feat: support for ollama users who run their models locally.

main.py

@@ -3,10 +3,11 @@ 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"] = "ollama"  # Use a different model
+config["backend_url"] = "http://localhost:11434"  # Use a different backend
+config["deep_think_llm"] = "mixtral:8x7b-instruct-v0.1-q4_K_M"  # Use a different model
+config["quick_think_llm"] = "phi3:mini"  # Use a different model
+config["embedding_model"] = "fingpt:7b"  # Use a different embedding model
 config["max_debate_rounds"] = 1  # Increase debate rounds
 config["online_tools"] = True  # Increase debate rounds
 
@@ -14,7 +15,7 @@ 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("NVDA", "2025-07-07")
 print(decision)
 
 # Memorize mistakes and reflect

tradingagents/agents/__init__.py

@@ -1,6 +1,7 @@
 from .utils.agent_utils import Toolkit, create_msg_delete
 from .utils.agent_states import AgentState, InvestDebateState, RiskDebateState
 from .utils.memory import FinancialSituationMemory
+from .utils.safe_bind_tools import safe_bind_tools
 
 from .analysts.fundamentals_analyst import create_fundamentals_analyst
 from .analysts.market_analyst import create_market_analyst
@@ -21,6 +22,7 @@ from .trader.trader import create_trader
 
 __all__ = [
     "FinancialSituationMemory",
+    "safe_bind_tools",
     "Toolkit",
     "AgentState",
     "create_msg_delete",

tradingagents/agents/analysts/fundamentals_analyst.py

@@ -1,3 +1,4 @@
+from tradingagents.agents.utils.safe_bind_tools import safe_bind_tools
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 import time
 import json
@@ -47,7 +48,7 @@ def create_fundamentals_analyst(llm, toolkit):
         prompt = prompt.partial(current_date=current_date)
         prompt = prompt.partial(ticker=ticker)
 
-        chain = prompt | llm.bind_tools(tools)
+        chain = prompt | safe_bind_tools(llm, tools)
 
         result = chain.invoke(state["messages"])
 

tradingagents/agents/analysts/market_analyst.py

@@ -1,3 +1,4 @@
+from tradingagents.agents.utils.safe_bind_tools import safe_bind_tools
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 import time
 import json
@@ -72,7 +73,7 @@ Volume-Based Indicators:
         prompt = prompt.partial(current_date=current_date)
         prompt = prompt.partial(ticker=ticker)
 
-        chain = prompt | llm.bind_tools(tools)
+        chain = prompt | safe_bind_tools(llm, tools)
 
         result = chain.invoke(state["messages"])
 

tradingagents/agents/analysts/news_analyst.py

@@ -1,3 +1,4 @@
+from tradingagents.agents.utils.safe_bind_tools import safe_bind_tools
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 import time
 import json
@@ -44,7 +45,7 @@ def create_news_analyst(llm, toolkit):
         prompt = prompt.partial(current_date=current_date)
         prompt = prompt.partial(ticker=ticker)
 
-        chain = prompt | llm.bind_tools(tools)
+        chain = prompt | safe_bind_tools(llm, tools)
         result = chain.invoke(state["messages"])
 
         report = ""

tradingagents/agents/analysts/social_media_analyst.py

@@ -1,3 +1,4 @@
+from tradingagents.agents.utils.safe_bind_tools import safe_bind_tools
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 import time
 import json
@@ -43,7 +44,7 @@ def create_social_media_analyst(llm, toolkit):
         prompt = prompt.partial(current_date=current_date)
         prompt = prompt.partial(ticker=ticker)
 
-        chain = prompt | llm.bind_tools(tools)
+        chain = prompt | safe_bind_tools(llm, tools)
 
         result = chain.invoke(state["messages"])
 

tradingagents/agents/utils/memory.py

@@ -1,24 +1,29 @@
 import chromadb
 from chromadb.config import Settings
 from openai import OpenAI
-
+from langchain_ollama import OllamaEmbeddings
 
 class FinancialSituationMemory:
     def __init__(self, name, config):
-        if config["backend_url"] == "http://localhost:11434/v1":
-            self.embedding = "nomic-embed-text"
+        if config["backend_url"] == "http://localhost:11434":
+            self.embedding = OllamaEmbeddings(
+                model=config["embedding_model"],
+                base_url=config["backend_url"],  # Remove trailing slash
+            )
         else:
-            self.embedding = "text-embedding-3-small"
-        self.client = OpenAI(base_url=config["backend_url"])
+            self.embedding = config["embedding_model"]
+            self.client = OpenAI(base_url=config["backend_url"])
         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
-        )
+        try:
+            response = self.client.embeddings.create(
+                model=self.embedding, input=text
+            )
+        except AttributeError:
+            return self.embedding.embed_query(text)
         return response.data[0].embedding
 
