CrewAI docs mentioned that most of the LLMs works. in reality it is not true, I have written wrapper class and tried to create handler and nothing worked out. not sure they have tested it or not. Still I am struggling to fix error:Provider List: Providers | liteLLM
ERROR:root:Failed to get supported params: argument of type ‘NoneType’ is not iterable
Error in CrewAI analysis: ‘str’ object has no attribute ‘get’
Providing my code. @crewai team, please help in fixing this issue and thanks in advance.
code:
import os
import yaml
from crewai import Crew, Process, Agent, Task
from tools.pdf_report_tool import generate_stock_report
from tools.market_data_tool import fetch_market_data
from tools.fundamental_tool import analyze_fundamentals
from tools.technical_tool import analyze_technical_strength
from tools.risk_tool import assess_risk
from tools.trade_plan_tool import generate_trade_plan
#from langchain_ollama import OllamaLLM # 
Updated Ollama import
#from langchain_community.llms import Ollama
from langchain_ollama import OllamaLLM
#from langchain.llms import LLM
#from langchain_community.llms import LLM
from langchain_mistralai import ChatMistralAI
api_key = “111”
mistral_model = “codestral-latest”
llm_mstral = ChatMistralAI(model=mistral_model, temperature=0, api_key=api_key)
MISTRAL_API_KEY=“111”
#from litellm import LLM
from litellm import completion
from typing import List
import requests
import json
from langchain.schema import SystemMessage, HumanMessage
# ------------------- Utility Function -------------------
# Create a function wrapper for litellm completion
def litellm_handler(messages: List[str | SystemMessage | HumanMessage], **kwargs):
try:
formatted_messages =
for message in messages:
if isinstance(message, SystemMessage):
formatted_messages.append({“role”: “system”, “content”: message.content})
elif isinstance(message, HumanMessage):
formatted_messages.append({“role”: “user”, “content”: message.content})
elif isinstance(message, str):
formatted_messages.append({“role”: “user”, “content”: message})
response = completion(
model=“mistral/mistral-large-latest”,
messages=formatted_messages,
temperature=0.7,
api_key=MISTRAL_API_KEY
)
return response.choices[0].message.content
except Exception as e:
print(f"LiteLLM Error: {str(e)}")
return “Error processing request”
class MistralWrapper:
def init(self, api_key):
self.api_key = api_key
self.url = “https://api.mistral.ai/v1/chat/completions”
self.headers = {
“Content-Type”: “application/json”,
“Authorization”: f"Bearer {api_key}"
}
def __call__(self, prompt, **kwargs):
try:
if isinstance(prompt, list):
messages = prompt
else:
messages = [{"role": "user", "content": prompt}]
data = {
"model": "mistral-large-latest",
"messages": messages,
"temperature": 0.7
}
response = requests.post(
self.url,
headers=self.headers,
data=json.dumps(data)
)
if response.status_code == 200:
return response.json()['choices'][0]['message']['content']
else:
return f"Error: {response.status_code}"
except Exception as e:
print(f"Error: {str(e)}")
return "Error processing request"
def get_mistral_response(**kwargs):
response = completion(
model=“mistral/mistral-large-latest”,
messages=[{“role”: “user”, “content”: kwargs.get(“prompt”, “”)}],
temperature=0.7,
api_key=MISTRAL_API_KEY
)
return response.choices[0].message.content
def load_yaml(filename):
“”“Loads agents or tasks from a YAML file.”“”
base_path = os.path.dirname(file)
full_path = os.path.join(base_path, “config”, filename)
with open(full_path, “r”) as file:
return yaml.safe_load(file)
------------------- Crew Creation -------------------
def create_stock_advisor_crew(stock):
“”“Create a Crew for stock analysis using Ollama LLM.”“”
# Initialize Mistral wrapper
mistral = MistralWrapper(api_key=MISTRAL_API_KEY)
# Initialize Ollama LLM
#ollama_llm = OllamaLLM(model=“llama2”) # Updated Ollama usage
mistral_llm = LLM(
model=“mistral/mistral-large-latest”,
temperature=0.7,
api_key=MISTRAL_API_KEY
)
# Tool mapping
tool_mapping = {
"fetch_market_data": fetch_market_data,
"analyze_fundamentals": analyze_fundamentals,
"analyze_technical_strength": analyze_technical_strength,
"assess_risk": assess_risk,
"generate_trade_plan": generate_trade_plan
}
# Load configurations
agents_config = load_yaml("agents.yaml")
tasks_config = load_yaml("tasks.yaml")
# Create agents
# In your create_stock_advisor_crew function, modify the agent creation:
agent = Agent(
role=value[“role”],
goal=value[“goal”],
backstory=value[“backstory”],
tools=tools,
llm=litellm_handler,
allow_delegation=True, # Add this line
verbose=True
)
agents = {}
for key, value in agents_config.items():
tools = [tool_mapping[tool] for tool in value.get("tools", [])]
agent = Agent(
role=value["role"],
goal=value["goal"],
backstory=value["backstory"],
tools=tools,
llm=mistral,#litellm_handler,#get_mistral_response,#mistral_llm,#llm_mstral,#mistral_llm,#ollama_llm, # ✅ Pass Ollama LLM here
#allow_delegation=True, # Add this line
verbose=True
)
agents[value["role"]] = agent
# Create tasks
tasks = []
for key, value in tasks_config.items():
if value["agent"] in agents:
task = Task(
description=f"{value['description']} Stock: {stock}",
expected_output=value['expected_output'],
agent=agents[value["agent"]],
context=[stock, key] # ✅ Pass context as a list
)
tasks.append(task)
# Create crew
crew = Crew(
agents=list(agents.values()),
tasks=tasks,
process=Process.sequential,
verbose=True
)
return crew
------------------- Stock Analysis Function -------------------
def analyze_stock(stock):
“”“Analyze a single stock and return results.”“”
print(f"
Analyzing {stock}…")
# Run individual tool analyses
market_data = fetch_market_data._run(stock, "stock")
fundamentals = analyze_fundamentals._run(stock)
technicals = analyze_technical_strength._run(stock)
risk_analysis = assess_risk._run(stock)
trade_plan = generate_trade_plan._run(stock)
# Combine individual analyses
stock_result = {
"symbol": stock,
"market_data": market_data,
"fundamentals": fundamentals,
"technicals": technicals,
"risk_analysis": risk_analysis,
"trade_plan": trade_plan
}
# Run CrewAI analysis
try:
crew = create_stock_advisor_crew(stock)
crew_result = crew.kickoff()
stock_result["crew_analysis"] = crew_result
print(f"\n📊 CrewAI Final Report for {stock}:\n{crew_result}")
except Exception as e:
print(f"Error in CrewAI analysis: {str(e)}")
stock_result["crew_analysis"] = f"Error: {str(e)}"
return stock_result
------------------- Main Function -------------------
def main():
“”“Main function to analyze stocks.”“”
stock = “HDFCBANK.NS”
results =
# Analyze single stock
result = analyze_stock(stock)
results.append(result)
if name == “main”:
main()
