Build A Financial Analyst Agent Using CrewAI and LlamaIndex

Published in

The AI Forum

18 min readJul 4, 2024

Introduction

Here we are about to create a sophisticated financial research assistant agent by integrating LlamaIndex-powered tools into a CrewAI-powered multi-agent framework.

Within the context of a team, an agent can be envisioned as an individual team member assigned with specific expertise and responsibilities. These agents assume distinct roles, such as ‘Researcher,’ ‘Writer,’ or ‘Customer Support,’ each playing a vital part in fulfilling the collective objectives of the team.

Technology Components Required

CrewAI :

CrewAI, an innovative open-source framework, allows users to harness the power of intelligent agents working collaboratively. In contrast to conventional chatbots, these agents possess the ability to collaborate, exchange information, and tackle intricate tasks as a cohesive team.

Envision a group of experts collaborating harmoniously. Each member possesses unique expertise and knowledge, yet their ability to communicate and distribute responsibilities results in accomplishments surpassing individual capabilities. CrewAI applies this collaborative principle to the realm of AI. Within this framework, individual agents, each with distinctive strengths and limitations, interact under the orchestration of CrewAI, ultimately achieving shared objectives.

LlamaIndex:

LlamaIndex is a user-friendly framework that empowers developers to effortlessly construct LLM-powered applications leveraging their own data. This comprehensive framework consists of essential modules for efficient indexing, retrieval, prompt creation, and agent orchestration. A primary application of LlamaIndex is the development of a versatile QA interface capable of synthesizing knowledge and providing comprehensive answers to intricate queries.

GROQ:

Groq is a cutting-edge platform, encompassing both hardware and software components, known for its outstanding computational speed, exceptional quality, and remarkable energy efficiency. It is accompanied by the LPU™ Inference Engine.

The LPU Inference Engine, also referred to as the Language Processing Unit™, is a state-of-the-art platform, comprising hardware and software elements, engineered to deliver unparalleled compute speed, superior quality, and exceptional energy efficiency. This revolutionary end-to-end processing unit system excels in providing rapid inference for computationally intensive applications, particularly for sequential components like AI language applications, including the impressive Large Language Models (LLMs).

Implementation of Financial Research Analyst Agent

Steps involved:

Build a RAG system using LlamaIndex
Wrap the RAG query engine as LlamaindexTool as having proper tool abstractions is at the core of building data agents.
Researcher agents uncovers insights from the stock data.
Write agents writes insightful and engaging articles based on the insights provided.
Define the task and expected layouts for the researcher and writer agent
Llama-3–70B as the Large Language Model

Code Implementation:

Implemented in google colab using high ram cpu.

Install required dependencies

!pip install llama-index
!pip install llama-index-llms-groq
!pip install llama-index-core
!pip install llama-index-readers-file
!pip install llama-index-tools-wolfram-alpha
!pip install llama-index-embeddings-huggingface
!pip install 'crewai[tools]'

Setup the LLM

from google.colab import userdata
from llama_index.llms.groq import Groq
groq_api_key = userdata.get('GROQ_API_KEY')
#
llm = Groq(model="llama3-70b-8192", api_key=groq_api_key)
#
response = llm.complete("Explain the importance of low latency LLMs")
print(response)

######################################Response############################
Low-latency Large Language Models (LLMs) are crucial in various applications where real-time or near-real-time processing is essential. Here are some reasons why low-latency LLMs are important:

