100644
--- a/python/third_party/ai-hedge-fund/src/agents/warren_buffett.py
+++ b/python/third_party/ai-hedge-fund/src/agents/warren_buffett.py
@@ -1,6 +1,7 @@
 from src.graph.state import AgentState, show_agent_reasoning
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.messages import HumanMessage
+from langgraph.config import get_stream_writer
 from pydantic import BaseModel
 import json
 from typing_extensions import Literal
@@ -24,13 +25,16 @@ def warren_buffett_agent(state: AgentState, agent_id: str = "warren_buffett_agen
     # Collect all analysis for LLM reasoning
     analysis_data = {}
     buffett_analysis = {}
+    writer = get_stream_writer()
 
     for ticker in tickers:
         progress.update_status(agent_id, ticker, "Fetching financial metrics")
+        writer(f"Fetching financial metrics for {ticker}...\n")
         # Fetch required data - request more periods for better trend analysis
         metrics = get_financial_metrics(ticker, end_date, period="ttm", limit=10, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Gathering financial line items")
+        writer(f"Gathering financial line items for {ticker}...\n")
         financial_line_items = search_line_items(
             ticker,
             [
@@ -54,29 +58,37 @@ def warren_buffett_agent(state: AgentState, agent_id: str = "warren_buffett_agen
         )
 
         progress.update_status(agent_id, ticker, "Getting market cap")
+        writer(f"Getting market cap for {ticker}...\n")
         # Get current market cap
         market_cap = get_market_cap(ticker, end_date, api_key=api_key)
 
         progress.update_status(agent_id, ticker, "Analyzing fundamentals")
+        writer(f"Analyzing fundamentals for {ticker}...\n")
         # Analyze fundamentals
         fundamental_analysis = analyze_fundamentals(metrics)
 
         progress.update_status(agent_id, ticker, "Analyzing consistency")
+        writer(f"Analyzing consistency for {ticker}...\n")
         consistency_analysis = analyze_consistency(financial_line_items)
 
         progress.update_status(agent_id, ticker, "Analyzing competitive moat")
+        writer(f"Analyzing competitive moat for {ticker}...\n")
         moat_analysis = analyze_moat(metrics)
 
         progress.update_status(agent_id, ticker, "Analyzing pricing power")
+        writer(f"Analyzing pricing power for {ticker}...\n")
         pricing_power_analysis = analyze_pricing_power(financial_line_items, metrics)
 
         progress.update_status(agent_id, ticker, "Analyzing book value growth")
+        writer(f"Analyzing book value growth for {ticker}...\n")
         book_value_analysis = analyze_book_value_growth(financial_line_items)
 
         progress.update_status(agent_id, ticker, "Analyzing management quality")
+        writer(f"Analyzing management quality for {ticker}...\n")
         mgmt_analysis = analyze_management_quality(financial_line_items)
 
         progress.update_status(agent_id, ticker, "Calculating intrinsic value")
+        writer(f"Calculating intrinsic value for {ticker}...\n")
         intrinsic_value_analysis = calculate_intrinsic_value(financial_line_items)
 
         # Calculate total score without circle of competence (LLM will handle that)
@@ -121,6 +133,7 @@ def warren_buffett_agent(state: AgentState, agent_id: str = "warren_buffett_agen
         }
 
         progress.update_status(agent_id, ticker, "Generating Warren Buffett analysis")
+        writer(f"Generating Warren Buffett analysis for {ticker}...\n")
         buffett_output = generate_buffett_output(
             ticker=ticker,
             analysis_data=analysis_data,
@@ -136,6 +149,10 @@ def warren_buffett_agent(state: AgentState, agent_id: str = "warren_buffett_agen
         }
 
         progress.update_status(agent_id, ticker, "Done", analysis=buffett_output.reasoning)
+        writer(
+            f"Analysis output: {buffett_output.signal} with confidence {buffett_output.confidence:.1%}\n"
+            f"{buffett_output.reasoning}\n\n"
+        )
 
