Make this project more robust, maintainable, scalable, and state of art, here is it "I apologize for the confusion in my previous responses. To align with the crewAI framework and provide a more comprehensive example, I will create a new set of files that utilize the crewAI library. Here's a complete set of files that demonstrates how to create and execute a research project using crewAI:

1. `agents_research_project.py`:

```python
from crewai import Agent
from tools import AirtableTool, GoogleSearchAPI, WebScraper

class Director(Agent):
    def __init__(self, airtable_api_key, serper_api_key, browserless_api_key):
        super().__init__(
            role="Director",
            goal="Oversee the entire research project and update the Airtable database accordingly",
            backstory="Responsible for breaking down the main research goal into sub-tasks and delegating these tasks to the Research Manager and Researchers.",
            verbose=True,
            allow_delegation=True,
            tools=[AirtableTool(airtable_api_key), GoogleSearchAPI(serper_api_key), WebScraper(browserless_api_key)]
        )

class ResearchManager(Agent):
    def __init__(self, airtable_api_key, serper_api_key, browserless_api_key):
        super().__init__(
            role="Research Manager",
            goal="Plan the research strategy and ensure the research conducted is of the highest quality",
            backstory="Develops a detailed research plan for each task and assigns specific research actions to the Research Agent(s).",
            verbose=True,
            allow_delegation=True,
            tools=[AirtableTool(airtable_api_key), GoogleSearchAPI(serper_api_key), WebScraper(browserless_api_key)]
        )

class ResearchAgent(Agent):
    def __init__(self, airtable_api_key, serper_api_key, browserless_api_key, task):
        super().__init__(
            role="Research Agent",
            goal=f"Conduct detailed research on {task} and summarize findings",
            backstory="Gathers data, summarizes findings, and submits these to the Research Manager for review.",
            verbose=True,
            allow_delegation=False,
            tools=[AirtableTool(airtable_api_key), GoogleSearchAPI(serper_api_key), WebScraper(browserless_api_key)]
        )
```

2. `tools.py`:

```python
from crewai import Tool
from langchain.agents import tool
from langchain.prompts import PromptTemplate, RecursiveCharacterTextSplitter
from langchain.llms import ChatOpenAI

class AirtableTool(Tool):

    def __init__(self, airtable_api_key):
        self.airtable_api_key = airtable_api_key

    @tool("Access Airtable")
    def get_records(self, base_id, table_id):
        url = f"https://api.airtable.com/v0/{base_id}/{table_id}"

        headers = {
            'Authorization': f'Bearer {self.airtable_api_key}',
        }

        response = requests.request("GET", url, headers=headers)
        data = response.json()
        return data

    @tool("Update Airtable")
    def update_record(self, base_id, table_id, id, fields):
        url = f"https://api.airtable.com/v0/{base_id}/{table_id}"

        headers = {
            'Authorization': f'Bearer {self.airtable_api_key}',
            "Content-Type": "application/json"
        }

        data = {
            "records": [{
                "id": id,
                "fields": fields
            }]
        }

        response = requests.patch(url, headers=headers, data=json.dumps(data))
        data = response.json()
        return data

class GoogleSearchAPI(Tool):

    def __init__(self, serper_api_key):
        self.serper_api_key = serper_api_key

    @tool("Google Search")
    def google_search(self, search_keyword):
        url = "https://google.serper.dev/search"

        payload = json.dumps({
            "q": search_keyword
        })

        headers = {
            'X-API-KEY': self.serper_api_key,
            'Content-Type': 'application/json'
        }

        response = requests.request("POST", url, headers=headers, data=payload)
        return response.text

class WebScraper(Tool):

    def __init__(self, browserless_api_key):
        self.browserless_api_key = browserless_api_key

    @tool("Web Scraper")
    def web_scraping(self, objective, url):
        headers = {
            'Cache-Control': 'no-cache',
            'Content-Type': 'application/json',
        }

        data = {
            "url": url        
        }

        data_json = json.dumps(data)

        response = requests.post(f"https://chrome.browserless.io/content?token={self.browserless_api_key}", headers=headers, data=data_json)

        if response.status_code == 200:
            soup = BeautifulSoup(response.content, "html.parser")
            text = soup.get_text()

            if len(text) > 10000:
                llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k-0613")
                text_splitter = RecursiveCharacterTextSplitter(separators=["\n\n", "\n"], chunk_size=10000, chunk_overlap=500)
                docs = text_splitter.create_documents([text])

                map_prompt = """
                Write a summary of the following text for {objective}:
                "{text}"
                SUMMARY:
                """
                map_prompt_template = PromptTemplate(template=map_prompt, input_variables=["text", "objective"])

                summary_chain = load_summarize_chain(
                    llm=llm, 
                    chain_type='map_reduce',
                    map_prompt=map_prompt_template,
                    combine_prompt=map_prompt_template,
                    verbose=False
                )

                output = summary_chain.run(input_documents=docs, objective=objective)
                return output
            else:
                return text
        else:
            print(f"HTTP request failed with status code {response.status_code}")
```