1. **Interactive Systems**: In interactive systems like chatbots, virtual assistants, and conversational AI, low-latency LLMs enable rapid response times, making the interaction feel more natural and human-like. This is particularly important in applications where users expect immediate responses, such as customer support or language translation.
2. **Real-time Decision Making**: In applications like autonomous vehicles, robotics, or medical diagnosis, low-latency LLMs can process and analyze vast amounts of data in real-time, enabling swift decision-making and reaction to changing circumstances.
3. **Live Streaming and Broadcasting**: Low-latency LLMs can facilitate real-time language translation, sentiment analysis, or content moderation in live streaming and broadcasting applications, enhancing the viewer experience and ensuring timely content delivery.
4. **Gaming and Esports**: In online gaming and esports, low-latency LLMs can improve the gaming experience by enabling faster language processing, sentiment analysis, and chat moderation, reducing lag and enhancing overall performance.
5. **Healthcare and Emergency Services**: In healthcare and emergency services, low-latency LLMs can quickly process medical records, diagnose conditions, and provide critical information to healthcare professionals, saving lives and improving patient outcomes.
6. **Financial Trading and Analytics**: Low-latency LLMs can rapidly analyze large datasets, enabling high-frequency trading, sentiment analysis, and risk assessment in financial markets, helping traders and analysts make informed decisions.
7. **Cybersecurity**: In cybersecurity, low-latency LLMs can quickly detect and respond to threats, such as malware, phishing attacks, or DDoS attacks, reducing the attack surface and minimizing damage.
8. **Edge Computing and IoT**: As IoT devices proliferate, low-latency LLMs can process data closer to the source, reducing latency and improving real-time decision-making in applications like smart homes, cities, or industrial automation.
9. **Accessibility and Inclusion**: Low-latency LLMs can enable real-time language translation, captioning, and transcription, improving accessibility and inclusion for people with disabilities, language barriers, or hearing impairments.
10. **Competitive Advantage**: In many industries, low-latency LLMs can provide a competitive advantage by enabling faster decision-making, improved customer experiences, and increased operational efficiency, ultimately driving business success.

To achieve low latency in LLMs, researchers and developers are exploring various techniques, including:

1. Model pruning and knowledge distillation
2. Quantization and precision reduction
3. Parallel processing and distributed computing
4. Edge computing and decentralized architectures
5. Optimized hardware and software designs
6. Caching and memoization
7. Lazy loading and just-in-time compilation

By reducing latency in LLMs, we can unlock new possibilities in various applications, leading to improved user experiences, increased efficiency, and enhanced decision-making capabilities.

## Crewai requires a chat based model for binding
from langchain_openai import ChatOpenAI

chat_llm = ChatOpenAI(
    openai_api_base="https://api.groq.com/openai/v1",
    openai_api_key=groq_api_key,
    model="llama3-70b-8192",
    temperature=0,
    max_tokens=1000,
)

Download data

!wget "https://s23.q4cdn.com/407969754/files/doc_financials/2019/ar/Uber-Technologies-Inc-2019-Annual-Report.pdf" -O uber_10k.pdf

Parse the content of the data

from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.llms.openai import OpenAI
import os
from langchain_openai import ChatOpenAI

#
reader = SimpleDirectoryReader(input_files=["uber_10k.pdf"])
docs = reader.load_data()
docs[1]

##############################################################
Document(id_='dd161725-2512-4b03-a689-accc69dc46d4', embedding=None, metadata={'page_label': '2', 'file_name': 'uber_10k.pdf', 'file_path': 'uber_10k.pdf', 'file_type': 'application/pdf', 'file_size': 2829436, 'creation_date': '2024-06-30', 'last_modified_date': '2020-03-31'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='69  \nCountries\n10K+  \nCities\n$65B  \nGross Bookings\n111M  \nMAPCs\n7B  \nTripsA global tech \nplatform at \nmassive scale\nServing multiple multi-trillion \ndollar markets with products \nleveraging our core technology \nand infrastructure\nWe believe deeply in our bold mission. Every minute \nof every day, consumers and Drivers on our platform \ncan tap a button and get a ride or tap a button and \nget work. We revolutionized personal mobility with \nridesharing, and we are leveraging our platform to \nredefine the massive meal delivery and logistics \nindustries. The foundation of our platform is our \nmassive network, leading technology, operational \nexcellence, and product expertise. Together, these \nelements power movement from point A to point B.', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n')

Setup the embedding model

from llama_index.embeddings.huggingface import HuggingFaceEmbedding

# loads BAAI/bge-small-en-v1.5
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")

Build Index

index = VectorStoreIndex.from_documents(docs,
                                        embed_model=embed_model,
                                        )

Build the Query Engine

query_engine = index.as_query_engine(similarity_top_k=5, llm=llm)

Instantiate the query engine as a Tool

CrewAI has a seamless integration with LlamaIndex’s comprehensive toolkit for RAG (Retrieval-Augmented Generation) and agentic pipelines, enabling advanced search-based queries and more. Here are the available built-in tools offered by LlamaIndex.

A tool in CrewAI is a skill or function that agents can utilize to perform various actions.

