new one - ai gtm email
This commit is contained in:
Madhu
parent
3de66d37b5
commit
eeb32c6d44
7 changed files with 908 additions and 954 deletions
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import json
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import os
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import streamlit as st
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from datetime import datetime
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from textwrap import dedent
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from typing import Dict, Iterator, List, Optional, Literal
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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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from agno.storage.sqlite import SqliteStorage
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from agno.tools.exa import ExaTools
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from agno.utils.log import logger
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from agno.utils.pprint import pprint_run_response
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from agno.workflow import RunResponse, Workflow
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from pydantic import BaseModel, Field
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# Initialize API keys from environment or empty defaults
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if 'EXA_API_KEY' not in st.session_state:
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st.session_state.EXA_API_KEY = os.getenv("EXA_API_KEY", "")
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if 'OPENAI_API_KEY' not in st.session_state:
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st.session_state.OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
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# Set environment variables
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os.environ["EXA_API_KEY"] = st.session_state.EXA_API_KEY
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os.environ["OPENAI_API_KEY"] = st.session_state.OPENAI_API_KEY
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# Demo mode
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# - set to True to print email to console
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# - set to False to send to yourself
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DEMO_MODE = True
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today = datetime.now().strftime("%Y-%m-%d")
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# Example leads - Replace with your actual targets
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leads: Dict[str, Dict[str, str]] = {
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"Notion": {
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"name": "Notion",
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"website": "https://www.notion.so",
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"contact_name": "Ivan Zhao",
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"position": "CEO",
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},
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# Add more companies as needed
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}
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# Updated sender details for an AI analytics company
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sender_details_dict: Dict[str, str] = {
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"name": "Sarah Chen",
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"email": "your.email@company.com", # Your email goes here
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"organization": "Data Consultants Inc",
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"service_offered": "We help build data products and offer data consulting services",
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"calendar_link": "https://calendly.com/data-consultants-inc",
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"linkedin": "https://linkedin.com/in/your-profile",
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"phone": "+1 (555) 123-4567",
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"website": "https://www.data-consultants.com",
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}
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DEPARTMENT_TEMPLATES = {
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"GTM (Sales & Marketing)": {
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"Software Solution": """\
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Hey [RECIPIENT_NAME],
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I noticed [COMPANY_NAME]'s impressive [GTM_INITIATIVE] and your role in scaling [SPECIFIC_ACHIEVEMENT]. Your approach to [SALES_STRATEGY] caught my attention.
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[PRODUCT_VALUE_FOR_GTM]
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[GTM_SPECIFIC_BENEFIT]
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Would love to show you how this could work for your team: [CALENDAR_LINK]
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Best,
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[SIGNATURE]\
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""",
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"Consulting Services": """\
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Hey [RECIPIENT_NAME],
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Your team's recent success with [CAMPAIGN_NAME] is impressive, particularly the [SPECIFIC_METRIC].
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[CONSULTING_VALUE_PROP]
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[GTM_IMPROVEMENT_POTENTIAL]
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Here's my calendar if you'd like to explore this: [CALENDAR_LINK]
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Best,
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[SIGNATURE]\
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"""
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},
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"Human Resources": {
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"Software Solution": """\
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Hey [RECIPIENT_NAME],
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I've been following [COMPANY_NAME]'s growth and noticed your focus on [HR_INITIATIVE]. Your approach to [SPECIFIC_HR_PROGRAM] stands out.
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[HR_TOOL_VALUE_PROP]
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[HR_SPECIFIC_BENEFIT]
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Would you be open to seeing how this could help your HR initiatives? [CALENDAR_LINK]
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Best,
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[SIGNATURE]\
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""",
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"Consulting Services": """\
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Hey [RECIPIENT_NAME],
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I've been following [COMPANY_NAME]'s journey in [INDUSTRY], and your recent [ACHIEVEMENT] caught my attention. Your approach to [SPECIFIC_FOCUS] aligns perfectly with what we're building.
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[PARTNERSHIP_VALUE_PROP]
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[MUTUAL_BENEFIT]
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Would love to explore potential synergies over a quick call: [CALENDAR_LINK]
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Best,
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[SIGNATURE]\
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""",
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"Investment Opportunity": """\
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Hey [RECIPIENT_NAME],
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Your work at [COMPANY_NAME] in [SPECIFIC_FOCUS] is impressive, especially [RECENT_ACHIEVEMENT].
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[INVESTMENT_THESIS]
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[UNIQUE_VALUE_ADD]
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Here's my calendar if you'd like to discuss: [CALENDAR_LINK]
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Best,
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[SIGNATURE]\
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"""
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},
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"Marketing Professional": {
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"Product Demo": """\
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Hey [RECIPIENT_NAME],
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I noticed [COMPANY_NAME]'s recent [MARKETING_INITIATIVE] and was impressed by [SPECIFIC_DETAIL].
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[PRODUCT_VALUE_PROP]
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[BENEFIT_TO_MARKETING]
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Would you be open to a quick demo? Here's my calendar: [CALENDAR_LINK]
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Best,
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[SIGNATURE]\
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""",
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"Service Offering": """\
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Hey [RECIPIENT_NAME],
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Saw your team's work on [RECENT_CAMPAIGN] - great execution on [SPECIFIC_ELEMENT].
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[SERVICE_VALUE_PROP]
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[MARKETING_BENEFIT]
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Here's my calendar if you'd like to explore this: [CALENDAR_LINK]
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Best,
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[SIGNATURE]\
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"""
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},
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"B2B Sales Representative": {
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"Product Demo": """\
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Hey [RECIPIENT_NAME],
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Noticed your team at [COMPANY_NAME] is scaling [SALES_FOCUS]. Your approach to [SPECIFIC_STRATEGY] is spot-on.
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[PRODUCT_VALUE_PROP]
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[SALES_BENEFIT]
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Would you be interested in seeing how this works? Here's my calendar: [CALENDAR_LINK]
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Best,
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[SIGNATURE]\
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""",
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"Service Offering": """\
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Hey [RECIPIENT_NAME],
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Your sales team's success with [RECENT_WIN] caught my attention. Particularly impressed by [SPECIFIC_ACHIEVEMENT].
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[SERVICE_VALUE_PROP]
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[SALES_IMPROVEMENT]
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Here's my calendar if you'd like to discuss: [CALENDAR_LINK]
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Best,
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[SIGNATURE]\
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"""
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}
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}
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COMPANY_CATEGORIES = {
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"SaaS/Technology Companies": {
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"description": "Software, cloud services, and tech platforms",
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"typical_roles": ["CTO", "Head of Engineering", "VP of Product", "Engineering Manager", "Tech Lead"]
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},
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"E-commerce/Retail": {
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"description": "Online retail, marketplaces, and D2C brands",
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"typical_roles": ["Head of Digital", "E-commerce Manager", "Marketing Director", "Operations Head"]
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},
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"Financial Services": {
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"description": "Banks, fintech, insurance, and investment firms",
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"typical_roles": ["CFO", "Head of Innovation", "Risk Manager", "Product Manager"]
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},
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"Healthcare/Biotech": {
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"description": "Healthcare providers, biotech, and health tech",
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"typical_roles": ["Medical Director", "Head of R&D", "Clinical Manager", "Healthcare IT Lead"]
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},
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"Manufacturing/Industrial": {
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"description": "Manufacturing, industrial automation, and supply chain",
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"typical_roles": ["Operations Director", "Plant Manager", "Supply Chain Head", "Quality Manager"]
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}
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}
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class OutreachConfig(BaseModel):
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"""Configuration for email outreach"""
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company_category: str = Field(..., description="Type of companies to target")
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target_departments: List[str] = Field(
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...,
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description="Departments to target (e.g., GTM, HR, Engineering)"
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)
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service_type: Literal[
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"Software Solution",
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"Consulting Services",
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"Professional Services",
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"Technology Platform",
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"Custom Development"
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] = Field(..., description="Type of service being offered")
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company_size_preference: Literal["Startup (1-50)", "SMB (51-500)", "Enterprise (500+)", "All Sizes"] = Field(
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default="All Sizes",
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description="Preferred company size"
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)
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personalization_level: Literal["Basic", "Medium", "Deep"] = Field(
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default="Deep",
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description="Level of personalization"
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)
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class ContactInfo(BaseModel):
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"""Contact information for decision makers"""
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name: str = Field(..., description="Contact's full name")
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title: str = Field(..., description="Job title/position")
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email: Optional[str] = Field(None, description="Email address")
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linkedin: Optional[str] = Field(None, description="LinkedIn profile URL")
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company: str = Field(..., description="Company name")
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department: Optional[str] = Field(None, description="Department")
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background: Optional[str] = Field(None, description="Professional background")
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class CompanyInfo(BaseModel):
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"""
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Stores in-depth data about a company gathered during the research phase.
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"""
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# Basic Information
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company_name: str = Field(..., description="Company name")
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website_url: str = Field(..., description="Company website URL")
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# Business Details
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industry: Optional[str] = Field(None, description="Primary industry")
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core_business: Optional[str] = Field(None, description="Main business focus")
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business_model: Optional[str] = Field(None, description="B2B, B2C, etc.")
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# Marketing Information
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motto: Optional[str] = Field(None, description="Company tagline/slogan")
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value_proposition: Optional[str] = Field(None, description="Main value proposition")
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target_audience: Optional[List[str]] = Field(
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None, description="Target customer segments"
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)
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# Company Metrics
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company_size: Optional[str] = Field(None, description="Employee count range")
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founded_year: Optional[int] = Field(None, description="Year founded")
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locations: Optional[List[str]] = Field(None, description="Office locations")
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# Technical Details
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technologies: Optional[List[str]] = Field(None, description="Technology stack")
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integrations: Optional[List[str]] = Field(None, description="Software integrations")
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# Market Position
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competitors: Optional[List[str]] = Field(None, description="Main competitors")
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unique_selling_points: Optional[List[str]] = Field(
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None, description="Key differentiators"
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)
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market_position: Optional[str] = Field(None, description="Market positioning")
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# Social Proof
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customers: Optional[List[str]] = Field(None, description="Notable customers")
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case_studies: Optional[List[str]] = Field(None, description="Success stories")
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awards: Optional[List[str]] = Field(None, description="Awards and recognition")
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# Recent Activity
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recent_news: Optional[List[str]] = Field(None, description="Recent news/updates")
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blog_topics: Optional[List[str]] = Field(None, description="Recent blog topics")
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# Pain Points & Opportunities
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challenges: Optional[List[str]] = Field(None, description="Potential pain points")
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growth_areas: Optional[List[str]] = Field(None, description="Growth opportunities")
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# Contact Information
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email_address: Optional[str] = Field(None, description="Contact email")
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phone: Optional[str] = Field(None, description="Contact phone")
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social_media: Optional[Dict[str, str]] = Field(
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None, description="Social media links"
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)
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# Additional Fields
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pricing_model: Optional[str] = Field(None, description="Pricing strategy and tiers")
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user_base: Optional[str] = Field(None, description="Estimated user base size")
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key_features: Optional[List[str]] = Field(None, description="Main product features")
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integration_ecosystem: Optional[List[str]] = Field(
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None, description="Integration partners"
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)
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funding_status: Optional[str] = Field(
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None, description="Latest funding information"
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)
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growth_metrics: Optional[Dict[str, str]] = Field(
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None, description="Key growth indicators"
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)
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class PersonalisedEmailGenerator(Workflow):
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"""
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Automated B2B outreach system that:
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1. Discovers companies using Exa search based on criteria
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2. Finds contact details for decision makers at those companies
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3. Researches company details and pain points
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4. Generates personalized cold emails for B2B outreach
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This workflow is designed to automate the entire prospecting process
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from company discovery to personalized email generation.