     # Create the message
     message = HumanMessage(content=json.dumps(buffett_analysis), name=agent_id)
diff --git a/python/third_party/ai-hedge-fund/uv.lock b/python/third_party/ai-hedge-fund/uv.lock
index 40e7fab18..ad16efa54 100644
--- a/python/third_party/ai-hedge-fund/uv.lock
+++ b/python/third_party/ai-hedge-fund/uv.lock
@@ -112,7 +112,7 @@ requires-dist = [
     { name = "langchain-ollama", specifier = "==0.3.6" },
     { name = "langchain-openai", specifier = ">=0.3.5,<0.4" },
     { name = "langchain-xai", specifier = ">=0.2.5,<0.3" },
-    { name = "langgraph", specifier = "==0.2.56" },
+    { name = "langgraph", specifier = "==0.3.34" },
     { name = "matplotlib", specifier = ">=3.9.2,<4" },
     { name = "numpy", specifier = ">=1.24.0,<2" },
     { name = "pandas", specifier = ">=2.1.0,<3" },
@@ -245,16 +245,16 @@ wheels = [
 
 [[package]]
 name = "alembic"
-version = "1.16.4"
+version = "1.16.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "mako" },
     { name = "sqlalchemy" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/83/52/72e791b75c6b1efa803e491f7cbab78e963695e76d4ada05385252927e76/alembic-1.16.4.tar.gz", hash = "sha256:efab6ada0dd0fae2c92060800e0bf5c1dc26af15a10e02fb4babff164b4725e2", size = 1968161, upload-time = "2025-07-10T16:17:20.192Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/9a/ca/4dc52902cf3491892d464f5265a81e9dff094692c8a049a3ed6a05fe7ee8/alembic-1.16.5.tar.gz", hash = "sha256:a88bb7f6e513bd4301ecf4c7f2206fe93f9913f9b48dac3b78babde2d6fe765e", size = 1969868, upload-time = "2025-08-27T18:02:05.668Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/c2/62/96b5217b742805236614f05904541000f55422a6060a90d7fd4ce26c172d/alembic-1.16.4-py3-none-any.whl", hash = "sha256:b05e51e8e82efc1abd14ba2af6392897e145930c3e0a2faf2b0da2f7f7fd660d", size = 247026, upload-time = "2025-07-10T16:17:21.845Z" },
+    { url = "https://files.pythonhosted.org/packages/39/4a/4c61d4c84cfd9befb6fa08a702535b27b21fff08c946bc2f6139decbf7f7/alembic-1.16.5-py3-none-any.whl", hash = "sha256:e845dfe090c5ffa7b92593ae6687c5cb1a101e91fa53868497dbd79847f9dbe3", size = 247355, upload-time = "2025-08-27T18:02:07.37Z" },
 ]
 
 [[package]]
@@ -268,7 +268,7 @@ wheels = [
 
 [[package]]
 name = "anthropic"
-version = "0.64.0"
+version = "0.67.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -279,22 +279,23 @@ dependencies = [
     { name = "sniffio" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/d8/4f/f2b880cba1a76f3acc7d5eb2ae217632eac1b8cef5ed3027493545c59eba/anthropic-0.64.0.tar.gz", hash = "sha256:3d496c91a63dff64f451b3e8e4b238a9640bf87b0c11d0b74ddc372ba5a3fe58", size = 427893, upload-time = "2025-08-13T17:09:49.915Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/09/08/ee91464cd821e6fca52d9a23be44815c95edd3c1cf1e844b2c5e85f0d57f/anthropic-0.67.0.tar.gz", hash = "sha256:d1531b210ea300c73423141d29bcee20fcd24ef9f426f6437c0a5d93fc98fb8e", size = 441639, upload-time = "2025-09-10T14:47:18.137Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a9/b2/2d268bcd5d6441df9dc0ebebc67107657edb8b0150d3fda1a5b81d1bec45/anthropic-0.64.0-py3-none-any.whl", hash = "sha256:6f5f7d913a6a95eb7f8e1bda4e75f76670e8acd8d4cd965e02e2a256b0429dd1", size = 297244, upload-time = "2025-08-13T17:09:47.908Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/9d/9adbda372710918cc8271d089a2ceae4d977a125f90bc3c4b456bca4f281/anthropic-0.67.0-py3-none-any.whl", hash = "sha256:f80a81ec1132c514215f33d25edeeab1c4691ad5361b391ebb70d528b0605b55", size = 317126, upload-time = "2025-09-10T14:47:16.351Z" },
 ]
 