Advantages:

Utility: The tools are designed for specific tasks like web searching, data analysis, content generation, and agent collaboration.
Integration: The tools seamlessly integrate into the workflow of agents, enhancing their capabilities.
Customizability: The tools can be customized to meet the unique needs of agents, either by developing new tools or utilizing existing ones.
Error Handling: The tools have robust error handling mechanisms in place to ensure smooth operation and minimize disruptions.
Caching Mechanism: The tools employ intelligent caching to optimize performance and minimize repetitive operations.

from crewai_tools import LlamaIndexTool
query_tool = LlamaIndexTool.from_query_engine(
    query_engine,
    name="Uber 2019 10K Query Tool",
    description="Use this tool to lookup the 2019 Uber 10K Annual Report",
)
#
query_tool.args_schema.schema()

########################################
{'title': 'QueryToolSchema',
 'description': 'Schema for query tool.',
 'type': 'object',
 'properties': {'query': {'title': 'Query',
   'description': 'Search query for the query tool.',
   'type': 'string'}},
 'required': ['query']}

Instantiate Researcher and Writer Agents

Important Agent Parameters:

Role : Defines the agent’s function within the crew. It determines the kind of tasks the agent is best suited for.
Goal : The individual objective that the agent aims to achieve. It guides the agent’s decision-making process.
Backstory :Provides context to the agent’s role and goal, enriching the interaction and collaboration dynamics.
LLM (optional): Represents the language model that will run the agent. It dynamically fetches the model name from the OPENAI_MODEL_NAME environment variable, defaulting to "gpt-4" if not specified.
Verbose (optional): Setting this to True configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is False.
Allow Delegation (optional): Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is True.

import os
from crewai import Agent, Task, Crew, Process

# Define your agents with roles and goals
researcher = Agent(
    role="Senior Financial Analyst",
    goal="Uncover insights about different tech companies",
    backstory="""You work at an asset management firm.
  Your goal is to understand tech stocks like Uber.""",
    verbose=True,
    allow_delegation=False,
    tools=[query_tool],
    llm=chat_llm,
)
writer = Agent(
    role="Tech Content Strategist",
    goal="Craft compelling content on tech advancements",
    backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
  You transform complex concepts into compelling narratives.""",
    llm=chat_llm,
    verbose=True,
    allow_delegation=False,
)

Define respective tasks

Important parameters:

Description: A clear, concise statement of what the task entails.
Agent: The agent responsible for the task, assigned either directly or by the crew’s process.
Expected Output: A detailed description of what the task’s completion looks like.
Tools (optional): The functions or capabilities the agent can utilize to perform the task
Context (optional): Specifies tasks whose outputs are used as context for this task.

# Create tasks for your agents
task1 = Task(
    description="""Conduct a comprehensive analysis of Uber's risk factors in 2019.""",
    expected_output="Full analysis report in bullet points",
    agent=researcher,
)

task2 = Task(
    description="""Using the insights provided, develop an engaging blog
  post that highlights the headwinds that Uber faces.
  Your post should be informative yet accessible, catering to a casual audience.
  Make it sound cool, avoid complex words.""",
    expected_output="Full blog post of at least 4 paragraphs",
    agent=writer,
)

Instantiate the Crew with a sequential process. Assemble your members here.


crew = Crew(
    agents=[researcher, writer],
    tasks=[task1, task2],
    verbose=2,  # You can set it to 1 or 2 to different logging levels
)

Initiate the agent workflow

Crew Execution Process

Sequential Process: Tasks are executed one after another, allowing for a linear flow of work.
Hierarchical Process: A manager agent coordinates the crew, delegating tasks and validating outcomes before proceeding. Note: A manager_llm or manager_agent is required for this process and it's essential for validating the process flow.

# Get your crew to work!
result = crew.kickoff()

print("######################")
print(result)


##########################Response######################################
[2024-06-30 16:41:45][DEBUG]: == Working Agent: Senior Financial Analyst
 [2024-06-30 16:41:45][INFO]: == Starting Task: Conduct a comprehensive analysis of Uber's risk factors in 2019.


> Entering new CrewAgentExecutor chain...
I need to understand the risk factors associated with Uber in 2019.

Action: Uber 2019 10K Query Tool
Action Input: {"query": "Item 1A. Risk Factors"} 

Our business, financial condition, operating results, and prospects are exposed to various risks and uncertainties, including those related to our expansion into new markets, development of new technologies, and growth of our network of vertical take-off and landing vehicles. These risks may have a material adverse effect on our business, financial condition, and results of operations.