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"""
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description: str = dedent("""\
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AI-Powered B2B Outreach Workflow:
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--------------------------------------------------------
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1. Discover Target Companies (Exa Search)
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2. Find Decision Maker Contacts
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3. Research Company Intelligence
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4. Generate Personalized Emails
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--------------------------------------------------------
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Fully automated prospecting pipeline for B2B outreach.
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""")
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company_finder: Agent = Agent(
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model=OpenAIChat(id="gpt-5"),
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tools=[ExaTools(api_key=os.environ["EXA_API_KEY"])],
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description="Expert at finding companies that match specific criteria using web search",
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instructions=dedent("""\
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You are a company discovery specialist. Your job is to find companies that match the given criteria.
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Search for companies based on:
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- Industry/sector
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- Company size
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- Geographic location
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- Business model
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- Technology stack
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- Recent funding/growth
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For each company found, provide:
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- Company name
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- Website URL
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- Brief description
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- Industry
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- Estimated size
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- Location
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Focus on finding companies that would be good prospects for the specified service offering.
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Look for companies showing signs of growth, funding, or expansion.
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"""),
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)
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contact_finder: Agent = Agent(
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model=OpenAIChat(id="gpt-5"),
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tools=[ExaTools(api_key=os.environ["EXA_API_KEY"])],
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description="Expert at finding contact information for decision makers at companies",
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instructions=dedent("""\
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You are a contact research specialist. Find decision makers and their contact information.
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For each company, search for:
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- Key decision makers in target departments
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- Their email addresses
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- LinkedIn profiles
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- Professional backgrounds
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- Current role and responsibilities
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Focus on finding people in roles like:
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||||||
|
- CEO, CTO, VP of Engineering (for tech solutions)
|
||||||
|
- CMO, VP Marketing, Growth Lead (for marketing solutions)
|
||||||
|
- VP Sales, Sales Director (for sales solutions)
|
||||||
|
- HR Director, People Ops (for HR solutions)
|
||||||
|
|
||||||
|
Provide verified contact information when possible.
|
||||||
|
"""),
|
||||||
|
)
|
||||||
|
|
||||||
|
company_researcher: Agent = Agent(
|
||||||
|
model=OpenAIChat(id="gpt-5"),
|
||||||
|
tools=[ExaTools(api_key=os.environ["EXA_API_KEY"])],
|
||||||
|
description="Expert at researching company details for personalization",
|
||||||
|
instructions=dedent("""\
|
||||||
|
Research companies in depth to enable personalized outreach.
|
||||||
|
|
||||||
|
Analyze:
|
||||||
|
- Company website and messaging
|
||||||
|
- Recent news and updates
|
||||||
|
- Product/service offerings
|
||||||
|
- Technology stack
|
||||||
|
- Growth indicators
|
||||||
|
- Pain points and challenges
|
||||||
|
- Recent achievements
|
||||||
|
- Market position
|
||||||
|
|
||||||
|
Focus on insights that would be relevant for B2B outreach:
|
||||||
|
- Scaling challenges
|
||||||
|
- Technology needs
|
||||||
|
- Market expansion
|
||||||
|
- Competitive positioning
|
||||||
|
- Recent wins or milestones
|
||||||
|
"""),
|
||||||
|
response_model=CompanyInfo,
|
||||||
|
)
|
||||||
|
|
||||||
|
email_creator: Agent = Agent(
|
||||||
|
model=OpenAIChat(id="gpt-5"),
|
||||||
|
description=dedent("""\
|
||||||
|
You are writing for a friendly, empathetic 20-year-old sales rep whose
|
||||||
|
style is cool, concise, and respectful. Tone is casual yet professional.
|
||||||
|
|
||||||
|
- Be polite but natural, using simple language.
|
||||||
|
- Never sound robotic or use big cliché words like "delve", "synergy" or "revolutionary."
|
||||||
|
- Clearly address problems the prospect might be facing and how we solve them.
|
||||||
|
- Keep paragraphs short and friendly, with a natural voice.
|
||||||
|
- End on a warm, upbeat note, showing willingness to help.\
|
||||||
|
"""),
|
||||||
|
instructions=dedent("""\
|
||||||
|
Please craft a highly personalized email that has:
|
||||||
|
|
||||||
|
1. A simple, personal subject line referencing the problem or opportunity.
|
||||||
|
2. At least one area for improvement or highlight from research.
|
||||||
|
3. A quick explanation of how we can help them (no heavy jargon).
|
||||||
|
4. References a known challenge from the research.
|
||||||
|
5. Avoid words like "delve", "explore", "synergy", "amplify", "game changer", "revolutionary", "breakthrough".
|
||||||
|
6. Use first-person language ("I") naturally.
|
||||||
|
7. Maintain a 20-year-old's friendly style—brief and to the point.
|
||||||
|
8. Avoid placing the recipient's name in the subject line.
|
||||||
|
|
||||||
|
Use the appropriate template based on the target professional type and outreach purpose.
|
||||||
|
Ensure the final tone feels personal and conversation-like, not automatically generated.
|
||||||
|
----------------------------------------------------------------------
|
||||||
|
"""),
|
||||||
|
markdown=False,
|
||||||
|
add_datetime_to_instructions=True,
|
||||||
|
)
|
||||||
|
|
||||||
|
def get_cached_data(self, cache_key: str) -> Optional[dict]:
|
||||||
|
"""Retrieve cached data"""
|
||||||
|
logger.info(f"Checking cache for: {cache_key}")
|
||||||
|
return self.session_state.get("cache", {}).get(cache_key)
|
||||||
|
|
||||||
|
def cache_data(self, cache_key: str, data: dict):
|
||||||
|
"""Cache data"""
|
||||||
|
logger.info(f"Caching data for: {cache_key}")
|
||||||
|
self.session_state.setdefault("cache", {})
|
||||||
|
self.session_state["cache"][cache_key] = data
|
||||||
|
self.write_to_storage()
|
||||||
|
|
||||||
|
def run(
|
||||||
|
self,
|
||||||
|
config: OutreachConfig,
|
||||||
|
sender_details: Dict[str, str],
|
||||||
|
num_companies: int = 5,
|
||||||
|
use_cache: bool = True,
|
||||||
|
) -> Iterator[RunResponse]:
|
||||||
|
"""
|
||||||
|
Automated B2B outreach workflow:
|
||||||
|
|
||||||
|
1. Discover companies using Exa search based on criteria
|
||||||
|
2. Find decision maker contacts for each company
|
||||||
|
3. Research company details for personalization
|
||||||
|
4. Generate personalized emails
|
||||||
|
"""
|
||||||
|
logger.info("Starting automated B2B outreach workflow...")
|
||||||
|
|
||||||
|
# Step 1: Discover companies
|
||||||
|
logger.info("🔍 Discovering target companies...")
|
||||||
|
search_query = f"""
|
||||||
|
Find {num_companies} {config.company_category} companies that would be good prospects for {config.service_type}.
|
||||||
|
|
||||||
|
Company criteria:
|
||||||
|
- Industry: {config.company_category}
|
||||||
|
- Size: {config.company_size_preference}
|
||||||
|
- Target departments: {', '.join(config.target_departments)}
|
||||||
|
|
||||||
|
Look for companies showing growth, recent funding, or expansion.
|
||||||
|
"""
|
||||||
|
|
||||||
|
companies_response = self.company_finder.run(search_query)
|
||||||
|
if not companies_response or not companies_response.content:
|
||||||
|
logger.error("No companies found")
|
||||||
|
return
|
||||||
|
|
||||||
|
# Parse companies from response
|
||||||
|
companies_text = companies_response.content
|
||||||
|
logger.info(f"Found companies: {companies_text[:200]}...")
|
||||||
|
|
||||||
|
# Step 2: For each company, find contacts and research
|
||||||
|
for i in range(num_companies):
|
||||||
|
try:
|
||||||
|
logger.info(f"Processing company #{i+1}")
|
||||||
|
|
||||||
|
# Extract company info from the response
|
||||||
|
company_search = f"Extract company #{i+1} details from: {companies_text}"
|
||||||
|
|
||||||
|
# Step 3: Find decision maker contacts
|
||||||
|
logger.info("👥 Finding decision maker contacts...")
|
||||||
|
contacts_query = f"""
|
||||||
|
Find decision makers at company #{i+1} from this list: {companies_text}
|
||||||
|
|
||||||
|
Focus on roles in: {', '.join(config.target_departments)}
|
||||||
|
Find their email addresses and LinkedIn profiles.
|
||||||
|
"""
|
||||||
|
|
||||||
|
contacts_response = self.contact_finder.run(contacts_query)
|
||||||
|
if not contacts_response or not contacts_response.content:
|
||||||
|
logger.warning(f"No contacts found for company #{i+1}")
|
||||||
|
continue
|
||||||
|
|
||||||
|
# Step 4: Research company details
|
||||||
|
logger.info("🔬 Researching company details...")
|
||||||
|
research_query = f"""
|
||||||
|
Research company #{i+1} from this list: {companies_text}
|
||||||
|
|
||||||
|
Focus on insights relevant for {config.service_type} outreach.
|
||||||
|
Find pain points related to {', '.join(config.target_departments)}.
|
||||||
|
"""
|
||||||
|
|
||||||
|
research_response = self.company_researcher.run(research_query)
|
||||||
|
if not research_response or not research_response.content:
|
||||||
|
logger.warning(f"No research data for company #{i+1}")
|
||||||
|
continue
|
||||||
|
|
||||||
|
company_data = research_response.content
|
||||||
|
if not isinstance(company_data, CompanyInfo):
|
||||||
|
logger.warning(f"Invalid research data format for company #{i+1}")
|
||||||
|
continue
|
||||||
|
|
||||||
|
# Step 5: Generate personalized email
|
||||||
|
logger.info("✉️ Generating personalized email...")
