Adding new CrewAI+LangGraph example

CrewAI-LangGraph/.env.example

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+OPENAI_API_KEY=...
+TAVILY_API_KEY=... (tavily.com)
+MY_EMAIL=... (your email)
+
+# Optional
+LANGCHAIN_TRACING_V2=...
+LANGCHAIN_ENDPOINT=...
+LANGCHAIN_API_KEY=...
+LANGCHAIN_PROJECT=...

CrewAI-LangGraph/.gitignore

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+__pycache__
+.env
+credentials.json
+token.json

CrewAI-LangGraph/README.md

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+# CrewAI + LangGraph
+
+## Introduction
+This is an example of how to use the [CrewAI](https://github.com/joaomdmoura/crewai) with LangChain and LangGraph to automate the process of automatically checking emails and creating drafts. CrewAI orchestrates autonomous AI agents, enabling them to collaborate and execute complex tasks efficiently.
+
+![High level image](./CrewAI-LangGraph.png)
+
+
+By [@joaomdmoura](https://x.com/joaomdmoura)
+
+- [CrewAI Framework](#crewai-framework)
+- [Running the code](#running-the-code)
+- [Details & Explanation](#details--explanation)
+- [Using Local Models with Ollama](#using-local-models-with-ollama)
+- [License](#license)
+
+## CrewAI Framework
+CrewAI is designed to facilitate the collaboration of role-playing AI agents. In this example, these agents work together to give a complete stock analysis and investment recommendation
+
+## Running the Code
+This example uses GPT-4.
+
+- **Configure Environment**: Copy ``.env.example` and set up the environment variable
+- **Setup a credentials.json**: Follow the [google instructions](https://developers.google.com/gmail/api/quickstart/python#authorize_credentials_for_a_desktop_application), once you’ve downloaded the file, name it `credentials.json` and add to the root of the project,
+- **Install Dependencies**: Run `pip install -r requirements.txt`
+- **Execute the Script**: Run `python main.py`
+
+## Details & Explanation
+- **Running the Script**: Execute `python main.py`
+- **Key Components**:
+	- `./src/graph.py`: Class defining the nodes and edges.
+	- `./src/nodes.py`: Class with the function for each node.
+	- `./src/state.py`: State declaration.
+	- `./src/crew/agents.py`: Class defining the CrewAI Agents.
+	- `./src/crew/taks.py`: Class definig the CrewAI Tasks.
+	- `./src/crew/crew.py`: Class defining the CrewAI Crew.
+	- `./src/crew/tools.py`: Class implementing the GmailDraft Tool.
+
+## License
+This project is released under the MIT License.

CrewAI-LangGraph/main.py

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+from src.graph import WorkFlow
+
+app = WorkFlow().app
+app.invoke({})

CrewAI-LangGraph/requirements.txt

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+crewai==0.1.32
+langgraph==0.0.15
+langchain-community==0.0.14
+python-dotenv==1.0.0
+google-search-results==2.1.0
+google-api-python-client==2.114.0
+google-auth-oauthlib==1.2.0
+google-auth-httplib2==0.2.0
+beautifulsoup4==4.12.3
+tavily-python==0.3.1

