added deepseek to the list of agents

.gitignore

@@ -6,3 +6,4 @@ src/
 eval_results/
 eval_data/
 *.egg-info/
+.env

README.md

@@ -103,7 +103,7 @@ cd TradingAgents
 
 Create a virtual environment in any of your favorite environment managers:
 ```bash
-conda create -n tradingagents python=3.13
+conda create -n tradingagents python=3.11
 conda activate tradingagents
 ```
 
@@ -119,9 +119,9 @@ You will also need the FinnHub API for financial data. All of our code is implem
 export FINNHUB_API_KEY=$YOUR_FINNHUB_API_KEY
 ```
 
-You will need the OpenAI API for all the agents.
+You will need the DeepSeek API for all the agents. By default, TradingAgents uses DeepSeek as the LLM provider.
 ```bash
-export OPENAI_API_KEY=$YOUR_OPENAI_API_KEY
+export DEEPSEEK_API_KEY=$YOUR_DEEPSEEK_API_KEY
 ```
 
 ### CLI Usage
@@ -150,7 +150,7 @@ An interface will appear showing results as they load, letting you track the age
 
 ### Implementation Details
 
-We built TradingAgents with LangGraph to ensure flexibility and modularity. We utilize `o1-preview` and `gpt-4o` as our deep thinking and fast thinking LLMs for our experiments. However, for testing purposes, we recommend you use `o4-mini` and `gpt-4.1-mini` to save on costs as our framework makes **lots of** API calls.
+We built TradingAgents with LangGraph to ensure flexibility and modularity. By default, we utilize `deepseek-chat` as our deep thinking and fast thinking LLMs. You can change the default models in `tradingagents/default_config.py`.
 
 ### Python Usage
 

cli/utils.py

@@ -144,6 +144,10 @@ def select_shallow_thinking_agent(provider) -> str:
             ("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
             ("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
         ],
+        "deepseek": [
+            ("DeepSeek Chat - Fast and efficient chat model", "deepseek-chat"),
+            ("DeepSeek Reasoner - Advanced reasoning model", "deepseek-reasoner"),
+        ],
         "openrouter": [
             ("Meta: Llama 4 Scout", "meta-llama/llama-4-scout:free"),
             ("Meta: Llama 3.3 8B Instruct - A lightweight and ultra-fast variant of Llama 3.3 70B", "meta-llama/llama-3.3-8b-instruct:free"),
@@ -198,7 +202,7 @@ def select_deep_thinking_agent(provider) -> str:
             ("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
             ("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
             ("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
-            ("Claude Opus 4 - Most powerful Anthropic model", "	claude-opus-4-0"),
+            ("Claude Opus 4 - Most powerful Anthropic model", "\tclaude-opus-4-0"),
         ],
         "google": [
             ("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
@@ -206,6 +210,10 @@ def select_deep_thinking_agent(provider) -> str:
             ("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
             ("Gemini 2.5 Pro", "gemini-2.5-pro-preview-06-05"),
         ],
+        "deepseek": [
+            ("DeepSeek Chat - Fast and efficient chat model", "deepseek-chat"),
+            ("DeepSeek Reasoner - Advanced reasoning model", "deepseek-reasoner"),
+        ],
         "openrouter": [
             ("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek/deepseek-chat-v3-0324:free"),
             ("Deepseek - latest iteration of the flagship chat model family from the DeepSeek team.", "deepseek/deepseek-chat-v3-0324:free"),
@@ -238,12 +246,13 @@ def select_deep_thinking_agent(provider) -> str:
     return choice
 
 def select_llm_provider() -> tuple[str, str]:
-    """Select the OpenAI api url using interactive selection."""
-    # Define OpenAI api options with their corresponding endpoints
+    """Select the LLM provider using interactive selection."""
+    # Define LLM provider options with their corresponding endpoints
     BASE_URLS = [
         ("OpenAI", "https://api.openai.com/v1"),
         ("Anthropic", "https://api.anthropic.com/"),
         ("Google", "https://generativelanguage.googleapis.com/v1"),
+        ("DeepSeek", "https://api.deepseek.com"),
         ("Openrouter", "https://openrouter.ai/api/v1"),
         ("Ollama", "http://localhost:11434/v1"),        
     ]
@@ -265,10 +274,10 @@ def select_llm_provider() -> tuple[str, str]:
     ).ask()
     
     if choice is None:
-        console.print("\n[red]no OpenAI backend selected. Exiting...[/red]")
+        console.print("\n[red]no LLM provider selected. Exiting...[/red]")
         exit(1)
     
     display_name, url = choice
     print(f"You selected: {display_name}\tURL: {url}")
     
-    return display_name, url
+    return display_name, url

main.py

@@ -1,12 +1,16 @@
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from tradingagents.graph.trading_graph import TradingAgentsGraph
 from tradingagents.default_config import DEFAULT_CONFIG
 