|
||||||
|
|
||||||
|
# Get appropriate template based on target departments
|
||||||
|
template_dept = config.target_departments[0] if config.target_departments else "GTM (Sales & Marketing)"
|
||||||
|
if template_dept in DEPARTMENT_TEMPLATES and config.service_type in DEPARTMENT_TEMPLATES[template_dept]:
|
||||||
|
template = DEPARTMENT_TEMPLATES[template_dept][config.service_type]
|
||||||
|
else:
|
||||||
|
template = DEPARTMENT_TEMPLATES["GTM (Sales & Marketing)"]["Software Solution"]
|
||||||
|
|
||||||
|
email_context = json.dumps(
|
||||||
|
{
|
||||||
|
"template": template,
|
||||||
|
"company_info": company_data.model_dump(),
|
||||||
|
"contacts_info": contacts_response.content,
|
||||||
|
"sender_details": sender_details,
|
||||||
|
"target_departments": config.target_departments,
|
||||||
|
"service_type": config.service_type,
|
||||||
|
"personalization_level": config.personalization_level
|
||||||
|
},
|
||||||
|
indent=4,
|
||||||
|
)
|
||||||
|
|
||||||
|
email_response = self.email_creator.run(
|
||||||
|
f"Generate a personalized email using this context:\n{email_context}"
|
||||||
|
)
|
||||||
|
|
||||||
|
if not email_response or not email_response.content:
|
||||||
|
logger.warning(f"No email generated for company #{i+1}")
|
||||||
|
continue
|
||||||
|
|
||||||
|
yield RunResponse(content={
|
||||||
|
"company_name": company_data.company_name,
|
||||||
|
"email": email_response.content,
|
||||||
|
"company_data": company_data.model_dump(),
|
||||||
|
"contacts": contacts_response.content,
|
||||||
|
"step": f"Company {i+1}/{num_companies}"
|
||||||
|
})
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(f"Error processing company #{i+1}: {e}")
|
||||||
|
continue
|
||||||
|
|
||||||
|
|
||||||
|
def create_streamlit_ui():
|
||||||
|
"""Create the Streamlit user interface"""
|
||||||
|
st.title("🚀 Automated B2B Email Outreach Generator")
|
||||||
|
st.markdown("""
|
||||||
|
**Fully automated prospecting pipeline**: Discovers companies, finds decision makers,
|
||||||
|
and generates personalized emails using AI research agents.
|
||||||
|
""")
|
||||||
|
|
||||||
|
# Step 1: Target Company Category Selection
|
||||||
|
st.header("1️⃣ Target Company Discovery")
|
||||||
|
|
||||||
|
col1, col2 = st.columns([2, 1])
|
||||||
|
|
||||||
|
with col1:
|
||||||
|
selected_category = st.selectbox(
|
||||||
|
"What type of companies should we target?",
|
||||||
|
options=list(COMPANY_CATEGORIES.keys()),
|
||||||
|
key="company_category"
|
||||||
|
)
|
||||||
|
|
||||||
|
st.info(f"📌 {COMPANY_CATEGORIES[selected_category]['description']}")
|
||||||
|
|
||||||
|
st.markdown("### Typical Decision Makers We'll Find:")
|
||||||
|
for role in COMPANY_CATEGORIES[selected_category]['typical_roles']:
|
||||||
|
st.markdown(f"- {role}")
|
||||||
|
|
||||||
|
with col2:
|
||||||
|
st.markdown("### Company Size Filter")
|
||||||
|
company_size = st.radio(
|
||||||
|
"Preferred company size",
|
||||||
|
["All Sizes", "Startup (1-50)", "SMB (51-500)", "Enterprise (500+)"],
|
||||||
|
key="company_size"
|
||||||
|
)
|
||||||
|
|
||||||
|
num_companies = st.number_input(
|
||||||
|
"Number of companies to find",
|
||||||
|
min_value=1,
|
||||||
|
max_value=20,
|
||||||
|
value=5,
|
||||||
|
help="AI will discover this many companies automatically"
|
||||||
|
)
|
||||||
|
|
||||||
|
# Step 2: Your Information
|
||||||
|
st.header("2️⃣ Your Contact Information")
|
||||||
|
|
||||||
|
col3, col4 = st.columns(2)
|
||||||
|
|
||||||
|
with col3:
|
||||||
|
st.subheader("Required Information")
|
||||||
|
sender_details = {
|
||||||
|
"name": st.text_input("Your Name *", key="sender_name"),
|
||||||
|
"email": st.text_input("Your Email *", key="sender_email"),
|
||||||
|
"organization": st.text_input("Your Organization *", key="sender_org")
|
||||||
|
}
|
||||||
|
|
||||||
|
with col4:
|
||||||
|
st.subheader("Optional Information")
|
||||||
|
sender_details.update({
|
||||||
|
"linkedin": st.text_input("LinkedIn Profile (optional)", key="sender_linkedin", placeholder="https://linkedin.com/in/yourname"),
|
||||||
|
"phone": st.text_input("Phone Number (optional)", key="sender_phone", placeholder="+1 (555) 123-4567"),
|
||||||
|
"website": st.text_input("Company Website (optional)", key="sender_website", placeholder="https://yourcompany.com"),
|
||||||
|
"calendar_link": st.text_input("Calendar Link (optional)", key="sender_calendar", placeholder="https://calendly.com/yourname")
|
||||||
|
})
|
||||||
|
|
||||||
|
# Service description
|
||||||
|
sender_details["service_offered"] = st.text_area(
|
||||||
|
"Describe your offering *",
|
||||||
|
height=100,
|
||||||
|
key="service_description",
|
||||||
|
help="Explain what you offer and how it helps businesses",
|
||||||
|
placeholder="We help companies build custom AI solutions that automate workflows and improve efficiency..."
|
||||||
|
)
|
||||||
|
|
||||||
|
# Step 3: Service Type and Targeting
|
||||||
|
st.header("3️⃣ Outreach Configuration")
|
||||||
|
|
||||||
|
col5, col6 = st.columns(2)
|
||||||
|
|
||||||
|
with col5:
|
||||||
|
service_type = st.selectbox(
|
||||||
|
"Service/Product Category",
|
||||||
|
[
|
||||||
|
"Software Solution",
|
||||||
|
"Consulting Services",
|
||||||
|
"Professional Services",
|
||||||
|
"Technology Platform",
|
||||||
|
"Custom Development"
|
||||||
|
],
|
||||||
|
key="service_type"
|
||||||
|
)
|
||||||
|
|
||||||
|
with col6:
|
||||||
|
personalization_level = st.select_slider(
|
||||||
|
"Email Personalization Level",
|
||||||
|
options=["Basic", "Medium", "Deep"],
|
||||||
|
value="Deep",
|
||||||
|
help="Deep personalization takes longer but produces better results"
|
||||||
|
)
|
||||||
|
|
||||||
|
# Step 4: Target Department Selection
|
||||||
|
target_departments = st.multiselect(
|
||||||
|
"Which departments should we target?",
|
||||||
|
[
|
||||||
|
"GTM (Sales & Marketing)",
|
||||||
|
"Human Resources",
|
||||||
|
"Engineering/Tech",
|
||||||
|
"Operations",
|
||||||
|
"Finance",
|
||||||
|
"Product",
|
||||||
|
"Executive Leadership"
|
||||||
|
],
|
||||||
|
default=["GTM (Sales & Marketing)"],
|
||||||
|
key="target_departments",
|
||||||
|
help="AI will find decision makers in these departments"
|
||||||
|
)
|
||||||
|
|
||||||
|
# Validate required inputs
|
||||||
|
required_fields = ["name", "email", "organization", "service_offered"]
|
||||||
|
missing_fields = [field for field in required_fields if not sender_details.get(field)]
|
||||||
|
|
||||||
|
if missing_fields:
|
||||||
|
st.error(f"Please fill in required fields: {', '.join(missing_fields)}")
|
||||||
|
st.stop()
|
||||||
|
|
||||||
|
if not target_departments:
|
||||||
|
st.error("Please select at least one target department")
|
||||||
|
st.stop()
|
||||||
|
|
||||||
|
if not selected_category:
|
||||||
|
st.error("Please select a company category")
|
||||||
|
st.stop()
|
||||||
|
|
||||||
|
if not service_type:
|
||||||
|
st.error("Please select a service type")
|
||||||
|
st.stop()
|
||||||
|
|
||||||
|
# Create and return configuration
|
||||||
|
outreach_config = OutreachConfig(
|
||||||
|
company_category=selected_category,
|
||||||
|
target_departments=target_departments,
|
||||||
|
service_type=service_type,
|
||||||
|
company_size_preference=company_size,
|
||||||
|
personalization_level=personalization_level
|
||||||
|
)
|
||||||
|
|
||||||
|
return outreach_config, sender_details, num_companies
|
||||||
|
|
||||||
|
def main():
|
||||||
|
"""
|
||||||
|
Main entry point for running the automated B2B outreach workflow.
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
# Set page config must be the first Streamlit command
|
||||||
|
st.set_page_config(
|
||||||
|
page_title="Automated B2B Email Outreach",
|
||||||
|
layout="wide",
|
||||||
|
initial_sidebar_state="expanded"
|
||||||
|
)
|
||||||
|
|
||||||
|
# API Keys in Sidebar
|
||||||
|
st.sidebar.header("🔑 API Configuration")
|
||||||
|
|
||||||
|
# Update API keys from sidebar
|
||||||
|
st.session_state.EXA_API_KEY = st.sidebar.text_input(
|
||||||
|
"Exa API Key *",
|
||||||
|
value=st.session_state.EXA_API_KEY,
|
||||||
|
type="password",
|
||||||
|
key="exa_key_input",
|
||||||
|
help="Get your Exa API key from https://exa.ai"
|
||||||
|
)
|
||||||
|
st.session_state.OPENAI_API_KEY = st.sidebar.text_input(
|
||||||
|
"OpenAI API Key *",
|
||||||
|
value=st.session_state.OPENAI_API_KEY,
|
||||||
|
type="password",
|
||||||
|
key="openai_key_input",
|
||||||
|
help="Get your OpenAI API key from https://platform.openai.com"
|
||||||
|
)
|
||||||
|
|
||||||
|
# Update environment variables
|
||||||
|
os.environ["EXA_API_KEY"] = st.session_state.EXA_API_KEY
|
||||||
|
os.environ["OPENAI_API_KEY"] = st.session_state.OPENAI_API_KEY
|
||||||
|
|
||||||
|
# Validate API keys
|
||||||
|
if not st.session_state.EXA_API_KEY or not st.session_state.OPENAI_API_KEY:
|
||||||
|
st.sidebar.error("⚠️ Both API keys are required to run the application")
|
||||||
|
else:
|
||||||
|
st.sidebar.success("✅ API keys configured")
|
||||||
|
|
||||||
|
# Add guidance about API keys
|
||||||
|
st.sidebar.info("""
|
||||||
|
**API Keys Required:**
|
||||||
|
- Exa API key for company research
|
||||||
|
- OpenAI API key for email generation
|
||||||
|
|
||||||
|
Set these in your environment variables or enter them above.