Thought:
I need to understand the specific risks associated with Uber's expansion into new markets.

Action: Uber 2019 10K Query Tool
Action Input: {"query": "expansion into new markets"} 

The company believes that European shippers and carriers experience many of the same pain points in their current operations as U.S. shippers and carriers, and therefore, they announced the expansion of their Freight offering into Europe in March 2019.

Thought:
I need to understand the specific risks associated with Uber's expansion into new markets.

Action: Uber 2019 10K Query Tool
Action Input: {"query": "expansion into new markets risks"} 

Our business is substantially dependent on operations outside the United States, including those in markets in which we have limited experience, and if we are unable to manage the risks presented by our business model internationally, our financial results and future prospects will be adversely impacted.

Thought:
I need to understand the risk factors associated with Uber's development of new technologies.

Action: Uber 2019 10K Query Tool
Action Input: {"query": "development of new technologies risks"} 

The development of new technologies is inherently risky, and we may never realize any expected benefits from them. In addition, the safety risks associated with these new technologies, such as dockless e-bikes and e-scooters, may not be fully mitigated, even with our efforts to advise users of local safety regulations and offer promotional codes for helmets. If we do not spend our development budget efficiently or effectively on commercially successful and innovative technologies, we may not realize the expected benefits of our strategy, and users may be exposed to increased risks of injuries or death.

Thought:
I need to understand the risk factors associated with Uber's growth of network of vertical take-off and landing vehicles.

Action: Uber 2019 10K Query Tool
Action Input: {"query": "vertical take-off and landing vehicles risks"} 

There is no mention of "vertical take-off and landing vehicles" or "VTOL" in the provided context information. The context discusses Uber's business, including its autonomous vehicle technologies, dockless e-bikes and e-scooters, Freight, and Uber Elevate, as well as risks related to these services, such as competition, regulatory issues, and safety concerns. However, it does not mention VTOL vehicles or any related risks.

Thought: I now know the final answer
Final Answer: 

Comprehensive Analysis of Uber's Risk Factors in 2019:

• Our business, financial condition, operating results, and prospects are exposed to various risks and uncertainties, including those related to our expansion into new markets, development of new technologies, and growth of our network of vertical take-off and landing vehicles. These risks may have a material adverse effect on our business, financial condition, and results of operations.

• Risks associated with expansion into new markets:
  • The company believes that European shippers and carriers experience many of the same pain points in their current operations as U.S. shippers and carriers, and therefore, they announced the expansion of their Freight offering into Europe in March 2019.
  • Our business is substantially dependent on operations outside the United States, including those in markets in which we have limited experience, and if we are unable to manage the risks presented by our business model internationally, our financial results and future prospects will be adversely impacted.

• Risks associated with development of new technologies:
  • The development of new technologies is inherently risky, and we may never realize any expected benefits from them.
  • In addition, the safety risks associated with these new technologies, such as dockless e-bikes and e-scooters, may not be fully mitigated, even with our efforts to advise users of local safety regulations and offer promotional codes for helmets.
  • If we do not spend our development budget efficiently or effectively on commercially successful and innovative technologies, we may not realize the expected benefits of our strategy, and users may be exposed to increased risks of injuries or death.

• Risks associated with growth of network of vertical take-off and landing vehicles:
  • There is no mention of "vertical take-off and landing vehicles" or "VTOL" in the provided context information. The context discusses Uber's business, including its autonomous vehicle technologies, dockless e-bikes and e-scooters, Freight, and Uber Elevate, as well as risks related to these services, such as competition, regulatory issues, and safety concerns. However, it does not mention VTOL vehicles or any related risks.

> Finished chain.
 [2024-06-30 16:45:39][DEBUG]: == [Senior Financial Analyst] Task output: Comprehensive Analysis of Uber's Risk Factors in 2019:

• Our business, financial condition, operating results, and prospects are exposed to various risks and uncertainties, including those related to our expansion into new markets, development of new technologies, and growth of our network of vertical take-off and landing vehicles. These risks may have a material adverse effect on our business, financial condition, and results of operations.

• Risks associated with expansion into new markets:
  • The company believes that European shippers and carriers experience many of the same pain points in their current operations as U.S. shippers and carriers, and therefore, they announced the expansion of their Freight offering into Europe in March 2019.
  • Our business is substantially dependent on operations outside the United States, including those in markets in which we have limited experience, and if we are unable to manage the risks presented by our business model internationally, our financial results and future prospects will be adversely impacted.

