CrewAIIntermediate⏱ ~30 minutes

Sales Outreach Agent

Turn cold prospects into warm leads with AI-personalized outreach at scale

★ 4.5(59 reviews)•Sales & Marketing

Preview Code ↓

$149$298

✓ Full source code & documentation
✓ Commercial license included
✓ 30-day email support
✓ Free updates for 1 year

What You Get

Everything included in this template package

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Working Agent Code

3 production-ready CrewAI agents with full orchestration

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Configuration File

YAML config for API keys, sending limits, and personalization rules

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Prompt Templates

10 battle-tested prompts for different outreach scenarios

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Setup Guide

Step-by-step walkthrough from install to first campaign

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Example I/O

Sample prospect lists and generated email outputs

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Architecture Diagram

Visual flow of the multi-agent pipeline

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The Problem

Manual cold outreach is painfully slow. Sales reps spend 3-4 hours per day researching prospects, writing personalized emails, and managing follow-ups — resulting in maybe 30-50 emails that still feel generic.

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The Solution

This agent crew automatically researches each prospect, crafts hyper-personalized emails referencing their company news, role, and pain points, then schedules intelligent follow-up sequences. Send 500+ personalized emails per day.

How It Works

Your AI crew handles the entire workflow

Input

Your task description, data, or trigger event

→

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AI Agents

Prospect ResearcherGathers company info, recent news, and role context from Apollo.io

Email ComposerCrafts personalized cold emails using research data and proven frameworks

Campaign ManagerSchedules sends, manages follow-ups, and tracks engagement via SendGrid

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Output

Structured results, reports, and actionable insights

Code Preview

Sample of the CrewAI agent setup

Preview only

crew.py

from crewai import Agent, Task, Crew
from tools import ApolloSearchTool, SendGridTool

researcher = Agent(
    role="Prospect Researcher",
    goal="Find detailed info about each prospect",
    backstory="You are an expert B2B researcher who finds"
              " compelling angles for sales outreach.",
    tools=[ApolloSearchTool()],
    verbose=True
)

composer = Agent(
    role="Email Composer",
    goal="Write hyper-personalized cold emails",
    backstory="You are a top-performing SDR who writes"
              " emails that get 40%+ open rates.",
    verbose=True
)

research_task = Task(
    description="Research {prospect_name} at {company}",
    agent=researcher,
    expected_output="Detailed prospect brief with angles"
)

crew = Crew(
    agents=[researcher, composer],
    tasks=[research_task, compose_task],
    verbose=True
)

Example Input & Output

See what goes in and what comes out

Input

Prospect: Sarah Chen, VP of Engineering at DataFlow Inc.
Company size: 200 employees
Industry: Data Infrastructure

Output

Subject: DataFlow's Series B — scaling eng without scaling headcount?

Hi Sarah,

Congrats on DataFlow's Series B — scaling from 50 to 200 engineers in 18 months is no small feat. I noticed your recent blog post about reducing pipeline latency by 40% — impressive work.

We help engineering leaders like you automate repetitive workflow orchestration so your team can focus on the hard problems. Companies similar to DataFlow have cut operational overhead by 60% within 3 months.

Worth a 15-minute chat next week?

Best,
[Your name]