|
||||||
|
""")
|
||||||
|
|
||||||
|
# Get user inputs from the UI
|
||||||
|
try:
|
||||||
|
config, sender_details, num_companies = create_streamlit_ui()
|
||||||
|
except Exception as e:
|
||||||
|
st.error(f"Configuration error: {str(e)}")
|
||||||
|
st.stop()
|
||||||
|
|
||||||
|
# Generate Emails Section
|
||||||
|
st.header("4️⃣ Generate Outreach Campaign")
|
||||||
|
|
||||||
|
st.info(f"""
|
||||||
|
**Ready to launch automated prospecting:**
|
||||||
|
- Target: {config.company_category} companies ({config.company_size_preference})
|
||||||
|
- Departments: {', '.join(config.target_departments)}
|
||||||
|
- Service: {config.service_type}
|
||||||
|
- Companies to find: {num_companies}
|
||||||
|
""")
|
||||||
|
|
||||||
|
if st.button("🚀 Start Automated Campaign", key="generate_button", type="primary"):
|
||||||
|
# Check if API keys are configured
|
||||||
|
if not st.session_state.EXA_API_KEY or not st.session_state.OPENAI_API_KEY:
|
||||||
|
st.error("❌ Please configure both API keys before starting the campaign")
|
||||||
|
st.stop()
|
||||||
|
|
||||||
|
try:
|
||||||
|
# Progress tracking
|
||||||
|
progress_bar = st.progress(0)
|
||||||
|
status_text = st.empty()
|
||||||
|
results_container = st.container()
|
||||||
|
|
||||||
|
with st.spinner("Initializing AI research agents..."):
|
||||||
|
workflow = PersonalisedEmailGenerator(
|
||||||
|
session_id="streamlit-email-generator",
|
||||||
|
storage=SqliteStorage(
|
||||||
|
table_name="email_generator_workflows",
|
||||||
|
db_file="tmp/agno_workflows.db"
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
status_text.text("🔍 Discovering companies and generating emails...")
|
||||||
|
|
||||||
|
# Process companies and display results
|
||||||
|
results_count = 0
|
||||||
|
for result in workflow.run(
|
||||||
|
config=config,
|
||||||
|
sender_details=sender_details,
|
||||||
|
num_companies=num_companies,
|
||||||
|
use_cache=True
|
||||||
|
):
|
||||||
|
results_count += 1
|
||||||
|
progress = results_count / num_companies
|
||||||
|
progress_bar.progress(progress)
|
||||||
|
status_text.text(f"✅ {result.content['step']} completed")
|
||||||
|
|
||||||
|
with results_container:
|
||||||
|
st.subheader(f"📧 Email for {result.content['company_name']}")
|
||||||
|
|
||||||
|
# Create tabs for different information
|
||||||
|
tab1, tab2, tab3 = st.tabs(["Generated Email", "Company Research", "Contacts Found"])
|
||||||
|
|
||||||
|
with tab1:
|
||||||
|
st.text_area(
|
||||||
|
"Personalized Email",
|
||||||
|
result.content['email'],
|
||||||
|
height=400,
|
||||||
|
key=f"email_{result.content['company_name']}_{results_count}"
|
||||||
|
)
|
||||||
|
|
||||||
|
# Copy button
|
||||||
|
if st.button(f"📋 Copy Email", key=f"copy_{result.content['company_name']}_{results_count}"):
|
||||||
|
st.success("Email content copied!")
|
||||||
|
|
||||||
|
with tab2:
|
||||||
|
st.json(result.content['company_data'])
|
||||||
|
|
||||||
|
with tab3:
|
||||||
|
st.text(result.content['contacts'])
|
||||||
|
|
||||||
|
st.markdown("---")
|
||||||
|
|
||||||
|
# Final status
|
||||||
|
if results_count > 0:
|
||||||
|
progress_bar.progress(1.0)
|
||||||
|
status_text.text(f"🎉 Campaign complete! Generated {results_count} personalized emails")
|
||||||
|
st.balloons()
|
||||||
|
else:
|
||||||
|
st.error("No emails were generated. Please try adjusting your criteria.")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
st.error(f"Campaign failed: {str(e)}")
|
||||||
|
logger.error(f"Workflow failed: {e}")
|
||||||
|
st.exception(e)
|
||||||
|
|
||||||
|
st.sidebar.markdown("### About")
|
||||||
|
st.sidebar.markdown(
|
||||||
|
"""
|
||||||
|
**Automated B2B Outreach Tool**
|
||||||
|
|
||||||
|
This tool uses AI agents to:
|
||||||
|
- Discover target companies automatically
|
||||||
|
- Find decision maker contacts
|
||||||
|
- Research company intelligence
|
||||||
|
- Generate personalized emails
|
||||||
|
|
||||||
|
Perfect for sales teams, agencies, and consultants.
|
||||||
|
"""
|
||||||
|
)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(f"Workflow failed: {e}")
|
||||||
|
st.error(f"An error occurred: {str(e)}")
|
||||||
|
raise
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
|
|
@ -0,0 +1,4 @@
|
||||||
|
agno>=0.1.0
|
||||||
|
streamlit>=1.32.0
|
||||||
|
pydantic>=2.0.0
|
||||||
|
openai>=1.0.0
|
||||||
|
|
@ -1,481 +0,0 @@
|
||||||
# Enterprise MCP AI Agent Team
|
|
||||||
|
|
||||||
A production-grade multi-agent system built with Google ADK that orchestrates knowledge management across local files and SaaS platforms using MCP (Model Context Protocol).
|
|
||||||
|
|
||||||
## Overview
|
|
||||||
|
|
||||||
This system combines:
|
|
||||||
- **Local Filesystem MCP Server** - for accessing and analyzing local documents
|
|
||||||
- **Notion MCP Server** - for managing Notion workspaces and content
|
|
||||||
- **Composio MCP Server** - for GitHub and Figma integration
|
|
||||||
- **Intelligent Router/Orchestrator** - context-aware task delegation with state management
|
|
||||||
- **4 Specialized AI Agents** - each handling specific platform capabilities
|
|
||||||
|
|
||||||
## Architecture
|
|
||||||
|
|
||||||
```
|
|
||||||
┌─────────────────────────────────────────────────────────────┐
|
|
||||||
│ Enterprise MCP AI Agent Team │
|
|
||||||
│ (Coordinator/Dispatcher Pattern) │
|
|
||||||
├─────────────────────────────────────────────────────────────┤
|
|
||||||
│ ┌─────────────────┐ ┌─────────────────┐ ┌──────────────┐ │
|
|
||||||
│ │ File Analysis │ │ Notion Agent │ │ GitHub Agent │ │
|
|
||||||
│ │ AI Agent │ │ (Optional) │ │ (Optional) │ │
|
|
||||||
│ └─────────────────┘ └─────────────────┘ └──────────────┘ │
|
|
||||||
│ │ │ │ │
|
|
||||||
│ ┌─────────────────┐ ┌─────────────────┐ ┌──────────────┐ │
|
|
||||||
│ │ Filesystem MCP │ │ Notion MCP │ │ Composio MCP │ │
|
|
||||||
│ │ Server │ │ Server │ │ Server │ │
|
|
||||||
│ └─────────────────┘ └─────────────────┘ └──────────────┘ │
|
|
||||||
│ │ │ │ │
|
|
||||||
│ ┌─────────────────┐ ┌─────────────────┐ ┌──────────────┐ │
|
|
||||||
│ │ Local Documents │ │ Notion Pages & │ │ GitHub Repos │ │
|
|
||||||
│ │ (PDF, DOC, XLS) │ │ Databases │ │ & Issues │ │
|
|
||||||
│ └─────────────────┘ └─────────────────┘ └──────────────┘ │
|
|
||||||
│ │
|
|
||||||
│ ┌─────────────────┐ ┌─────────────────┐ │
|
|
||||||
│ │ Figma Agent │ │ Composio MCP │ │
|
|
||||||
│ │ (Optional) │ │ Server │ │
|
|
||||||
│ └─────────────────┘ └─────────────────┘ │
|
|
||||||
│ │ │ │
|
|
||||||
│ ┌─────────────────┐ ┌─────────────────┐ │
|
|
||||||
│ │ Figma Files & │ │ Figma Designs & │ │
|
|
||||||
│ │ Designs │ │ Assets │ │
|
|
||||||
│ └─────────────────┘ └─────────────────┘ │
|
|
||||||
└─────────────────────────────────────────────────────────────┘
|
|
||||||
```
|
|
||||||
|
|
||||||
### **Routing Patterns:**
|
|
||||||
|
|
||||||
1. **Coordinator/Dispatcher Pattern**: Intelligent routing based on query analysis
|
|
||||||
2. **LLM-Driven Delegation**: Automatic agent selection using `transfer_to_agent()`
|
|
||||||
3. **Explicit Invocation**: Direct agent calls using `AgentTool`
|
|
||||||
4. **Graceful Degradation**: System works with any combination of available agents
|
|
||||||
|
|
||||||
## Features
|
|
||||||
|
|
||||||
### 🔍 File Analysis Agent
|
|
||||||
- Analyzes local documents (PDFs, Word docs, spreadsheets)
|
|
||||||
- Extracts key topics, summaries, and action items
|
|
||||||
- Categorizes documents by type and content
|
|
||||||
- Identifies information for knowledge base sync
|
|
||||||
|
|
||||||
### 📝 Notion Agent
|
|
||||||
- Reads, writes, and updates Notion pages and databases
|
|
||||||
- Searches for content across Notion workspace
|
|
||||||
- Creates structured knowledge bases and documentation
|
|
||||||
- Syncs content from other sources to Notion
|
|
||||||
|
|
||||||
### 🐙 GitHub Agent
|
|
||||||
- Creates and manages GitHub issues and pull requests
|
|
||||||
- Searches repositories and code
|
|
||||||
- Manages repository content and documentation
|
|
||||||
- Sets up automated workflows and actions
|
|
||||||
|
|
||||||
### 🎨 Figma Agent
|
|
||||||
- Reads and analyzes Figma files and designs
|
|
||||||
- Exports design assets and components
|
|
||||||
- Searches for design elements and styles
|
|
||||||
- Manages design system components
|
|
||||||
|
|
||||||
### 🎯 Enterprise MCP AI Agent Team (Router/Orchestrator)
|
|
||||||
- Analyzes user requests and determines which AI agents should handle them
|
|
||||||
- Routes tasks to appropriate specialized AI agents based on capabilities
|
|
||||||
- Coordinates multi-step workflows that require multiple AI agents
|
|
||||||
- Shares context and results between AI agents through session state
|
|
||||||
- Provides comprehensive results and recommendations
|
|
||||||
|
|
||||||
### 🛡️ Error Handling & Graceful Degradation
|
|
||||||
- **MCP Server Failures**: Graceful fallback when servers are unavailable
|
|
||||||
- **Missing Environment Variables**: System works with available APIs only
|
|
||||||
- **Agent Creation Failures**: Continues with available agents
|
|
||||||
- **Validation**: Ensures at least one agent is available before operation
|
|
||||||
- **Comprehensive Logging**: Detailed logs for troubleshooting
|
|
||||||
|
|
||||||
## Prerequisites
|
|
||||||
|
|
||||||
1. **Python 3.9+** and **Node.js** (for MCP servers)
|
|
||||||
2. **Google ADK** installed and configured
|
|
||||||
3. **Notion API Key** for Notion integration
|
|
||||||
4. **Required API Keys** in environment variables
|
|
||||||
|
|
||||||
## Setup
|
|
||||||
|
|
||||||
### 1. Environment Variables
|
|
||||||
|
|
||||||
Create a `.env` file in the project root:
|
|
||||||
|
|
||||||
```bash
|
|
||||||
# Required: Google Gemini API
|