• Risks associated with development of new technologies:
  • The development of new technologies is inherently risky, and we may never realize any expected benefits from them.
  • In addition, the safety risks associated with these new technologies, such as dockless e-bikes and e-scooters, may not be fully mitigated, even with our efforts to advise users of local safety regulations and offer promotional codes for helmets.
  • If we do not spend our development budget efficiently or effectively on commercially successful and innovative technologies, we may not realize the expected benefits of our strategy, and users may be exposed to increased risks of injuries or death.

• Risks associated with growth of network of vertical take-off and landing vehicles:
  • There is no mention of "vertical take-off and landing vehicles" or "VTOL" in the provided context information. The context discusses Uber's business, including its autonomous vehicle technologies, dockless e-bikes and e-scooters, Freight, and Uber Elevate, as well as risks related to these services, such as competition, regulatory issues, and safety concerns. However, it does not mention VTOL vehicles or any related risks.


 [2024-06-30 16:45:39][DEBUG]: == Working Agent: Tech Content Strategist
 [2024-06-30 16:45:39][INFO]: == Starting Task: Using the insights provided, develop an engaging blog
  post that highlights the headwinds that Uber faces.
  Your post should be informative yet accessible, catering to a casual audience.
  Make it sound cool, avoid complex words.


> Entering new CrewAgentExecutor chain...
Formatting errors incremented
Thought: I now can give a great answer

Final Answer:

**Uber's Turbulent Ride: Navigating the Headwinds Ahead**

As the pioneer of ride-hailing, Uber has revolutionized the way we move around cities. But beneath its sleek interface and convenient services, the company faces a multitude of challenges that threaten to disrupt its growth and profitability. In this post, we'll delve into the key headwinds that Uber must navigate to stay ahead of the curve.

**Expansion into New Markets: A Double-Edged Sword**

Uber's aggressive expansion into new markets, including Europe, may seem like a bold move, but it comes with its fair share of risks. The company's business model is heavily dependent on operations outside the United States, where it has limited experience. This lack of familiarity can lead to cultural and regulatory missteps, ultimately impacting its financial results and future prospects. Moreover, Uber must contend with local competitors who are better acquainted with the nuances of their respective markets. As the company ventures into uncharted territories, it must be prepared to adapt and innovate to stay competitive.

**The Risks of Innovation: Developing New Technologies**

Uber's foray into new technologies, such as dockless e-bikes and e-scooters, is a high-stakes game. While these innovations have the potential to disrupt traditional transportation models, they also come with inherent safety risks. The company must balance the need to advise users of local safety regulations with the need to promote its services and encourage adoption. Furthermore, the development of new technologies is inherently risky, and there's no guarantee that Uber will realize the expected benefits from its investments. If the company fails to spend its development budget efficiently, it may not only miss out on potential revenue streams but also expose users to increased risks of injuries or death.

**The Road Ahead: Navigating Regulatory Hurdles and Safety Concerns**

As Uber continues to grow and evolve, it must contend with a complex web of regulatory hurdles and safety concerns. From competition and regulatory issues to safety concerns and user trust, the company faces a multitude of challenges that can impact its bottom line. To stay ahead of the curve, Uber must prioritize user safety, invest in innovative technologies, and foster collaborative relationships with regulators and local authorities. By doing so, the company can mitigate the risks associated with its business model and continue to thrive in an increasingly competitive landscape.

In conclusion, Uber's journey to the top has been marked by innovation and disruption, but it's not without its challenges. As the company navigates the headwinds ahead, it must be prepared to adapt, innovate, and prioritize user safety above all else. By doing so, Uber can continue to revolutionize the transportation industry and stay ahead of the competition.

> Finished chain.
 [2024-06-30 16:45:59][DEBUG]: == [Tech Content Strategist] Task output: **Uber's Turbulent Ride: Navigating the Headwinds Ahead**

As the pioneer of ride-hailing, Uber has revolutionized the way we move around cities. But beneath its sleek interface and convenient services, the company faces a multitude of challenges that threaten to disrupt its growth and profitability. In this post, we'll delve into the key headwinds that Uber must navigate to stay ahead of the curve.

