Cant get crewAI run with ollama running on an external server

CrewAI Community Support
1

I have been using gemini api’s uptil now to run my agents
For a change i tried shifting to ollama , which resides on an external server in the same network
Below is my code , the problem is that i keep getting connection errors , and have tried everything to fix it , mind it that ollama running on the local computer works charms , and also i can access the remote ollama model on openwebui easily
So there is no apparent blocker in between

from crewai import Agent, Task, Crew , Process ,LLM
import os
from typing import List, Dict
from pydantic import BaseModel, Field
from langchain_community.tools import DuckDuckGoSearchRun
from crewai.tools import BaseTool
from crewai_tools import ScrapeWebsiteTool
import json
from pprint import pprint

LLM for agents

gemini_llm = LLM(

model=“openai/gemini-2.0-flash-exp”,

temperature=0.7,

base_url=“”,

api_key=

)

# reasoning LLm for manager

gemini_thinking_llm = LLM(

model=“openai/gemini-2.0-flash-thinking-exp”,

temperature=0.7,

base_url=“/”,

api_key=

)

gemini_llm = LLM(
model=“ollama/hf.co/benhaotang/Rombos-Coder-V2.5-Qwen-7b-GGUF_cline:latest”,
temperature=0.7,
base_url=“182.31.22.19:11434”,(removing the https since the forum doesnt allow me to post)
)

Search tool init

class SearchToolInput(BaseModel):
query: str = Field(…, description=“Search query string”)

class SearchTool(BaseTool):
name: str = “web_search”
description: str = “Search for real-time information about companies and industries”
args_schema = SearchToolInput

def _run(self, query: str) -> str:
    search = DuckDuckGoSearchRun(region="pt-br")
    return search.run(query)

tool initialization

search_tool = SearchTool()
scrape_tool = ScrapeWebsiteTool()

creating agents

data_analyst_agent = Agent(
role=“Data Analyst”,
goal="Monitor and analyze market data in real-time "
“to identify trends and predict market movements.”,
backstory="Specializing in financial markets, this agent "
"uses statistical modeling and machine learning "
"to provide crucial insights. With a knack for data, "
"the Data Analyst Agent is the cornerstone for "
“informing trading decisions.”,
verbose=True,
allow_delegation=True,
tools = [scrape_tool, search_tool],
llm=gemini_llm
)

Take a carefull look of how the below agent takes into consideration the analysis from the previous agent

trading_strategy_agent = Agent(
role=“Trading Strategy Developer”,
goal="Develop and test various trading strategies based "
“on insights from the Data Analyst Agent.”,
backstory="Equipped with a deep understanding of financial "
"markets and quantitative analysis, this agent "
"devises and refines trading strategies. It evaluates "
"the performance of different approaches to determine "
“the most profitable and risk-averse options.”,
verbose=True,
allow_delegation=True,
tools = [scrape_tool, search_tool],
llm=gemini_llm
)
execution_agent = Agent(
role=“Trade Advisor”,
goal="Suggest optimal trade execution strategies "
“based on approved trading strategies.”,
backstory="This agent specializes in analyzing the timing, price, "
"and logistical details of potential trades. By evaluating "
"these factors, it provides well-founded suggestions for "
"when and how trades should be executed to maximize "
“efficiency and adherence to strategy.”,
verbose=True,
allow_delegation=True,
tools = [scrape_tool, search_tool],
llm=gemini_llm
)
risk_management_agent = Agent(
role=“Risk Advisor”,
goal="Evaluate and provide insights on the risks "
“associated with potential trading activities.”,
backstory="Armed with a deep understanding of risk assessment models "
"and market dynamics, this agent scrutinizes the potential "
"risks of proposed trades. It offers a detailed analysis of "
"risk exposure and suggests safeguards to ensure that "
“trading activities align with the firm’s risk tolerance.”,
verbose=True,
allow_delegation=True,
tools = [scrape_tool, search_tool],
llm=gemini_llm
)

Tasks

Task for Data Analyst Agent: Analyze Market Data

data_analysis_task = Task(
description=(
"Continuously monitor and analyze market data for "
"the selected stock ({stock_selection}). "
"Use statistical modeling and machine learning to "
“identify trends and predict market movements.”
),
expected_output=(
"Insights and alerts about significant market "
“opportunities or threats for {stock_selection}.”
),
output_file=“analysis_results.txt”,
agent=data_analyst_agent,
)

Task for Trading Strategy Agent: Develop Trading Strategies

see how the output of data analyst agent is given to the trade strat agent in words

strategy_development_task = Task(
description=(
"Develop and refine trading strategies based on "
"the insights from the Data Analyst and "
"user-defined risk tolerance ({risk_tolerance}). "
“Consider trading preferences ({trading_strategy_preference}).”
),
expected_output=(
"A set of potential trading strategies for {stock_selection} "
“that align with the user’s risk tolerance.”
),
output_file=“strategy.md”,
agent=trading_strategy_agent,
)

Task for Trade Advisor Agent: Plan Trade Execution

execution_planning_task = Task(
description=(
"Analyze approved trading strategies to determine the "
"best execution methods for {stock_selection}, "
“considering current market conditions and optimal pricing.”
),
expected_output=(
"Detailed execution plans suggesting how and when to "
“execute trades for {stock_selection}.”
),
output_file=“execution_plans.md”,
agent=execution_agent,
)

Task for Risk Advisor Agent: Assess Trading Risks

risk_assessment_task = Task(
description=(
"Evaluate the risks associated with the proposed trading "
"strategies and execution plans for {stock_selection}. "
"Provide a detailed analysis of potential risks "
“and suggest mitigation strategies.”
),
expected_output=(
"A comprehensive risk analysis report detailing potential "
“risks and mitigation recommendations for {stock_selection}.”
),
output_file=“risk_mitigation_plan.md”,
agent=risk_management_agent,
)

from crewai import Crew, Process

from langchain_openai import ChatOpenAI

Define the crew with agents and tasks

HERE HIERARCHICAL process is used so there is a manager_llm that manages the other agents , this is to ensure that the context doesnt fade out with multiple agents working on the same task

financial_trading_crew = Crew(
agents=[data_analyst_agent,
trading_strategy_agent,
execution_agent,
risk_management_agent],

tasks=[data_analysis_task, 
       strategy_development_task, 
       execution_planning_task, 
       risk_assessment_task],

# manager_llm=gemini_thinking_llm,
manager_llm=gemini_llm,

process=Process.hierarchical,
verbose=True

)

Example data for kicking off the process

financial_trading_inputs = {
‘stock_selection’: ‘Mazgaon Docks’,
‘initial_capital’: ‘10000’,
‘risk_tolerance’: ‘Low’,
‘trading_strategy_preference’: ‘Day Trading’,
‘news_impact_consideration’: True
}

this execution will take some time to run

result = financial_trading_crew.kickoff(inputs=financial_trading_inputs)

Any help on this would be a wonderful feat !!!