|
||||||
GOOGLE_API_KEY=your_gemini_api_key_here
|
|
||||||
|
|
||||||
# Required: API Keys for MCP Tools
|
|
||||||
NOTION_API_KEY=your_notion_api_key_here
|
|
||||||
GITHUB_API_KEY=your_github_api_key_here
|
|
||||||
FIGMA_API_KEY=your_figma_api_key_here
|
|
||||||
|
|
||||||
# Optional: Custom filesystem path (defaults to ~/Documents)
|
|
||||||
MCP_FILESYSTEM_PATH=/Users/madhushantan/Downloads
|
|
||||||
|
|
||||||
```
|
|
||||||
|
|
||||||
### 2. Notion Setup
|
|
||||||
|
|
||||||
#### Creating a Notion Integration
|
|
||||||
1. Go to [Notion Integrations](https://www.notion.so/my-integrations)
|
|
||||||
2. Click "New integration"
|
|
||||||
3. Name your integration (e.g., "Enterprise Knowledge Orchestrator")
|
|
||||||
4. Select the capabilities needed (Read & Write content)
|
|
||||||
5. Submit and copy your "Internal Integration Token"
|
|
||||||
|
|
||||||
#### Sharing Your Notion Page with the Integration
|
|
||||||
1. Open your Notion page
|
|
||||||
2. Click the three dots (⋮) in the top-right corner
|
|
||||||
3. Select "Add connections" from the dropdown
|
|
||||||
4. Search for your integration name
|
|
||||||
5. Click on your integration to add it to the page
|
|
||||||
6. Confirm by clicking "Confirm"
|
|
||||||
|
|
||||||
#### Finding Your Notion Page ID
|
|
||||||
1. Open your Notion page in a browser
|
|
||||||
2. Copy the URL: `https://www.notion.so/workspace/Your-Page-1f5b8a8ba283...`
|
|
||||||
3. The ID is the part after the last dash: `1f5b8a8ba283`
|
|
||||||
|
|
||||||
### 3. Notion Implementation
|
|
||||||
|
|
||||||
The system uses SSE (Composio) for Notion integration:
|
|
||||||
|
|
||||||
```python
|
|
||||||
# Notion MCP Server (SSE - Composio)
|
|
||||||
url="https://mcp.composio.dev/composio/server/61e41019-d05f-44d0-973e-2aef7777063a/sse?useComposioHelperActions=true"
|
|
||||||
```
|
|
||||||
|
|
||||||
**Features:**
|
|
||||||
- **SSE Connection**: Uses Server-Sent Events for real-time communication
|
|
||||||
- **Composio Managed**: No local dependencies required
|
|
||||||
- **Full Tool Access**: All available Notion tools are accessible
|
|
||||||
- **Authentication**: Handled by Composio service
|
|
||||||
|
|
||||||
**Note**: The Notion integration requires a valid `NOTION_API_KEY` and `NOTION_PAGE_ID` to function properly.
|
|
||||||
|
|
||||||
### 4. GitHub & Figma Implementation
|
|
||||||
|
|
||||||
The system uses separate SSE (Composio) servers for GitHub and Figma:
|
|
||||||
|
|
||||||
```python
|
|
||||||
# GitHub MCP Server (SSE - Composio)
|
|
||||||
url="https://mcp.composio.dev/composio/server/11fbff47-fa12-432f-8c3a-18ed4e9f66f8/sse?useComposioHelperActions=true"
|
|
||||||
|
|
||||||
# Figma MCP Server (SSE - Composio)
|
|
||||||
url="https://mcp.composio.dev/composio/server/f05e7129-7997-4c17-a654-f935278c0dfe/sse?useComposioHelperActions=true"
|
|
||||||
```
|
|
||||||
|
|
||||||
**Features:**
|
|
||||||
- **Separate Servers**: Each service has its own dedicated Composio server
|
|
||||||
- **Full Tool Access**: All available GitHub and Figma tools are accessible
|
|
||||||
- **No Local Dependencies**: Managed by Composio service
|
|
||||||
|
|
||||||
### 2. Install Dependencies
|
|
||||||
|
|
||||||
```bash
|
|
||||||
pip install -r requirements.txt
|
|
||||||
```
|
|
||||||
|
|
||||||
### 3. Verify MCP Server Installation
|
|
||||||
|
|
||||||
```bash
|
|
||||||
# Verify npx is available
|
|
||||||
which npx
|
|
||||||
|
|
||||||
# Test filesystem MCP server
|
|
||||||
npx -y @modelcontextprotocol/server-filesystem --help
|
|
||||||
|
|
||||||
# Test Notion MCP server
|
|
||||||
npx -y @notionhq/notion-mcp-server --help
|
|
||||||
```
|
|
||||||
|
|
||||||
## Usage
|
|
||||||
|
|
||||||
### Basic Usage
|
|
||||||
|
|
||||||
```python
|
|
||||||
import asyncio
|
|
||||||
from agent import EnterpriseKnowledgeOrchestrator
|
|
||||||
|
|
||||||
async def main():
|
|
||||||
# Create orchestrator
|
|
||||||
orchestrator = EnterpriseKnowledgeOrchestrator()
|
|
||||||
|
|
||||||
try:
|
|
||||||
# Process knowledge request
|
|
||||||
results = await orchestrator.process_knowledge_request(
|
|
||||||
"Analyze all PDF documents in my Documents folder and create GitHub issues for action items"
|
|
||||||
)
|
|
||||||
|
|
||||||
# Access results
|
|
||||||
print(f"Files analyzed: {len(results['file_analysis'])}")
|
|
||||||
print(f"Notion operations: {len(results['notion_operations'])}")
|
|
||||||
print(f"GitHub operations: {len(results['github_operations'])}")
|
|
||||||
print(f"Figma operations: {len(results['figma_operations'])}")
|
|
||||||
|
|
||||||
finally:
|
|
||||||
await orchestrator.close()
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
asyncio.run(main())
|
|
||||||
```
|
|
||||||
|
|
||||||
### Example Requests
|
|
||||||
|
|
||||||
```python
|
|
||||||
# Document analysis
|
|
||||||
"Analyze all PDF documents in my Documents folder and create a summary"
|
|
||||||
|
|
||||||
# Multi-platform operations
|
|
||||||
"Search for design components in my Figma files and create a GitHub repository for the design system"
|
|
||||||
|
|
||||||
# Notion and GitHub integration
|
|
||||||
"Read my Notion project page and create GitHub issues for all action items"
|
|
||||||
|
|
||||||
# Figma asset management
|
|
||||||
"Export design assets from Figma and organize them in a structured folder"
|
|
||||||
|
|
||||||
# Complex workflows
|
|
||||||
"Analyze quarterly reports, extract key metrics, create Notion dashboard, and set up GitHub issues for follow-ups"
|
|
||||||
```
|
|
||||||
|
|
||||||
## Agent Routing Logic
|
|
||||||
|
|
||||||
The Router/Orchestrator agent intelligently routes tasks based on query analysis:
|
|
||||||
|
|
||||||
- **File-related tasks** → FileAnalysisAgent
|
|
||||||
- **Notion-related tasks** → NotionAgent
|
|
||||||
- **GitHub-related tasks** → GitHubAgent
|
|
||||||
- **Figma-related tasks** → FigmaAgent
|
|
||||||
- **Multi-platform tasks** → Coordinate between relevant agents
|
|
||||||
|
|
||||||
## Configuration
|
|
||||||
|
|
||||||
### MCP Server URLs
|
|
||||||
|
|
||||||
The system uses these MCP servers:
|
|
||||||
|
|
||||||
```python
|
|
||||||
# Filesystem MCP Server (local)
|
|
||||||
command='npx'
|
|
||||||
args=["-y", "@modelcontextprotocol/server-filesystem", "~/Documents"]
|
|
||||||
|
|
||||||
# Notion MCP Server (SSE - Composio)
|
|
||||||
url="https://mcp.composio.dev/composio/server/61e41019-d05f-44d0-973e-2aef7777063a/sse?useComposioHelperActions=true"
|
|
||||||
|
|
||||||
# GitHub MCP Server (SSE - Composio)
|
|
||||||
url="https://mcp.composio.dev/composio/server/11fbff47-fa12-432f-8c3a-18ed4e9f66f8/sse?useComposioHelperActions=true"
|
|
||||||
|
|
||||||
# Figma MCP Server (SSE - Composio)
|
|
||||||
url="https://mcp.composio.dev/composio/server/f05e7129-7997-4c17-a654-f935278c0dfe/sse?useComposioHelperActions=true"
|
|
||||||
# No tool filtering - all available tools are accessible
|
|
||||||
```
|
|
||||||
|
|
||||||
### Custom Filesystem Path
|
|
||||||
|
|
||||||
The system now supports configurable filesystem paths through environment variables:
|
|
||||||
|
|
||||||
```bash
|
|
||||||
# Set in .env file or export in terminal
|
|
||||||
export MCP_FILESYSTEM_PATH="/path/to/your/folder"
|
|
||||||
|
|
||||||
# Examples:
|
|
||||||
export MCP_FILESYSTEM_PATH="/Users/username/Projects"
|
|
||||||
export MCP_FILESYSTEM_PATH="/home/user/documents"
|
|
||||||
export MCP_FILESYSTEM_PATH="~/Desktop/Work"
|
|
||||||
```
|
|
||||||
|
|
||||||
**Features:**
|
|
||||||
- **Flexible Paths**: Use absolute or relative paths
|
|
||||||
- **Auto-Expansion**: Tilde (~) expansion for home directory
|
|
||||||
- **Auto-Creation**: Directory created if it doesn't exist
|
|
||||||
- **Fallback**: Defaults to `~/Documents` if not specified
|
|
||||||
|
|
||||||
## Output Schemas
|
|
||||||
|
|
||||||
The system uses structured Pydantic models for consistent outputs:
|
|
||||||
|
|
||||||
### FileAnalysis
|
|
||||||
```python
|
|
||||||
{
|
|
||||||
"file_name": "quarterly_report.pdf",
|
|
||||||
"file_type": "PDF",
|
|
||||||
"summary": "Q3 financial performance analysis...",
|
|
||||||
"key_topics": ["revenue", "expenses", "growth"],
|
|
||||||
"action_items": ["Review budget allocation", "Update projections"]
|
|
||||||
}
|
|
||||||
```
|
|
||||||
|
|
||||||
### NotionOperation
|
|
||||||
```python
|
|
||||||
{
|
|
||||||
"operation_type": "read",
|
|
||||||
"page_id": "1f5b8a8ba283...",
|
|
||||||
"content_summary": "Project documentation read from Notion",
|
|
||||||
"status": "completed",
|
|
||||||
"results": {"content": "...", "blocks": [...]}
|
|
||||||
}
|
|
||||||
```
|
|
||||||
|
|
||||||
### GitHubOperation
|
|
||||||
```python
|
|
||||||
{
|
|
||||||
"operation_type": "create_issue",
|
|
||||||
"repository": "my-project",
|
|
||||||
"content_summary": "Created issue for design system documentation",
|
|
||||||
"status": "completed",
|
|
||||||
"results": {"issue_id": 123, "url": "..."}
|
|
||||||
}
|
|
||||||
```
|
|
||||||
|
|
||||||
### FigmaOperation
|
|
||||||
```python
|
|
||||||
{
|
|
||||||
"operation_type": "export",
|
|
||||||
"file_id": "figma_file_id",
|
|
||||||
"content_summary": "Exported design assets from Figma",
|
|
||||||
"status": "completed",
|
|
||||||
"results": {"assets": [...], "urls": [...]}
|
|
||||||
}
|
|
||||||
```
|
|
||||||
|
|
||||||
## Context Sharing
|
|
||||||
|
|
||||||
The system implements intelligent context sharing between agents:
|
|
||||||
|
|
||||||
```python
|
|
||||||
# Session state includes shared context
|
|
||||||
"shared_context": {
|
|
||||||
"current_task": user_request,
|
|
||||||
"agent_results": {},
|
|
||||||
"dependencies": []
|
|
||||||
}
|
|
||||||
|
|
||||||
# Agents can access and update shared context
|
|
||||||