CrewAI-LangGraph/src/crew/agents.py

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+from langchain_community.agent_toolkits import GmailToolkit
+from langchain_community.tools.gmail.get_thread import GmailGetThread
+from langchain_community.tools.tavily_search import TavilySearchResults
+
+from textwrap import dedent
+from crewai import Agent
+from .tools import CreateDraftTool
+
+class EmailFilterAgents():
+	def __init__(self):
+		self.gmail = GmailToolkit()
+
+	def email_filter_agent(self):
+		return Agent(
+			role='Senior Email Analyst',
+			goal='Filter out non-essential emails like newsletters and promotional content',
+			backstory=dedent("""\
+				As a Senior Email Analyst, you have extensive experience in email content analysis.
+				You are adept at distinguishing important emails from spam, newsletters, and other
+				irrelevant content. Your expertise lies in identifying key patterns and markers that
+				signify the importance of an email."""),
+			verbose=True,
+			allow_delegation=False
+		)
+
+	def email_action_agent(self):
+
+		return Agent(
+			role='Email Action Specialist',
+			goal='Identify action-required emails and compile a list of their IDs',
+			backstory=dedent("""\
+				With a keen eye for detail and a knack for understanding context, you specialize
+				in identifying emails that require immediate action. Your skill set includes interpreting
+				the urgency and importance of an email based on its content and context."""),
+			tools=[
+				GmailGetThread(api_resource=self.gmail.api_resource),
+				TavilySearchResults()
+			],
+			verbose=True,
+			allow_delegation=False,
+		)
+
+	def email_response_writer(self):
+		return Agent(
+			role='Email Response Writer',
+			goal='Draft responses to action-required emails',
+			backstory=dedent("""\
+				You are a skilled writer, adept at crafting clear, concise, and effective email responses.
+				Your strength lies in your ability to communicate effectively, ensuring that each response is
+				tailored to address the specific needs and context of the email."""),
+			tools=[
+				TavilySearchResults(),
+				GmailGetThread(api_resource=self.gmail.api_resource),
+				CreateDraftTool.create_draft
+			],
+			verbose=True,
+			allow_delegation=False,
+		)

CrewAI-LangGraph/src/crew/crew.py

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+from crewai import Crew
+
+from .agents import EmailFilterAgents
+from .tasks import EmailFilterTasks
+
+class EmailFilterCrew():
+	def __init__(self):
+		agents = EmailFilterAgents()
+		self.filter_agent = agents.email_filter_agent()
+		self.action_agent = agents.email_action_agent()
+		self.writer_agent = agents.email_response_writer()
+
+	def kickoff(self, state):
+		print("### Filtering emails")
+		tasks = EmailFilterTasks()
+		crew = Crew(
+			agents=[self.filter_agent, self.action_agent, self.writer_agent],
+			tasks=[
+				tasks.filter_emails_task(self.filter_agent, self._format_emails(state['emails'])),
+				tasks.action_required_emails_task(self.action_agent),
+				tasks.draft_responses_task(self.writer_agent)
+			],
+			verbose=True
+		)
+		result = crew.kickoff()
+		return {**state, "action_required_emails": result}
+
+	def _format_emails(self, emails):
+		emails_string = []
+		for email in emails:
+			print(email)
+			arr = [
+				f"ID: {email['id']}",
+				f"- Thread ID: {email['threadId']}",
+				f"- Snippet: {email['snippet']}",
+				f"- From: {email['sender']}",
+				f"--------"
+			]
+			emails_string.append("\n".join(arr))
+		return "\n".join(emails_string)

CrewAI-LangGraph/src/crew/tasks.py

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+from crewai import Task
+from textwrap import dedent
+
+class EmailFilterTasks:
+	def filter_emails_task(self, agent, emails):
+		return Task(
+			description=dedent(f"""\
+				Analyze a batch of emails and filter out
+				non-essential ones such as newsletters, promotional content and notifications.
+
+			  Use your expertise in email content analysis to distinguish
+				important emails from the rest, pay attention to the sender and avoind invalid emails.
+
+				Make sure to filter for the messages actually directed at the user and avoid notifications.
+
+				EMAILS
+				-------
+				{emails}
+
+				Your final answer MUST be a the relevant thread_ids and the sender, use bullet points.
+				"""),
+			agent=agent
+		)
+
+	def action_required_emails_task(self, agent):
+		return Task(
+			description=dedent("""\
+				For each email thread, pull and analyze the complete threads using only the actual Thread ID.
+				understand the context, key points, and the overall sentiment
+				of the conversation.
+
+				Identify the main query or concerns that needs to be
+				addressed in the response for each
+
+				Your final answer MUST be a list for all emails with:
+				- the thread_id
+				- a summary of the email thread
+				- a highlighting with the main points
+				- identify the user and who he will be answering to
+				- communication style in the thread
+				- the sender's email address
+				"""),
+			agent=agent
+		)
+
+	def draft_responses_task(self, agent):
+		return Task(
+			description=dedent(f"""\
+				Based on the action-required emails identified, draft responses for each.
+				Ensure that each response is tailored to address the specific needs
+				and context outlined in the email.
+				Assume the persona of the user and mimic the communication style in the thread.
+
+				Use the tool provided to draft each of the responses.
+				When using the tool pass the following input:
+				- to (sender to be responded)
+				- subject
+				- message
+
+				You MUST create all drafts before sending your final answer.
+				Your final answer MUST be a confirmation that all responses have been drafted.
+				"""),
+			agent=agent
+		)

