crewAI Multi-Agent Systems Framework

When configuring agents for tech startup outreach, the outreach strategy must focus on three core pillars to align with startup priorities:

1. Innovation: Emphasize that products or services are at the forefront of technology.
2. Scalability: Explicitly discuss how solutions can grow alongside the startup's business.
3. Support: Highlight the specific resources and support provided, as startups require high levels of reliability and assistance during growth phases.

Use these pillars to guide the persona and task descriptions of your CrewAI agents to ensure the generated content resonates with the target audience's unique challenges.

When designing AI agents or manual outreach workflows for enterprise-level clients, use the Strategy and Innovation Framework. The framework shifts the focus from simple product sales to strategic partnership and seamless integration.

When configuring agent prompts or message templates for enterprise targets, ensure the following three pillars are addressed:

1. Strategic Partnership: Emphasize long-term value rather than transactional benefits.
2. Integration: Highlight how the solution fits into existing enterprise infrastructure.
3. Innovation: Demonstrate how the solution drives organizational progress.

In hierarchical collaboration, crewAI automatically creates a manager agent to oversee the crew.

Requirements:

• Set process=Process.hierarchical in the Crew object.
• You must provide a manager_llm (e.g., using ChatOpenAI) to the Crew object.

The manager agent allocates tasks based on roles and tools, evaluates outcomes, and ensures standards are met. This approach helps prevent the 'fading context' problem seen in sequential processes.

from crewai import Crew
from crewai.process import Process
from langchain_openai import ChatOpenAI

crew = Crew(
    agents=my_agents,
    tasks=my_tasks,
    process=Process.hierarchical,
    manager_llm=ChatOpenAI(model="gpt-4")
)

When conducting outreach to small businesses, focus on personalization, operational efficiency, and community support. Use the following template to structure your messages, ensuring you replace the bracketed placeholders with specific details about the target business and your own offerings.

Hello [Name],

As a local business owner, your dedication to [specific aspect of their business, e.g., providing excellent customer service, offering high-quality products] truly stands out. At [Your Company], we offer solutions that can help businesses like [Business Name] become even more efficient and effective.

[Describe a specific feature of your product/service and how it solves a problem they face].

We would love to discuss how we can be part of your success story.

Warm regards,
[Your Name]

In a hierarchical process, the Crew does not just run tasks sequentially; instead, it creates a manager to oversee the workflow and delegate tasks to the appropriate agents.

To implement this:

1. Set process=Process.hierarchical in the Crew definition.
2. Provide a manager_llm (using a LangChain chat model like ChatOpenAI) to act as the manager that coordinates the agents.

from crewai import Crew, Process
from langchain_openai import ChatOpenAI

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],
    verbose=True,
    process=Process.hierarchical,
    manager_llm=ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)
)

To allow agents to remember, reason, and learn from past interactions (including short-term, long-term, and entity memory), enable memory by setting memory=True when initializing the Crew object.

from crewai import Crew, Agent, Task, Process

my_crew = Crew(
    agents=[...],
    tasks=[...],
    process=Process.sequential,
    memory=True,
    verbose=True
)

Tools can be assigned at two levels:

1. Agent Level: The agent can use the tool for any task it performs.
2. Task Level: The agent will only use the tool for that specific task.

Note: Task-level tools override Agent-level tools.

from crewai import Task

inquiry_resolution = Task(
    description="...",
    expected_output="...",
    agent=customer_support_agent,
    tools=[docs_scrape_tool] # Task-level tool assignment
)

CrewAI uses environment variables to configure the LLM. Set OPENAI_API_KEY for authentication and OPENAI_MODEL_NAME to specify the model (e.g., gpt-3.5-turbo).

import os

os.environ["OPENAI_API_KEY"] = "Your OPEN AI API KEY"
os.environ["OPENAI_MODEL_NAME"] = 'gpt-3.5-turbo'

crewAI uses LLMs to power agents. By default, this example uses OpenAI's gpt-3.5-turbo. You must set the OPENAI_API_KEY environment variable to authenticate your requests.

import os
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

To use CrewAI, ensure you have Python version 3.10, 3.11, 3.12, or 3.13 installed. You can install the core library and the optional tools package using pip.

pip install crewai
pip install 'crewai[tools]'

Before initializing Agents, you must set the necessary environment variables for your LLM provider and search tools. At a minimum, you need OPENAI_API_KEY and OPENAI_MODEL_NAME. If using SerperDevTool, you also need SERPER_API_KEY.

import os
os.environ['OPENAI_API_KEY'] = 'Your OPENAI API KEY'
os.environ['OPENAI_MODEL_NAME'] = 'gpt-4-turbo'
os.environ['SERPER_API_KEY'] = 'Your SERPER API KEY'