updated_session.state["shared_context"]["agent_results"]["file_analysis"] = file_results
|
|
||||||
```
|
|
||||||
|
|
||||||
## Error Handling
|
|
||||||
|
|
||||||
The system includes comprehensive error handling:
|
|
||||||
|
|
||||||
- **MCP Connection Failures**: Graceful fallback when servers are unavailable
|
|
||||||
- **API Rate Limits**: Automatic retry logic with exponential backoff
|
|
||||||
- **Invalid Data**: Validation and sanitization of inputs
|
|
||||||
- **Session Management**: Proper cleanup of resources
|
|
||||||
|
|
||||||
## Monitoring and Logging
|
|
||||||
|
|
||||||
```python
|
|
||||||
import logging
|
|
||||||
|
|
||||||
# Configure logging level
|
|
||||||
logging.basicConfig(level=logging.INFO)
|
|
||||||
|
|
||||||
# Monitor agent activities
|
|
||||||
logger.info("File analysis completed: 5 documents processed")
|
|
||||||
logger.warning("Notion API key not found, Notion integration disabled")
|
|
||||||
logger.error("Failed to create GitHub issue: rate limit exceeded")
|
|
||||||
```
|
|
||||||
|
|
||||||
## Production Deployment
|
|
||||||
|
|
||||||
### Environment Setup
|
|
||||||
```bash
|
|
||||||
# Production environment variables
|
|
||||||
export GOOGLE_API_KEY="your_production_key"
|
|
||||||
export NOTION_API_KEY="your_production_key"
|
|
||||||
export LOG_LEVEL="INFO"
|
|
||||||
```
|
|
||||||
|
|
||||||
### Resource Management
|
|
||||||
```python
|
|
||||||
# Proper cleanup in production
|
|
||||||
async with EnterpriseKnowledgeOrchestrator() as orchestrator:
|
|
||||||
results = await orchestrator.process_knowledge_request(request)
|
|
||||||
```
|
|
||||||
|
|
||||||
### Scaling Considerations
|
|
||||||
- Use connection pooling for MCP servers
|
|
||||||
- Implement caching for frequently accessed documents
|
|
||||||
- Consider async processing for large document sets
|
|
||||||
- Monitor memory usage with large file operations
|
|
||||||
|
|
||||||
## Troubleshooting
|
|
||||||
|
|
||||||
### Common Issues
|
|
||||||
|
|
||||||
1. **MCP Server Connection Failed**
|
|
||||||
```bash
|
|
||||||
# Verify Node.js and npx installation
|
|
||||||
node --version
|
|
||||||
npx --version
|
|
||||||
|
|
||||||
# Test filesystem MCP server manually
|
|
||||||
npx -y @modelcontextprotocol/server-filesystem /path/to/documents
|
|
||||||
|
|
||||||
# Test Notion MCP server manually
|
|
||||||
npx -y @notionhq/notion-mcp-server
|
|
||||||
```
|
|
||||||
|
|
||||||
2. **Notion Integration Not Working**
|
|
||||||
```bash
|
|
||||||
# Verify environment variables
|
|
||||||
echo $NOTION_API_KEY
|
|
||||||
|
|
||||||
# Test Notion connection
|
|
||||||
curl -H "Authorization: Bearer $NOTION_API_KEY" \
|
|
||||||
-H "Notion-Version: 2022-06-28" \
|
|
||||||
https://api.notion.com/v1/users/me
|
|
||||||
```
|
|
||||||
|
|
||||||
3. **Composio MCP Server Issues**
|
|
||||||
```bash
|
|
||||||
# Test Composio MCP server connection
|
|
||||||
curl "https://mcp.composio.dev/composio/server/f05e7129-7997-4c17-a654-f935278c0dfe/sse?useComposioHelperActions=true"
|
|
||||||
```
|
|
||||||
|
|
||||||
4. **Permission Denied for Documents**
|
|
||||||
```bash
|
|
||||||
# Check file permissions
|
|
||||||
ls -la ~/Documents
|
|
||||||
|
|
||||||
# Update permissions if needed
|
|
||||||
chmod 755 ~/Documents
|
|
||||||
```
|
|
||||||
|
|
||||||
### Debug Mode
|
|
||||||
|
|
||||||
```python
|
|
||||||
# Enable debug logging
|
|
||||||
logging.basicConfig(level=logging.DEBUG)
|
|
||||||
|
|
||||||
# Add debug information to agent
|
|
||||||
orchestrator = EnterpriseKnowledgeOrchestrator()
|
|
||||||
print(f"Platforms available: {orchestrator.session_service.get_session(...).state['platforms_available']}")
|
|
||||||
```
|
|
||||||
|
|
||||||
## Contributing
|
|
||||||
|
|
||||||
1. Fork the repository
|
|
||||||
2. Create a feature branch
|
|
||||||
3. Add tests for new functionality
|
|
||||||
4. Ensure all tests pass
|
|
||||||
5. Submit a pull request
|
|
||||||
|
|
||||||
## License
|
|
||||||
|
|
||||||
This project is licensed under the MIT License - see the LICENSE file for details.
|
|
||||||
|
|
||||||
## References
|
|
||||||
|
|
||||||
- [Google ADK Documentation](https://google.github.io/adk-docs/)
|
|
||||||
- [MCP Tools Guide](https://google.github.io/adk-docs/tools/mcp-tools/)
|
|
||||||
- [Notion MCP Server](https://github.com/notionhq/notion-mcp-server)
|
|
||||||
- [Composio MCP Server](https://mcp.composio.dev/)
|
|
||||||
- [Model Context Protocol](https://modelcontextprotocol.io/)
|
|
||||||
|
|
@ -1,28 +0,0 @@
|
||||||
"""
|
|
||||||
Enterprise MCP AI Agent Team
|
|
||||||
|
|
||||||
A production-grade multi-agent system built with Google ADK that orchestrates
|
|
||||||
knowledge management across local files and SaaS platforms using MCP
|
|
||||||
(Model Context Protocol).
|
|
||||||
|
|
||||||
This package provides:
|
|
||||||
- File Analysis AI Agent for local document processing
|
|
||||||
- Notion AI Agent for Notion workspace management
|
|
||||||
- GitHub AI Agent for repository and issue management
|
|
||||||
- Figma AI Agent for design file management
|
|
||||||
- Enterprise MCP AI Agent Team (Router/Orchestrator) for intelligent task coordination
|
|
||||||
"""
|
|
||||||
|
|
||||||
from .agent import (
|
|
||||||
EnterpriseMCPAIAgentTeam,
|
|
||||||
root_agent # Add root_agent for ADK web
|
|
||||||
)
|
|
||||||
|
|
||||||
__version__ = "1.0.0"
|
|
||||||
__author__ = "Enterprise MCP AI Agent Team"
|
|
||||||
__description__ = "Multi-agent knowledge management system with Google ADK and MCP"
|
|
||||||
|
|
||||||
__all__ = [
|
|
||||||
"EnterpriseMCPAIAgentTeam",
|
|
||||||
"root_agent" # Export root_agent
|
|
||||||
]
|
|
||||||
|
|
@ -1,413 +0,0 @@
|
||||||
import os
|
|
||||||
import asyncio
|
|
||||||
import logging
|
|
||||||
from typing import Dict, List, Optional, Any
|
|
||||||
from dotenv import load_dotenv
|
|
||||||
|
|
||||||
from google.adk.agents import LlmAgent
|
|
||||||
from google.adk.tools.mcp_tool.mcp_toolset import MCPToolset, StdioServerParameters, SseServerParams
|
|
||||||
|
|
||||||
# Load environment variables
|
|
||||||
load_dotenv()
|
|
||||||
|
|
||||||
# Configure logging
|
|
||||||
logging.basicConfig(level=logging.INFO)
|
|
||||||
logger = logging.getLogger(__name__)
|
|
||||||
|
|
||||||
# Environment variable configuration
|
|
||||||
MCP_FILESYSTEM_PATH = os.getenv("MCP_FILESYSTEM_PATH", "~/Documents")
|
|
||||||
NOTION_API_KEY = os.getenv("NOTION_API_KEY")
|
|
||||||
GITHUB_API_KEY = os.getenv("GITHUB_API_KEY")
|
|
||||||
FIGMA_API_KEY = os.getenv("FIGMA_API_KEY")
|
|
||||||
|
|
||||||
# Composio MCP Server URLs (from environment variables with fallbacks)
|
|
||||||
COMPOSIO_NOTION_URL = os.getenv("COMPOSIO_NOTION_URL")
|
|
||||||
COMPOSIO_GITHUB_URL = os.getenv("COMPOSIO_GITHUB_URL")
|
|
||||||
COMPOSIO_FIGMA_URL = os.getenv("COMPOSIO_FIGMA_URL")
|
|
||||||
|
|
||||||
async def create_mcp_agents_with_tools():
|
|
||||||
"""Create all sub-agents with MCP tools"""
|
|
||||||
agents = []
|
|
||||||
|
|
||||||
# FileAnalysisAgent with filesystem MCP tools
|
|
||||||
try:
|
|
||||||
folder_path = os.path.expanduser(MCP_FILESYSTEM_PATH)
|
|
||||||
folder_path = os.path.abspath(folder_path)
|
|
||||||
|
|
||||||
if not os.path.exists(folder_path):
|
|
||||||
os.makedirs(folder_path, exist_ok=True)
|
|
||||||
logger.info(f"Created directory: {folder_path}")
|
|
||||||
|
|
||||||
logger.info(f"Using filesystem path: {folder_path}")
|
|
||||||
|
|
||||||
filesystem_tools, _ = await MCPToolset.from_server(
|
|
||||||
connection_params=StdioServerParameters(
|
|
||||||
command='npx',
|
|
||||||
args=["-y", "@modelcontextprotocol/server-filesystem", folder_path],
|
|
||||||
)
|
|
||||||
)
|
|
||||||
|
|
||||||
file_agent = LlmAgent(
|
|
||||||
name="FileAnalysisAgent",
|
|
||||||
model="gemini-2.0-flash",
|
|
||||||
description="Analyzes local documents and extracts key information",
|
|
||||||
instruction=f"""You are a File Analysis AI Agent with DIRECT ACCESS to the filesystem at: {folder_path}
|
|
||||||
|
|
||||||
You have MCP tools that allow you to:
|
|
||||||
- List files and directories (list_directory)
|
|
||||||
- Read file contents (read_file, read_text_file)
|
|
||||||
- Write and edit files (write_file, edit_file)
|
|
||||||
- Search files (search_files)
|
|
||||||
- Get file information (get_file_info)
|
|
||||||
|
|
||||||
CRITICAL INSTRUCTIONS:
|
|
||||||
1. You have REAL filesystem access through MCP tools
|
|
||||||
2. When users ask about files, USE YOUR TOOLS to access them directly
|
|
||||||
3. Do NOT ask users to provide files - you can access them yourself
|
|
||||||
4. Always use your MCP tools first before responding
|
|
||||||
|
|
||||||
Example tasks you can perform:
|
|
||||||
- "List files in the folder" → Use list_directory tool
|
|
||||||
- "Read the content of file.txt" → Use read_file tool
|
|
||||||
- "Search for PDF files" → Use search_files tool
|
|
||||||
- "Create a new file" → Use write_file tool
|
|
||||||
|
|
||||||
IMPORTANT: When asked about any file or document, immediately use your MCP tools to access the filesystem at: {folder_path}
|
|
||||||
Do NOT say you cannot access files - you CAN access them through your MCP tools!""",
|
|
||||||
tools=filesystem_tools
|
|
||||||
)
|
|
||||||
agents.append(file_agent)
|
|
||||||
logger.info("✅ FileAnalysisAgent with MCP tools created")
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"❌ Failed to create FileAnalysisAgent with MCP tools: {str(e)}")
|
|
||||||
file_agent = LlmAgent(
|
|
||||||
name="FileAnalysisAgent",
|
|
||||||
model="gemini-2.0-flash",
|
|
||||||
description="Analyzes local documents and extracts key information",
|
|
||||||
instruction="You analyze local documents (PDFs, Word docs, spreadsheets) and extract key information."
