Merge 90482e8b39 into 589b351f2a

.env.example

@@ -3,4 +3,5 @@ OPENAI_API_KEY=
 GOOGLE_API_KEY=
 ANTHROPIC_API_KEY=
 XAI_API_KEY=
+PERPLEXITY_API_KEY=
 OPENROUTER_API_KEY=

.gitignore

@@ -217,3 +217,7 @@ __marimo__/
 
 # Cache
 **/data_cache/
+
+reports/
+
+

cli/utils.py

@@ -162,6 +162,10 @@ def select_shallow_thinking_agent(provider) -> str:
             ("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
             ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
         ],
+        "perplexity": [
+            ("Sonar - Fast online search", "sonar"),
+            ("Sonar Pro - Advanced online search", "sonar-pro"),
+        ],
         "openrouter": [
             ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
             ("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
@@ -229,6 +233,11 @@ def select_deep_thinking_agent(provider) -> str:
             ("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
             ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
         ],
+        "perplexity": [
+            ("Sonar Pro - Advanced online search", "sonar-pro"),
+            ("Sonar Reasoning Pro - Advanced reasoning with search", "sonar-reasoning-pro"),
+            ("Sonar Deep Research - In-depth research", "sonar-deep-research"),
+        ],
         "openrouter": [
             ("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
             ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
@@ -270,6 +279,7 @@ def select_llm_provider() -> tuple[str, str]:
         ("Google", "https://generativelanguage.googleapis.com/v1"),
         ("Anthropic", "https://api.anthropic.com/"),
         ("xAI", "https://api.x.ai/v1"),
+        ("Perplexity", "https://api.perplexity.ai"),
         ("Openrouter", "https://openrouter.ai/api/v1"),
         ("Ollama", "http://localhost:11434/v1"),
     ]

tradingagents/agents/analysts/fundamentals_analyst.py

@@ -1,6 +1,6 @@
+from langchain_core.messages import HumanMessage
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
+
-import json
 from tradingagents.agents.utils.agent_utils import (
     build_instrument_context,
     get_balance_sheet,
@@ -8,6 +8,8 @@ from tradingagents.agents.utils.agent_utils import (
     get_fundamentals,
     get_income_statement,
     get_insider_transactions,
+    prefetch_tool_data,
+    supports_tool_calling,
 )
 from tradingagents.dataflows.config import get_config
 
@@ -52,13 +54,24 @@ def create_fundamentals_analyst(llm):
         prompt = prompt.partial(current_date=current_date)
         prompt = prompt.partial(instrument_context=instrument_context)
 
-        chain = prompt | llm.bind_tools(tools)
+        if supports_tool_calling():
-
+            chain = prompt | llm.bind_tools(tools)
-        result = chain.invoke(state["messages"])
+            result = chain.invoke(state["messages"])
+        else:
+            ticker = state["company_of_interest"]
+            tool_data = prefetch_tool_data(tools, [
+                {"ticker": ticker, "curr_date": current_date},
+                {"ticker": ticker, "freq": "quarterly", "curr_date": current_date},
+                {"ticker": ticker, "freq": "quarterly", "curr_date": current_date},
+                {"ticker": ticker, "freq": "quarterly", "curr_date": current_date},
+            ])
+            result = (prompt | llm).invoke([
+                HumanMessage(content=f"Analyze {ticker}.\n\nHere is the pre-fetched fundamental data:\n\n{tool_data}\n\nWrite your comprehensive report.")
+            ])
 
         report = ""
 
-        if len(result.tool_calls) == 0:
+        if not getattr(result, "tool_calls", None):
             report = result.content
 
         return {

tradingagents/agents/analysts/market_analyst.py

@@ -1,10 +1,14 @@
+from datetime import datetime, timedelta
+
+from langchain_core.messages import HumanMessage
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
+
-import json
 from tradingagents.agents.utils.agent_utils import (
     build_instrument_context,
     get_indicators,
     get_stock_data,
+    prefetch_tool_data,
+    supports_tool_calling,
 )
 from tradingagents.dataflows.config import get_config
 