CrewAI-LangGraph/src/crew/tools.py

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+from langchain_community.agent_toolkits import GmailToolkit
+from langchain_community.tools.gmail.create_draft import GmailCreateDraft
+from langchain.tools import tool
+
+class CreateDraftTool():
+  @tool("Create Draft")
+  def create_draft(data):
+    """
+    	Useful to create an email draft.
+      The input to this tool should be a pipe (|) separated text
+      of length 3 (three), representing who to send the email to,
+      the subject of the email and the actual message.
+      For example, `[email protected]|Nice To Meet You|Hey it was great to meet you.`.
+    """
+    email, subject, message = data.split('|')
+    gmail = GmailToolkit()
+    draft = GmailCreateDraft(api_resource=gmail.api_resource)
+    resutl = draft({
+				'to': [email],
+				'subject': subject,
+				'message': message
+		})
+    return f"\nDraft created: {resutl}\n"
+
+
+

CrewAI-LangGraph/src/graph.py

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+from dotenv import load_dotenv
+load_dotenv()
+
+from langgraph.graph import StateGraph
+
+from .state import EmailsState
+from .nodes import Nodes
+from .crew.crew import EmailFilterCrew
+
+class WorkFlow():
+	def __init__(self):
+		nodes = Nodes()
+		workflow = StateGraph(EmailsState)
+
+		workflow.add_node("check_new_emails", nodes.check_email)
+		workflow.add_node("wait_next_run", nodes.wait_next_run)
+		workflow.add_node("draft_responses", EmailFilterCrew().kickoff)
+
+		workflow.set_entry_point("check_new_emails")
+		workflow.add_conditional_edges(
+				"check_new_emails",
+				nodes.new_emails,
+				{
+					"continue": 'draft_responses',
+					"end": 'wait_next_run'
+				}
+		)
+		workflow.add_edge('draft_responses', 'wait_next_run')
+		workflow.add_edge('wait_next_run', 'check_new_emails')
+		self.app = workflow.compile()

CrewAI-LangGraph/src/nodes.py

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+import os
+import time
+
+from langchain_community.agent_toolkits import GmailToolkit
+from langchain_community.tools.gmail.search import GmailSearch
+
+class Nodes():
+	def __init__(self):
+		self.gmail = GmailToolkit()
+
+	def check_email(self, state):
+		print("# Checking for new emails")
+		search = GmailSearch(api_resource=self.gmail.api_resource)
+		emails = search('after:newer_than:1d')
+		checked_emails = state['checked_emails_ids'] if state['checked_emails_ids'] else []
+		thread = []
+		new_emails = []
+		for email in emails:
+			if (email['id'] not in checked_emails) and (email['threadId'] not in thread) and ( os.environ['MY_EMAIL'] not in email['sender']):
+				thread.append(email['threadId'])
+				new_emails.append(
+					{
+						"id": email['id'],
+						"threadId": email['threadId'],
+						"snippet": email['snippet'],
+						"sender": email["sender"]
+					}
+				)
+		checked_emails.extend([email['id'] for email in emails])
+		return {
+			**state,
+			"emails": new_emails,
+			"checked_emails_ids": checked_emails
+		}
+
+	def wait_next_run(self, state):
+		print("## Waiting for 180 seconds")
+		time.sleep(180)
+		return state
+
+	def new_emails(self, state):
+		if len(state['emails']) == 0:
+			print("## No new emails")
+			return "end"
+		else:
+			print("## New emails")
+			return "continue"
+

CrewAI-LangGraph/src/state.py

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+import datetime
+from typing import TypedDict
+
+class EmailsState(TypedDict):
+	checked_emails_ids: list[str]
+	emails: list[dict]
+	action_required_emails: dict