 [[package]]
 name = "anyio"
-version = "3.7.1"
+version = "4.10.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "idna" },
     { name = "sniffio" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/28/99/2dfd53fd55ce9838e6ff2d4dac20ce58263798bd1a0dbe18b3a9af3fcfce/anyio-3.7.1.tar.gz", hash = "sha256:44a3c9aba0f5defa43261a8b3efb97891f2bd7d804e0e1f56419befa1adfc780", size = 142927, upload-time = "2023-07-05T16:45:02.294Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/f1/b4/636b3b65173d3ce9a38ef5f0522789614e590dab6a8d505340a4efe4c567/anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6", size = 213252, upload-time = "2025-08-04T08:54:26.451Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/19/24/44299477fe7dcc9cb58d0a57d5a7588d6af2ff403fdd2d47a246c91a3246/anyio-3.7.1-py3-none-any.whl", hash = "sha256:91dee416e570e92c64041bd18b900d1d6fa78dff7048769ce5ac5ddad004fbb5", size = 80896, upload-time = "2023-07-05T16:44:59.805Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/12/e5e0282d673bb9746bacfb6e2dba8719989d3660cdb2ea79aee9a9651afb/anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1", size = 107213, upload-time = "2025-08-04T08:54:24.882Z" },
 ]
 
 [[package]]
@@ -632,16 +633,16 @@ wheels = [
 
 [[package]]
 name = "fastapi"
-version = "0.115.14"
+version = "0.116.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pydantic" },
     { name = "starlette" },
     { name = "typing-extensions" },
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+
 [[package]]
 name = "yarl"
 version = "1.20.1"
diff --git a/python/valuecell/core/agent/decorator.py b/python/valuecell/core/agent/decorator.py
index f1f218fc3..328610d7e 100644
--- a/python/valuecell/core/agent/decorator.py
+++ b/python/valuecell/core/agent/decorator.py
@@ -199,7 +199,7 @@ async def _add_chunk(content: str, last: bool = False):
                     break
         except Exception as e:
             message = (
-                f"Error during {self.agent.__class__.__name__} agent execution : {e}"
+                f"Error during {self.agent.__class__.__name__} agent execution: {e}"
             )
             logger.error(message)
             await updater.update_status(
diff --git a/python/valuecell/examples/core_remote_agent_demo.py b/python/valuecell/examples/core_remote_agent_demo.py
index b95837b74..ed36eee6a 100644
--- a/python/valuecell/examples/core_remote_agent_demo.py
+++ b/python/valuecell/examples/core_remote_agent_demo.py
@@ -55,11 +55,11 @@ async def main():
             updated_info = connections.get_agent_info(agent_name)
             logger.info(f"Updated info for '{agent_name}': {updated_info}")
 
-            async for resp in await client.send_message(
-                "analyze apple stock with buffett and damodaran",
+            async for task, event in await client.send_message(
+                "analyze apple stock with buffett",
                 streaming=True,
             ):
-                logger.info(f"Response from {agent_name}: {resp}")
+                logger.info(f"Response from {agent_name}:\n{task}\n{event}")
 
         except Exception as e:
             logger.error(f"Failed to start {agent_name}: {e}")