3. `tasks.py`:

```python
from crewai import Task
from agents_research_project import Director, ResearchManager, ResearchAgent
from tools import AirtableTool, GoogleSearchAPI, WebScraper

class Tasks:
    def __init__(self):
        self.director = Director("airtable_api_key", "serper_api_key", "browserless_api_key")
        self.research_manager = ResearchManager("airtable_api_key", "serper_api_key", "browserless_api_key")

    def initialize_project(self):
        return Task(
            description="Initialize the research project",
            expected_output="List of research tasks",
            agent=self.director,
            output_file="research_tasks.txt"
        )

    def execute_research_task(self, task):
        return Task(
            description=f"Execute research task: {task}",
            expected_output="Summary of research findings",
            agent=self.research_manager.delegate(self.research_agent_for_task, task),
            output_file=f"{task}_research_findings.txt"
        )

    def research_agent_for_task(self, task):
        return ResearchAgent("airtable_api_key", "serper_api_key", "browserless_api_key", task)
```

4. `main.py`:

```python
from crewai import Crew
from tasks import Tasks

airtable_api_key = "your_airtable_api_key"
serper_api_key = "your_serper_api_key"
browserless_api_key = "your_browserless_api_key"

tasks = Tasks()
tasks.director.airtable_api_key = airtable_api_key
tasks.director.serper_api_key = serper_api_key
tasks.director.browserless_api_key = browserless_api_key
tasks.research_manager.airtable_api_key = airtable_api_key
tasks.research_manager.serper_api_key = serper_api_key
tasks.research_manager.browserless_api_key = browserless_api_key

crew = Crew(
    agents=[tasks.director, tasks.research_manager],
    tasks=[
        tasks.initialize_project(),
        tasks.execute_research_task("Task 1"),
        tasks.execute_research_task("Task 2")
    ],
    verbose=2
)

result = crew.kickoff()
```

These files define the agents, tools, tasks, and main execution code for the research project. Remember to replace the placeholders for the API keys with actual keys before running the code.

To run the code, follow these steps:

1. Install the required packages:

```bash
pip install crewai langchain requests beautifulsoup4 openai
```

2. Create an `.env` file in the same directory as the `main.py` file and add your API keys:

```
AIRTABLE_API_KEY=your_airtable_api_key
SERPER_API_KEY=your_serper_api_key
BROWSERLESS_API_KEY=your_browserless_api_key
OPENAI_API_KEY=your_openai_api_key
```

3. Run the `main.py` file:

```bash
python main.py
```

This will initialize the crew, execute the tasks, and print the result. Note that the output will be saved to the specified files (`research_tasks.txt`, `Task 1_research_findings.txt`, and `Task 2_research_findings.txt`).

I hope this updated set of files helps you better understand how to use the crewAI framework for your research project. Let me know if you have any further questions!"