 # 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"] = "deepseek"  # Use a different model
+config["backend_url"] = "https://api.deepseek.com"  # Use a different backend
+config["deep_think_llm"] = "deepseek-chat"  # Use a different model
+config["quick_think_llm"] = "deepseek-chat"  # Use a different model
 config["max_debate_rounds"] = 1  # Increase debate rounds
 config["online_tools"] = True  # Increase debate rounds
 

requirements.txt

@@ -1,5 +1,8 @@
 typing-extensions
 langchain-openai
+langchain-anthropic
+langchain-google-genai
+langchain-deepseek
 langchain-experimental
 pandas
 yfinance
@@ -21,4 +24,5 @@ pytz
 redis
 chainlit
 rich
-questionary
+typer
+questionary

tradingagents/agents/utils/memory.py

@@ -1,25 +1,40 @@
 import chromadb
 from chromadb.config import Settings
 from openai import OpenAI
+import os
+from sentence_transformers import SentenceTransformer
 
 
 class FinancialSituationMemory:
     def __init__(self, name, config):
-        if config["backend_url"] == "http://localhost:11434/v1":
-            self.embedding = "nomic-embed-text"
+        if config["llm_provider"].lower() == "deepseek":
+            self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
         else:
-            self.embedding = "text-embedding-3-small"
-            self.client = OpenAI()
+            if config["backend_url"] == "http://localhost:11434/v1":
+                self.embedding = "nomic-embed-text"
+            else:
+                self.embedding = "text-embedding-3-small"
+
+            if config["llm_provider"].lower() == "deepseek":
+                self.client = OpenAI(
+                    api_key=os.getenv("DEEPSEEK_API_KEY"),
+                    base_url=config["backend_url"]
+                )
+            else:
+                self.client = OpenAI()
+
         self.chroma_client = chromadb.Client(Settings(allow_reset=True))
         self.situation_collection = self.chroma_client.create_collection(name=name)
 
     def get_embedding(self, text):
-        """Get OpenAI embedding for a text"""
-        
-        response = self.client.embeddings.create(
-            model=self.embedding, input=text
-        )
-        return response.data[0].embedding
+        """Get embedding for a text"""
+        if hasattr(self, 'embedding_model'):
+            return self.embedding_model.encode(text).tolist()
+        else:
+            response = self.client.embeddings.create(
+                model=self.embedding, input=text
+            )
+            return response.data[0].embedding
 
     def add_situations(self, situations_and_advice):
         """Add financial situations and their corresponding advice. Parameter is a list of tuples (situation, rec)"""
@@ -110,4 +125,4 @@ if __name__ == "__main__":
             print(f"Recommendation: {rec['recommendation']}")
 
     except Exception as e:
-        print(f"Error during recommendation: {str(e)}")
+        print(f"Error during recommendation: {str(e)}")

tradingagents/default_config.py

@@ -2,16 +2,19 @@ import os
 
 DEFAULT_CONFIG = {
     "project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
-    "data_dir": "/Users/yluo/Documents/Code/ScAI/FR1-data",
+    "data_dir": os.path.join(
+        os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
+        "dataflows/data_cache",
+    ),
     "data_cache_dir": os.path.join(
         os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
         "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": "deepseek",
+    "deep_think_llm": "deepseek-chat",
+    "quick_think_llm": "deepseek-chat",
+    "backend_url": "https://api.deepseek.com",
     # 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_deepseek import ChatDeepSeek
 
 from langgraph.prebuilt import ToolNode
 
@@ -67,6 +68,10 @@ class TradingAgentsGraph:
         elif self.config["llm_provider"].lower() == "google":
             self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"])
             self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"])
+        elif self.config["llm_provider"].lower() == "deepseek":
+            deepseek_api_key = os.getenv("DEEPSEEK_API_KEY")
+            self.deep_thinking_llm = ChatDeepSeek(model=self.config["deep_think_llm"], base_url=self.config["backend_url"], api_key=deepseek_api_key)
+            self.quick_thinking_llm = ChatDeepSeek(model=self.config["quick_think_llm"], base_url=self.config["backend_url"], api_key=deepseek_api_key)
         else:
             raise ValueError(f"Unsupported LLM provider: {self.config['llm_provider']}")
         
@@ -251,4 +256,4 @@ class TradingAgentsGraph:
 
     def process_signal(self, full_signal):
         """Process a signal to extract the core decision."""
-        return self.signal_processor.process_signal(full_signal)
+        return self.signal_processor.process_signal(full_signal)