ollama support

.gitignore

@@ -7,3 +7,5 @@ eval_results/
 eval_data/
 *.egg-info/
 .env
+*.log
+results

cli/utils.py

@@ -213,7 +213,7 @@ def select_deep_thinking_agent(provider) -> str:
         ],
         "ollama": [
             ("llama3.1 local", "llama3.1"),
-            ("qwen3", "qwen3"),
+            ("qwen3 local", "qwen3"),
         ]
     }
     

main.py

@@ -1,12 +1,15 @@
 from tradingagents.graph.trading_graph import TradingAgentsGraph
 from tradingagents.default_config import DEFAULT_CONFIG
+from dotenv import load_dotenv
+# Load environment variables
+load_dotenv()
 
 # Create a custom config
 config = DEFAULT_CONFIG.copy()
-config["llm_provider"] = "google"  # Use a different model
-config["backend_url"] = "https://generativelanguage.googleapis.com/v1"  # Use a different backend
-config["deep_think_llm"] = "gemini-2.0-flash"  # Use a different model
-config["quick_think_llm"] = "gemini-2.0-flash"  # Use a different model
+config["llm_provider"] = "ollama"  # Use a different model
+config["backend_url"] = "http://localhost:11434/v1"  # Use a different backend
+config["deep_think_llm"] = "llama3.2"  # Use a different model
+config["quick_think_llm"] = "llama3.2"  # Use a different model
 config["max_debate_rounds"] = 1  # Increase debate rounds
 config["online_tools"] = True  # Increase debate rounds
 
@@ -14,7 +17,7 @@ config["online_tools"] = True  # Increase debate rounds
 ta = TradingAgentsGraph(debug=True, config=config)
 
 # forward propagate
-_, decision = ta.propagate("NVDA", "2024-05-10")
+_, decision = ta.propagate("SPY", "2024-07-09")
 print(decision)
 
 # Memorize mistakes and reflect

requirements.txt

@@ -24,3 +24,4 @@ rich
 questionary
 langchain_anthropic
 langchain-google-genai
+langchain-ollama

tradingagents/default_config.py

@@ -9,10 +9,10 @@ DEFAULT_CONFIG = {
         "dataflows/data_cache",
     ),
     # LLM settings
-    "llm_provider": "openai",
-    "deep_think_llm": "o4-mini",
-    "quick_think_llm": "gpt-4o-mini",
-    "backend_url": "https://api.openai.com/v1",
+    "llm_provider": "ollama",
+    "deep_think_llm": "llama3.2",
+    "quick_think_llm": "llama3.2",
+    "backend_url": "http://localhost:11434/v1",
     # Debate and discussion settings
     "max_debate_rounds": 1,
     "max_risk_discuss_rounds": 1,

tradingagents/graph/trading_graph.py

@@ -9,6 +9,7 @@ from typing import Dict, Any, Tuple, List, Optional
 from langchain_openai import ChatOpenAI
 from langchain_anthropic import ChatAnthropic
 from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_ollama import ChatOllama
 
 from langgraph.prebuilt import ToolNode
 
@@ -58,9 +59,12 @@ class TradingAgentsGraph:
         )
 
         # Initialize LLMs
-        if self.config["llm_provider"].lower() == "openai" or self.config["llm_provider"] == "ollama" or self.config["llm_provider"] == "openrouter":
+        if self.config["llm_provider"].lower() == "openai" or self.config["llm_provider"] == "openrouter":
             self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], base_url=self.config["backend_url"])
             self.quick_thinking_llm = ChatOpenAI(model=self.config["quick_think_llm"], base_url=self.config["backend_url"])
+        elif self.config["llm_provider"].lower() == "ollama":
+            self.deep_thinking_llm = ChatOllama(model=self.config["deep_think_llm"])
+            self.quick_thinking_llm = ChatOllama(model=self.config["quick_think_llm"])
         elif self.config["llm_provider"].lower() == "anthropic":
             self.deep_thinking_llm = ChatAnthropic(model=self.config["deep_think_llm"], base_url=self.config["backend_url"])
             self.quick_thinking_llm = ChatAnthropic(model=self.config["quick_think_llm"], base_url=self.config["backend_url"])