Deployable Agent Blueprints

from langgraph.graph import StateGraph
from typing import TypedDict

class LeadState(TypedDict):
    company: str
    contact: str
    score: int

from langgraph.graph import StateGraph
from typing import TypedDict

class SupportState(TypedDict):
    ticket: str
    confidence: float
    response: str

from langgraph.graph import StateGraph
from typing import TypedDict

class ContentState(TypedDict):
    topic: str
    keyword: str
    brief: dict

from langgraph.graph import StateGraph
from typing import TypedDict

class FPAState(TypedDict):
    financials: dict
    scenarios: list
    narrative: str

from langgraph.graph import StateGraph
from typing import TypedDict

class OnboardState(TypedDict):
    hire_name: str
    role: str
    plan: dict

from crewai import Agent, Task, Crew

researcher = Agent(
    role='Competitive Intelligence Analyst',
    goal='Monitor competitor activity weekly',
    tools=['web_search', 'scraper'],
    llm='gpt-4o'
)

from langgraph.graph import StateGraph
from typing import TypedDict

class CartState(TypedDict):
    cart_value: float
    items: list
    emails: list

from langgraph.graph import StateGraph
from typing import TypedDict

class MeetingState(TypedDict):
    transcript: str
    summary: list
    action_items: list

from langgraph.graph import StateGraph
from typing import TypedDict

class SEOState(TypedDict):
    keyword: str
    pillar: dict
    articles: list

from langgraph.graph import StateGraph
from typing import TypedDict

class InvoiceState(TypedDict):
    invoice: dict
    po: dict
    status: str

from langgraph.graph import StateGraph
from typing import TypedDict

class TriageState(TypedDict):
    complaint: str
    esi_level: int
    routing: str

from crewai import Agent, Task, Crew

copywriter = Agent(
    role='Performance Marketing Copywriter',
    goal='Generate multi-platform ad copy variants',
    tools=['brief_analyzer'],
    llm='gpt-4o'
)

from langgraph.graph import StateGraph
from typing import TypedDict, Literal

class SupportState(TypedDict):
    ticket_body: str
    sentiment: str
    severity: str
    credit_amount: float

from langgraph.graph import StateGraph
from typing import TypedDict, Literal

class CodeReviewState(TypedDict):
    diff: str
    verdict: str
    security_blocker: bool
    issues: list

from langgraph.graph import StateGraph
from typing import TypedDict, Literal

class AuditState(TypedDict):
    invoice: dict
    po: dict
    status: str
    variance_pct: float

from crewai import Agent, Task, Crew, Process

order_analyst = Agent(
    role='E-commerce Order Analyst',
    goal='Verify order details and customer LTV',
    tools=['shopify_order_lookup', 'stripe_payment_history'],
    allow_delegation=False,
    llm='gpt-4o'

from crewai import Agent, Task, Crew, Process

stock_monitor = Agent(
    role='Inventory Stock Monitor',
    tools=['erp_stock_lookup', 'sales_velocity_calculator'],
    llm='gpt-4o', temperature=0.0
)
supplier_researcher = Agent(

from crewai import Agent, Task, Crew, Process

pricing_researcher = Agent(
    role='Competitive Pricing Researcher',
    tools=['brightdata_scraper', 'price_comparison_matrix'],
    llm='gpt-4o', temperature=0.1
)
margin_analyst = Agent(

from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver
from typing import TypedDict, Annotated
import operator

class RAGState(TypedDict):
    query: str
    docs: list

from azure.ai.agents import AgentsClient, Agent
from azure.ai.agents.models import MessageRole
from azure.identity import DefaultAzureCredential

credential = DefaultAzureCredential()
client = AgentsClient(
    endpoint="https://<your-project>.services.ai.azure.com",
    credential=credential,

import anthropic

client = anthropic.Anthropic()

# Orchestrator spawns subagents via tool use
tools = [
    {
        "name": "spawn_subagent",

from pydantic import BaseModel, Field, field_validator
from pydantic_ai import Agent
from typing import Literal

class ContractExtraction(BaseModel):
    vendor_name: str = Field(min_length=2)
    contract_value_usd: float = Field(gt=0)
    start_date: str

from crewai import Agent, Task, Crew, Process
from crewai_tools import SerperDevTool, ScrapeWebsiteTool

researcher = Agent(
    role='Market Intelligence Researcher',
    goal='Gather competitor data from web sources',
    tools=[SerperDevTool(), ScrapeWebsiteTool()],
    llm='gpt-4o', verbose=True

import { Mastra, createTool } from '@mastra/core';
import { Agent } from '@mastra/core/agent';
import { z } from 'zod';

const sendWelcomeEmail = createTool({
  id: 'send-welcome-email',
  description: 'Send a personalized welcome email to a new user',
  inputSchema: z.object({

from llama_index.core.workflow import Workflow, StartEvent, StopEvent, step, Event
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI

class DocumentLoadedEvent(Event):
    index: VectorStoreIndex

class LegalAnalysisWorkflow(Workflow):

{
  "name": "competitor-pricing-sentinel",
  "framework": "Gumloop",
  "nodes": [
    {
      "id": "trigger",
      "type": "schedule",
      "config": {"cron": "0 9 * * 1", "timezone": "UTC"}

import boto3
import json

bedrock_agent = boto3.client('bedrock-agent-runtime', region_name='us-east-1')

def invoke_patient_intake_agent(patient_complaint: str, session_id: str):
    response = bedrock_agent.invoke_agent(
        agentId='AGENT_ID',