**Expansion into New Markets: A Double-Edged Sword**

Uber's aggressive expansion into new markets, including Europe, may seem like a bold move, but it comes with its fair share of risks. The company's business model is heavily dependent on operations outside the United States, where it has limited experience. This lack of familiarity can lead to cultural and regulatory missteps, ultimately impacting its financial results and future prospects. Moreover, Uber must contend with local competitors who are better acquainted with the nuances of their respective markets. As the company ventures into uncharted territories, it must be prepared to adapt and innovate to stay competitive.

**The Risks of Innovation: Developing New Technologies**

Uber's foray into new technologies, such as dockless e-bikes and e-scooters, is a high-stakes game. While these innovations have the potential to disrupt traditional transportation models, they also come with inherent safety risks. The company must balance the need to advise users of local safety regulations with the need to promote its services and encourage adoption. Furthermore, the development of new technologies is inherently risky, and there's no guarantee that Uber will realize the expected benefits from its investments. If the company fails to spend its development budget efficiently, it may not only miss out on potential revenue streams but also expose users to increased risks of injuries or death.

**The Road Ahead: Navigating Regulatory Hurdles and Safety Concerns**

As Uber continues to grow and evolve, it must contend with a complex web of regulatory hurdles and safety concerns. From competition and regulatory issues to safety concerns and user trust, the company faces a multitude of challenges that can impact its bottom line. To stay ahead of the curve, Uber must prioritize user safety, invest in innovative technologies, and foster collaborative relationships with regulators and local authorities. By doing so, the company can mitigate the risks associated with its business model and continue to thrive in an increasingly competitive landscape.

In conclusion, Uber's journey to the top has been marked by innovation and disruption, but it's not without its challenges. As the company navigates the headwinds ahead, it must be prepared to adapt, innovate, and prioritize user safety above all else. By doing so, Uber can continue to revolutionize the transportation industry and stay ahead of the competition.


######################

Final answer

**Uber's Turbulent Ride: Navigating the Headwinds Ahead**

As the pioneer of ride-hailing, Uber has revolutionized the way we move around cities. But beneath its sleek interface and convenient services, the company faces a multitude of challenges that threaten to disrupt its growth and profitability. In this post, we'll delve into the key headwinds that Uber must navigate to stay ahead of the curve.

**Expansion into New Markets: A Double-Edged Sword**

Uber's aggressive expansion into new markets, including Europe, may seem like a bold move, but it comes with its fair share of risks. The company's business model is heavily dependent on operations outside the United States, where it has limited experience. This lack of familiarity can lead to cultural and regulatory missteps, ultimately impacting its financial results and future prospects. Moreover, Uber must contend with local competitors who are better acquainted with the nuances of their respective markets. As the company ventures into uncharted territories, it must be prepared to adapt and innovate to stay competitive.

**The Risks of Innovation: Developing New Technologies**

Uber's foray into new technologies, such as dockless e-bikes and e-scooters, is a high-stakes game. While these innovations have the potential to disrupt traditional transportation models, they also come with inherent safety risks. The company must balance the need to advise users of local safety regulations with the need to promote its services and encourage adoption. Furthermore, the development of new technologies is inherently risky, and there's no guarantee that Uber will realize the expected benefits from its investments. If the company fails to spend its development budget efficiently, it may not only miss out on potential revenue streams but also expose users to increased risks of injuries or death.

**The Road Ahead: Navigating Regulatory Hurdles and Safety Concerns**

As Uber continues to grow and evolve, it must contend with a complex web of regulatory hurdles and safety concerns. From competition and regulatory issues to safety concerns and user trust, the company faces a multitude of challenges that can impact its bottom line. To stay ahead of the curve, Uber must prioritize user safety, invest in innovative technologies, and foster collaborative relationships with regulators and local authorities. By doing so, the company can mitigate the risks associated with its business model and continue to thrive in an increasingly competitive landscape.

In conclusion, Uber's journey to the top has been marked by innovation and disruption, but it's not without its challenges. As the company navigates the headwinds ahead, it must be prepared to adapt, innovate, and prioritize user safety above all else. By doing so, Uber can continue to revolutionize the transportation industry and stay ahead of the competition.

Conclusion:

Here we have built a Financial Research Analyst agent completely using Open Source LLMs.This solution is based on the example shared by Llamaindex using CrewAI.

Referrences:

https://docs.llamaindex.ai/en/stable/examples/cookbooks/crewai_llamaindex/

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