     def add_situations(self, situations_and_advice):

tradingagents/agents/utils/safe_bind_tools.py

@@ -0,0 +1,82 @@
+"""
+safe_bind_tools.py
+────────────────────────────────────────────────────────
+Attach tool schemas only when the underlying LLM truly
+supports OpenAI-style function calling.
+
+• OpenAI / Anthropic / Google models  →  always attach
+• ChatOllama models                  →  attach **only**
+  if the Ollama tag contains `"tools": true`
+• All other cases                    →  silently fall
+  back to plain text reasoning
+"""
+
+from __future__ import annotations
+
+import logging
+import shlex
+import subprocess
+from typing import Any, Sequence
+
+from langchain_core.language_models.chat_models import BaseChatModel
+
+
+log = logging.getLogger(__name__)
+
+def _ollama_has_tools_flag(model_name: str) -> bool:
+    """
+    Return True iff `ollama show <model_name>` contains `"tools": true`.
+    If the command fails (e.g. Windows, sandbox), fall back to False.
+    """
+    try:
+        output = subprocess.check_output(
+            shlex.split(f"ollama show {model_name}"), text=True
+        )
+        return '"tools": true' in output
+    except (NotImplementedError, AttributeError) as e:
+        log.debug("Could not inspect model %s: %s", model_name, e)
+        return False
+
+def safe_bind_tools(
+    llm: BaseChatModel, tools: Sequence[dict[str, Any]]
+) -> BaseChatModel:
+    """
+    Attach `tools` to an LLM **only** if the model can actually handle them.
+    Otherwise, return the original LLM unchanged.
+
+    Parameters
+    ----------
+    llm
+        Any LangChain chat model instance.
+    tools
+        List of tool schemas compatible with OpenAI function calling.
+
+    Returns
+    -------
+    BaseChatModel
+        Either the bound LLM (when tool calling is available) or the
+        original LLM (fallback).
+    """
+    # LLM has no bind_tools method at all → nothing to do
+    if not hasattr(llm, "bind_tools"):
+        return llm
+
+    # Special-case ChatOllama: check the `"tools": true` tag first
+    if isinstance(llm, BaseChatModel) and not _ollama_has_tools_flag(llm.model):
+        log.info(
+            "[safe_bind_tools] Model %s lacks tools support -- skipping.",
+            llm.model,
+        )
+        return llm
+
+    # Generic path: try to bind; fall back gracefully on failure
+    try:
+        return llm.bind_tools(tools)
+    except (NotImplementedError, AttributeError) as e:
+        log.debug(
+            "[safe_bind_tools] bind_tools failed for %s: %s – "
+            "falling back to plain reasoning.",
+            llm.__class__.__name__,
+            e,
+        )
+        return llm

tradingagents/default_config.py

@@ -13,6 +13,7 @@ DEFAULT_CONFIG = {
     "deep_think_llm": "o4-mini",
     "quick_think_llm": "gpt-4o-mini",
     "backend_url": "https://api.openai.com/v1",
+    "embedding_model": "text-embedding-3-small",
     # Debate and discussion settings
     "max_debate_rounds": 1,
     "max_risk_discuss_rounds": 1,

tradingagents/graph/trading_graph.py

@@ -9,7 +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.chat_models import ChatOllama
 from langgraph.prebuilt import ToolNode
 
 from tradingagents.agents import *
@@ -58,7 +58,19 @@ 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.get("llm_provider") == "ollama":
+            self.deep_thinking_llm = ChatOllama(
+                model=self.config["deep_think_llm"],
+                base_url=self.config["backend_url"],
+                temperature=0.2,
+                gpu_layers=32,          # ← 這裡就能塞 Ollama 特有參數
+            )
+            self.quick_thinking_llm = ChatOllama(
+                model=self.config["quick_think_llm"],
+                base_url=self.config["backend_url"].rstrip("/v1"),  # Remove trailing slash
+                temperature=0.1,
+            )
+        elif self.config["llm_provider"].lower() == "openai" or self.config["llm_provider"] == "ollama" 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() == "anthropic":