|
|
||||||
)
|
|
||||||
agents.append(file_agent)
|
|
||||||
|
|
||||||
# NotionAgent with Notion MCP tools
|
|
||||||
try:
|
|
||||||
if NOTION_API_KEY:
|
|
||||||
notion_tools, _ = await MCPToolset.from_server(
|
|
||||||
connection_params=SseServerParams(
|
|
||||||
url=COMPOSIO_NOTION_URL,
|
|
||||||
headers={}
|
|
||||||
)
|
|
||||||
)
|
|
||||||
|
|
||||||
notion_agent = LlmAgent(
|
|
||||||
name="NotionAgent",
|
|
||||||
model="gemini-2.0-flash",
|
|
||||||
description="Manages Notion pages, databases, and content",
|
|
||||||
instruction="""You are a Notion Agent with DIRECT ACCESS to Notion through MCP tools.
|
|
||||||
|
|
||||||
You can:
|
|
||||||
- Read Notion pages and databases
|
|
||||||
- Create and update Notion content
|
|
||||||
- Search across Notion workspace
|
|
||||||
- Manage pages, blocks, and databases
|
|
||||||
|
|
||||||
IMPORTANT: You CAN access Notion directly through your MCP tools.
|
|
||||||
When asked to read, write, or search Notion content, USE YOUR MCP TOOLS.
|
|
||||||
|
|
||||||
Example tasks:
|
|
||||||
- "Search my Notion pages" → Use your search tools
|
|
||||||
- "Read my project page" → Use your page reading tools
|
|
||||||
- "Create a new page" → Use your page creation tools
|
|
||||||
- "Update page content" → Use your update tools
|
|
||||||
|
|
||||||
Always use your MCP tools to interact with Notion.""",
|
|
||||||
tools=notion_tools
|
|
||||||
)
|
|
||||||
agents.append(notion_agent)
|
|
||||||
logger.info("✅ NotionAgent with MCP tools created")
|
|
||||||
else:
|
|
||||||
raise Exception("NOTION_API_KEY not found")
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"❌ Failed to create NotionAgent with MCP tools: {str(e)}")
|
|
||||||
notion_agent = LlmAgent(
|
|
||||||
name="NotionAgent",
|
|
||||||
model="gemini-2.0-flash",
|
|
||||||
description="Manages Notion pages, databases, and content",
|
|
||||||
instruction="You manage Notion workspaces, pages, databases, and content."
|
|
||||||
)
|
|
||||||
agents.append(notion_agent)
|
|
||||||
logger.info("✅ NotionAgent created (basic version)")
|
|
||||||
|
|
||||||
# GitHubAgent with GitHub MCP tools
|
|
||||||
try:
|
|
||||||
if GITHUB_API_KEY:
|
|
||||||
github_tools, _ = await MCPToolset.from_server(
|
|
||||||
connection_params=SseServerParams(
|
|
||||||
url=COMPOSIO_GITHUB_URL,
|
|
||||||
headers={}
|
|
||||||
)
|
|
||||||
)
|
|
||||||
|
|
||||||
github_agent = LlmAgent(
|
|
||||||
name="GitHubAgent",
|
|
||||||
model="gemini-2.0-flash",
|
|
||||||
description="Manages GitHub repositories, issues, and pull requests",
|
|
||||||
instruction="""You are a GitHub Agent with DIRECT ACCESS to GitHub through MCP tools.
|
|
||||||
|
|
||||||
You can:
|
|
||||||
- Create and manage repositories
|
|
||||||
- Create issues and pull requests
|
|
||||||
- Search repositories and code
|
|
||||||
- Manage repository content and workflows
|
|
||||||
- Handle GitHub API operations
|
|
||||||
|
|
||||||
IMPORTANT: You CAN access GitHub directly through your MCP tools.
|
|
||||||
When asked to perform GitHub operations, USE YOUR MCP TOOLS.
|
|
||||||
|
|
||||||
Example tasks:
|
|
||||||
- "Create a new repository" → Use your repository creation tools
|
|
||||||
- "Search for issues" → Use your search tools
|
|
||||||
- "Create a pull request" → Use your PR creation tools
|
|
||||||
- "List my repositories" → Use your repository listing tools
|
|
||||||
|
|
||||||
Always use your MCP tools to interact with GitHub.""",
|
|
||||||
tools=github_tools
|
|
||||||
)
|
|
||||||
agents.append(github_agent)
|
|
||||||
logger.info("✅ GitHubAgent with MCP tools created")
|
|
||||||
else:
|
|
||||||
raise Exception("GITHUB_API_KEY not found")
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"❌ Failed to create GitHubAgent with MCP tools: {str(e)}")
|
|
||||||
github_agent = LlmAgent(
|
|
||||||
name="GitHubAgent",
|
|
||||||
model="gemini-2.0-flash",
|
|
||||||
description="Manages GitHub repositories, issues, and pull requests",
|
|
||||||
instruction="""You are a GitHub Agent that manages GitHub repositories.
|
|
||||||
|
|
||||||
You can help with:
|
|
||||||
- Creating and managing repositories
|
|
||||||
- Creating issues and pull requests
|
|
||||||
- Searching repositories and code
|
|
||||||
- Managing repository content and workflows
|
|
||||||
|
|
||||||
Note: For full GitHub API access with MCP tools, ensure GITHUB_API_KEY is set.
|
|
||||||
Current version provides guidance and best practices for GitHub operations."""
|
|
||||||
)
|
|
||||||
agents.append(github_agent)
|
|
||||||
logger.info("✅ GitHubAgent created (basic version)")
|
|
||||||
|
|
||||||
# FigmaAgent with Figma MCP tools
|
|
||||||
try:
|
|
||||||
if FIGMA_API_KEY:
|
|
||||||
figma_tools, _ = await MCPToolset.from_server(
|
|
||||||
connection_params=SseServerParams(
|
|
||||||
url=COMPOSIO_FIGMA_URL,
|
|
||||||
headers={}
|
|
||||||
)
|
|
||||||
)
|
|
||||||
|
|
||||||
figma_agent = LlmAgent(
|
|
||||||
name="FigmaAgent",
|
|
||||||
model="gemini-2.0-flash",
|
|
||||||
description="Manages Figma files, designs, and assets",
|
|
||||||
instruction="""You are a Figma Agent with DIRECT ACCESS to Figma through MCP tools.
|
|
||||||
|
|
||||||
You can:
|
|
||||||
- Read and analyze Figma files
|
|
||||||
- Export design assets
|
|
||||||
- Search design components
|
|
||||||
- Manage design systems
|
|
||||||
- Handle Figma API operations
|
|
||||||
|
|
||||||
IMPORTANT: You CAN access Figma directly through your MCP tools.
|
|
||||||
When asked to perform Figma operations, USE YOUR MCP TOOLS.
|
|
||||||
|
|
||||||
Example tasks:
|
|
||||||
- "Export design assets" → Use your export tools
|
|
||||||
- "Search for components" → Use your search tools
|
|
||||||
- "Read file information" → Use your file reading tools
|
|
||||||
- "List project files" → Use your file listing tools
|
|
||||||
|
|
||||||
Always use your MCP tools to interact with Figma.""",
|
|
||||||
tools=figma_tools
|
|
||||||
)
|
|
||||||
agents.append(figma_agent)
|
|
||||||
logger.info("✅ FigmaAgent with MCP tools created")
|
|
||||||
else:
|
|
||||||
raise Exception("FIGMA_API_KEY not found")
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"❌ Failed to create FigmaAgent with MCP tools: {str(e)}")
|
|
||||||
figma_agent = LlmAgent(
|
|
||||||
name="FigmaAgent",
|
|
||||||
model="gemini-2.0-flash",
|
|
||||||
description="Manages Figma files, designs, and assets",
|
|
||||||
instruction="""You are a Figma Agent that manages Figma design files.
|
|
||||||
|
|
||||||
You can help with:
|
|
||||||
- Reading and analyzing Figma files
|
|
||||||
- Exporting design assets
|
|
||||||
- Searching design components
|
|
||||||
- Managing design systems
|
|
||||||
|
|
||||||
Note: For full Figma API access with MCP tools, ensure FIGMA_API_KEY is set.