@@ -71,13 +75,23 @@ Volume-Based Indicators:
         prompt = prompt.partial(current_date=current_date)
         prompt = prompt.partial(instrument_context=instrument_context)
 
-        chain = prompt | llm.bind_tools(tools)
+        if supports_tool_calling():
-
+            chain = prompt | llm.bind_tools(tools)
-        result = chain.invoke(state["messages"])
+            result = chain.invoke(state["messages"])
+        else:
+            ticker = state["company_of_interest"]
+            start_date = (datetime.strptime(current_date, "%Y-%m-%d") - timedelta(days=30)).strftime("%Y-%m-%d")
+            tool_data = prefetch_tool_data(tools, [
+                {"symbol": ticker, "start_date": start_date, "end_date": current_date},
+                {"symbol": ticker, "indicator": "rsi,macd,boll,boll_ub,boll_lb,atr,vwma,close_50_sma", "curr_date": current_date, "look_back_days": 30},
+            ])
+            result = (prompt | llm).invoke([
+                HumanMessage(content=f"Analyze {ticker}.\n\nHere is the pre-fetched market data:\n\n{tool_data}\n\nWrite your comprehensive report.")
+            ])
 
         report = ""
 
-        if len(result.tool_calls) == 0:
+        if not getattr(result, "tool_calls", None):
             report = result.content
 
         return {

tradingagents/agents/analysts/news_analyst.py

@@ -1,10 +1,14 @@
+from datetime import datetime, timedelta
+
+from langchain_core.messages import HumanMessage
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
+
-import json
 from tradingagents.agents.utils.agent_utils import (
     build_instrument_context,
     get_global_news,
     get_news,
+    prefetch_tool_data,
+    supports_tool_calling,
 )
 from tradingagents.dataflows.config import get_config
 
@@ -46,12 +50,23 @@ def create_news_analyst(llm):
         prompt = prompt.partial(current_date=current_date)
         prompt = prompt.partial(instrument_context=instrument_context)
 
-        chain = prompt | llm.bind_tools(tools)
+        if supports_tool_calling():
-        result = chain.invoke(state["messages"])
+            chain = prompt | llm.bind_tools(tools)
+            result = chain.invoke(state["messages"])
+        else:
+            ticker = state["company_of_interest"]
+            start_date = (datetime.strptime(current_date, "%Y-%m-%d") - timedelta(days=7)).strftime("%Y-%m-%d")
+            tool_data = prefetch_tool_data(tools, [
+                {"ticker": ticker, "start_date": start_date, "end_date": current_date},
+                {"curr_date": current_date, "look_back_days": 7, "limit": 5},
+            ])
+            result = (prompt | llm).invoke([
+                HumanMessage(content=f"Analyze {ticker}.\n\nHere is the pre-fetched news data:\n\n{tool_data}\n\nWrite your comprehensive report.")
+            ])
 
         report = ""
 
-        if len(result.tool_calls) == 0:
+        if not getattr(result, "tool_calls", None):
             report = result.content
 
         return {

tradingagents/agents/analysts/social_media_analyst.py

@@ -1,7 +1,14 @@
+from datetime import datetime, timedelta
+
+from langchain_core.messages import HumanMessage
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
+
-import json
+from tradingagents.agents.utils.agent_utils import (
-from tradingagents.agents.utils.agent_utils import build_instrument_context, get_news
+    build_instrument_context,
+    get_news,
+    prefetch_tool_data,
+    supports_tool_calling,
+)
 from tradingagents.dataflows.config import get_config
 
 
@@ -41,13 +48,22 @@ def create_social_media_analyst(llm):
         prompt = prompt.partial(current_date=current_date)
         prompt = prompt.partial(instrument_context=instrument_context)
 
-        chain = prompt | llm.bind_tools(tools)
+        if supports_tool_calling():
-
+            chain = prompt | llm.bind_tools(tools)
-        result = chain.invoke(state["messages"])
+            result = chain.invoke(state["messages"])
+        else:
+            ticker = state["company_of_interest"]
+            start_date = (datetime.strptime(current_date, "%Y-%m-%d") - timedelta(days=7)).strftime("%Y-%m-%d")
+            tool_data = prefetch_tool_data(tools, [
+                {"ticker": ticker, "start_date": start_date, "end_date": current_date},
+            ])
+            result = (prompt | llm).invoke([
+                HumanMessage(content=f"Analyze {ticker}.\n\nHere is the pre-fetched social media and news data:\n\n{tool_data}\n\nWrite your comprehensive report.")
+            ])
 
         report = ""
 