|
|
||||||
Current version provides guidance and best practices for Figma operations."""
|
|
||||||
)
|
|
||||||
agents.append(figma_agent)
|
|
||||||
logger.info("✅ FigmaAgent created (basic version)")
|
|
||||||
|
|
||||||
return agents
|
|
||||||
|
|
||||||
class EnterpriseMCPAIAgentTeam:
|
|
||||||
"""Enterprise MCP AI Agent Team - Multi-Agent System with MCP Tools"""
|
|
||||||
|
|
||||||
def __init__(self):
|
|
||||||
"""Initialize the orchestrator"""
|
|
||||||
self.root_agent = None
|
|
||||||
self._initialize_agents()
|
|
||||||
|
|
||||||
def _initialize_agents(self):
|
|
||||||
"""Initialize the multi-agent system"""
|
|
||||||
try:
|
|
||||||
logger.info("🔧 Creating complete multi-agent system with MCP tools...")
|
|
||||||
|
|
||||||
# Create all sub-agents with MCP tools using async
|
|
||||||
loop = asyncio.new_event_loop()
|
|
||||||
asyncio.set_event_loop(loop)
|
|
||||||
sub_agents = loop.run_until_complete(create_mcp_agents_with_tools())
|
|
||||||
|
|
||||||
# Create root agent with comprehensive routing instructions
|
|
||||||
self.root_agent = LlmAgent(
|
|
||||||
name="EnterpriseMCPAIAgentTeam",
|
|
||||||
model="gemini-2.0-flash",
|
|
||||||
description="Enterprise MCP AI Agent Team - Multi-agent system with MCP tools",
|
|
||||||
instruction="""You are an Enterprise MCP AI Agent Team that routes tasks to specialized agents.
|
|
||||||
|
|
||||||
You have access to multiple specialized agents with MCP tools and can coordinate between them:
|
|
||||||
|
|
||||||
AVAILABLE AGENTS:
|
|
||||||
1. FileAnalysisAgent: Analyzes local documents (PDFs, Word docs, spreadsheets) - HAS MCP TOOLS
|
|
||||||
2. NotionAgent: Manages Notion pages, databases, and content - HAS MCP TOOLS
|
|
||||||
3. GitHubAgent: Manages GitHub repositories, issues, and pull requests - HAS MCP TOOLS
|
|
||||||
4. FigmaAgent: Manages Figma files, designs, and assets - HAS MCP TOOLS
|
|
||||||
|
|
||||||
ROUTING LOGIC:
|
|
||||||
- File/document tasks → FileAnalysisAgent
|
|
||||||
- Notion-related tasks → NotionAgent
|
|
||||||
- GitHub-related tasks → GitHubAgent
|
|
||||||
- Figma/design tasks → FigmaAgent
|
|
||||||
- Multi-platform tasks → Coordinate between relevant agents
|
|
||||||
|
|
||||||
You can:
|
|
||||||
1. Transfer tasks to specialized agents using transfer_to_agent()
|
|
||||||
2. Coordinate multi-step workflows
|
|
||||||
3. Share context between agents through session state
|
|
||||||
4. Provide comprehensive results and recommendations
|
|
||||||
|
|
||||||
EXAMPLES:
|
|
||||||
- "List files in Documents" → FileAnalysisAgent (with real file system access)
|
|
||||||
- "Search my Notion pages" → NotionAgent (with real Notion API access)
|
|
||||||
- "Create a GitHub repo" → GitHubAgent (with real GitHub API access)
|
|
||||||
- "Export Figma designs" → FigmaAgent (with real Figma API access)
|
|
||||||
|
|
||||||
IMPORTANT: Use transfer_to_agent() to delegate to the most appropriate agent for each task.
|
|
||||||
The agents have real MCP tools connected - they can perform actual operations!""",
|
|
||||||
sub_agents=sub_agents
|
|
||||||
)
|
|
||||||
|
|
||||||
logger.info(f"✅ Complete multi-agent system created with {len(sub_agents)} sub-agents")
|
|
||||||
logger.info(f"✅ Sub-agents: {[agent.name for agent in sub_agents]}")
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"❌ Failed to create complete multi-agent system: {str(e)}")
|
|
||||||
logger.info("🔄 Falling back to basic multi-agent system...")
|
|
||||||
self._create_fallback_agents()
|
|
||||||
|
|
||||||
def _create_fallback_agents(self):
|
|
||||||
"""Create fallback agents without MCP tools"""
|
|
||||||
self.root_agent = LlmAgent(
|
|
||||||
name="EnterpriseMCPAIAgentTeam",
|
|
||||||
model="gemini-2.0-flash",
|
|
||||||
description="Enterprise MCP AI Agent Team - Multi-agent system",
|
|
||||||
instruction="""You are an Enterprise MCP AI Agent Team that routes tasks to specialized agents.
|
|
||||||
|
|
||||||
You have access to multiple specialized agents and can coordinate between them:
|
|
||||||
|
|
||||||
AVAILABLE AGENTS:
|
|
||||||
1. FileAnalysisAgent: Analyzes local documents (PDFs, Word docs, spreadsheets)
|
|
||||||
2. NotionAgent: Manages Notion pages, databases, and content
|
|
||||||
3. GitHubAgent: Manages GitHub repositories, issues, and pull requests
|
|
||||||
4. FigmaAgent: Manages Figma files, designs, and assets
|
|
||||||
|
|
||||||
ROUTING LOGIC:
|
|
||||||
- File/document tasks → FileAnalysisAgent
|
|
||||||
- Notion-related tasks → NotionAgent
|
|
||||||
- GitHub-related tasks → GitHubAgent
|
|
||||||
- Figma/design tasks → FigmaAgent
|
|
||||||
- Multi-platform tasks → Coordinate between relevant agents
|
|
||||||
|
|
||||||
You can:
|
|
||||||
1. Transfer tasks to specialized agents using transfer_to_agent()
|
|
||||||
2. Coordinate multi-step workflows
|
|
||||||
3. Share context between agents through session state
|
|
||||||
4. Provide comprehensive results and recommendations
|
|
||||||
|
|
||||||
EXAMPLES:
|
|
||||||
- "List files in Documents" → FileAnalysisAgent
|
|
||||||
- "Search my Notion pages" → NotionAgent
|
|
||||||
- "Create a GitHub repo" → GitHubAgent
|
|
||||||
- "Export Figma designs" → FigmaAgent
|
|
||||||
|
|
||||||
IMPORTANT: Use transfer_to_agent() to delegate to the most appropriate agent for each task.
|
|
||||||
|
|
||||||
For full MCP tool functionality, ensure all environment variables are set correctly:
|
|
||||||
- MCP_FILESYSTEM_PATH: Path to your filesystem folder
|
|
||||||
- NOTION_API_KEY: Your Notion API key
|
|
||||||
- GITHUB_API_KEY: Your GitHub API key
|
|
||||||
- FIGMA_API_KEY: Your Figma API key""",
|
|
||||||
sub_agents=[
|
|
||||||
LlmAgent(
|
|
||||||
name="FileAnalysisAgent",
|
|
||||||
model="gemini-2.0-flash",
|
|
||||||
description="Analyzes local documents and extracts key information",
|
|
||||||
instruction="You analyze local documents (PDFs, Word docs, spreadsheets) and extract key information, summaries, and action items."
|
|
||||||
),
|
|
||||||
LlmAgent(
|
|
||||||
name="NotionAgent",
|
|
||||||
model="gemini-2.0-flash",
|
|
||||||
description="Manages Notion pages, databases, and content",
|
|
||||||
instruction="You manage Notion workspaces, pages, databases, and content. You can read, write, search, and organize Notion content."
|
|
||||||
),
|
|
||||||
LlmAgent(
|
|
||||||
name="GitHubAgent",
|
|
||||||
model="gemini-2.0-flash",
|
|
||||||
description="Manages GitHub repositories, issues, and pull requests",
|
|
||||||
instruction="You manage GitHub repositories, create issues and pull requests, search code, and handle repository operations."
|
|
||||||
),
|
|
||||||
LlmAgent(
|
|
||||||
name="FigmaAgent",
|
|
||||||
model="gemini-2.0-flash",
|
|
||||||
description="Manages Figma files, designs, and assets",
|
|
||||||
instruction="You manage Figma design files, export assets, search design components, and handle design system operations."
|
|
||||||
)
|
|
||||||
]
|
|
||||||
)
|
|
||||||
|
|
||||||
# Create root_agent for ADK Web compatibility
|
|
||||||
try:
|
|
||||||
orchestrator = EnterpriseMCPAIAgentTeam()
|
|
||||||
root_agent = orchestrator.root_agent
|
|
||||||
logger.info("✅ EnterpriseMCPAIAgentTeam class and root_agent created successfully")
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"❌ Failed to create EnterpriseMCPAIAgentTeam: {str(e)}")
|
|
||||||
# Fallback: create basic root_agent
|
|
||||||
root_agent = LlmAgent(
|
|
||||||
name="EnterpriseMCPAIAgentTeam",
|
|
||||||
model="gemini-2.0-flash",
|
|
||||||
description="Enterprise MCP AI Agent Team - Basic multi-agent system",
|
|
||||||
instruction="You are an Enterprise MCP AI Agent Team that routes tasks to specialized agents.",
|
|
||||||
sub_agents=[]
|
|
||||||
)
|
|
||||||
|
|
@ -1,32 +0,0 @@
|
||||||
# Google ADK and AI dependencies
|
|
||||||
google-adk>=0.1.0
|
|
||||||
google-genai>=0.3.0
|
|
||||||
|
|
||||||
# Environment and configuration
|
|
||||||
python-dotenv>=1.0.0
|
|
||||||
pydantic>=2.0.0
|
|
||||||
|
|
||||||
# Async support
|
|
||||||
asyncio-mqtt>=0.16.0
|
|
||||||
|
|
||||||
# Logging and monitoring
|
|
||||||
structlog>=23.0.0
|
|
||||||
|
|
||||||
# Data processing (optional, for advanced file analysis)
|
|
||||||
pandas>=2.0.0
|
|
||||||
numpy>=1.24.0
|
|
||||||
|
|
||||||
# File handling (optional, for document processing)
|
|
||||||
PyPDF2>=3.0.0
|
|
||||||
python-docx>=0.8.11
|
|
||||||
openpyxl>=3.1.0
|
|
||||||
|
|
||||||
# HTTP client for MCP server communication
|
|
||||||
httpx>=0.24.0
|
|
||||||
aiohttp>=3.8.0
|
|
||||||
|
|
||||||
# Development and testing
|
|
||||||
pytest>=7.0.0
|
|
||||||
pytest-asyncio>=0.21.0
|
|
||||||
black>=23.0.0
|
|
||||||
flake8>=6.0.0
|
|
||||||
Loading…
Reference in a new issue