-        if len(result.tool_calls) == 0:
+        if not getattr(result, "tool_calls", None):
             report = result.content
 
         return {

tradingagents/agents/utils/agent_utils.py

@@ -1,5 +1,32 @@
 from langchain_core.messages import HumanMessage, RemoveMessage
 
+from tradingagents.dataflows.config import get_config
+
+# Providers whose APIs do not support tool/function calling.
+_NO_TOOL_CALLING_PROVIDERS = frozenset({"perplexity"})
+
+
+def supports_tool_calling() -> bool:
+    """Check if the current LLM provider supports tool calling."""
+    config = get_config()
+    return config.get("llm_provider", "openai").lower() not in _NO_TOOL_CALLING_PROVIDERS
+
+
+def prefetch_tool_data(tools, tool_args_list) -> str:
+    """Pre-call tools and return formatted results for prompt injection.
+
+    Used as a fallback for providers that don't support tool calling.
+    """
+    results = []
+    for tool, args in zip(tools, tool_args_list):
+        try:
+            data = tool.invoke(args)
+            results.append(f"=== {tool.name} ===\n{data}")
+        except Exception as e:
+            results.append(f"=== {tool.name} ===\n[Error fetching data: {e}]")
+    return "\n\n".join(results)
+
+
 # Import tools from separate utility files
 from tradingagents.agents.utils.core_stock_tools import (
     get_stock_data

tradingagents/llm_clients/factory.py

@@ -15,7 +15,7 @@ def create_llm_client(
     """Create an LLM client for the specified provider.
 
     Args:
-        provider: LLM provider (openai, anthropic, google, xai, ollama, openrouter)
+        provider: LLM provider (openai, anthropic, google, xai, perplexity, ollama, openrouter)
         model: Model name/identifier
         base_url: Optional base URL for API endpoint
         **kwargs: Additional provider-specific arguments
@@ -37,8 +37,8 @@ def create_llm_client(
     if provider_lower in ("openai", "ollama", "openrouter"):
         return OpenAIClient(model, base_url, provider=provider_lower, **kwargs)
 
-    if provider_lower == "xai":
+    if provider_lower in ("xai", "perplexity"):
-        return OpenAIClient(model, base_url, provider="xai", **kwargs)
+        return OpenAIClient(model, base_url, provider=provider_lower, **kwargs)
 
     if provider_lower == "anthropic":
         return AnthropicClient(model, base_url, **kwargs)

tradingagents/llm_clients/openai_client.py

@@ -27,13 +27,14 @@ _PASSTHROUGH_KWARGS = (
 # Provider base URLs and API key env vars
 _PROVIDER_CONFIG = {
     "xai": ("https://api.x.ai/v1", "XAI_API_KEY"),
+    "perplexity": ("https://api.perplexity.ai", "PERPLEXITY_API_KEY"),
     "openrouter": ("https://openrouter.ai/api/v1", "OPENROUTER_API_KEY"),
     "ollama": ("http://localhost:11434/v1", None),
 }
 
 
 class OpenAIClient(BaseLLMClient):
-    """Client for OpenAI, Ollama, OpenRouter, and xAI providers.
+    """Client for OpenAI, Ollama, OpenRouter, xAI, and Perplexity providers.
 
     For native OpenAI models, uses the Responses API (/v1/responses) which
     supports reasoning_effort with function tools across all model families

tradingagents/llm_clients/validators.py

@@ -48,6 +48,15 @@ VALID_MODELS = {
         "grok-4-fast-reasoning",
         "grok-4-fast-non-reasoning",
     ],
+    "perplexity": [
+        # Sonar Pro series
+        "sonar-pro",
+        "sonar-reasoning-pro",
+        # Sonar series
+        "sonar",
+        # Deep Research
+        "sonar-deep-research",
+    ],
 }