A production-ready multi-agent AI system built with crewAI that autonomously conducts end-to-end market research, competitive intelligence, customer insights, product strategy, and business analysis — all in a single pipeline.
Five specialized AI agents work sequentially, each producing a professional-grade report:
| Agent | Role | Output |
|---|---|---|
| 🔍 Market Research Specialist | TAM/SAM/SOM sizing, trends, segmentation | Market Research Report |
| 🕵️ Competitive Intelligence Analyst | Competitor profiles, feature matrix, threat heatmap | Competitive Intelligence Briefing |
| 👥 Customer Insights Researcher | Personas, journey maps, unmet needs | Customer Insights Report |
| 🧭 Product Strategy Adviser | Vision, strategic bets, roadmap | Product Strategy Document |
| 📊 Senior Business Analyst | Unit economics, financial projections, ROI | Business & Financial Analysis |
When you run crewai run with the default inputs (AutoAgentX / Autonomous AI Agents), the crew produces five sequential reports covering:
1. Market Research Report
- TAM projected at ~$13.4B by 2025, CAGR of 34.6%
- 5 customer segments: automotive (40%), aerospace (20%), healthcare (15%), industrial automation (10%), consumer electronics (5%)
- Top white-space: autonomous agents for industrial automation (~$4.2B opportunity)
- Key risks: regulatory uncertainty, cybersecurity, technological disruption
2. Competitive Intelligence Briefing
- Competitor tiers: Leaders (NVIDIA, Intel), Challengers (Microsoft, Google, Amazon), Niche (Siemens, Rockwell, ABB)
- Feature comparison matrix across 7 capability dimensions
- Recent signals: acquisitions, partnerships, and product launches (2020–present)
- Strategic recommendation: differentiate via industrial automation focus
3. Customer Insights Report
- 5 detailed personas across automotive, aerospace, healthcare, industrial, and consumer segments
- End-to-end journey map: Awareness → Consideration → Evaluation → Purchase → Post-purchase
- Top pain points: regulatory uncertainty, high dev costs, integration complexity, cybersecurity
- Loyalty levers: reliability, customer support, continuous innovation
4. Product Strategy Document
- Vision: become the leading autonomous agent provider for industrial automation
- North star: 20% market share, $2.5B revenue by 2028
- 3 strategic bets: industrial automation, healthcare, consumer autonomous vehicles
- 6–12 month roadmap with milestones and resourcing asks
5. Business & Financial Analysis
- Unit economics benchmarked against industry (CAC, LTV, gross margins)
- 3 financial scenarios: base, upside, downside (3–5 year horizon)
- ROI analysis for each strategic initiative
- Prioritized recommendations to optimize margins and accelerate growth
- Python
>=3.10, <3.14 - uv package manager
pip install uvcrewai installOPENAI_API_BASE=https://api.groq.com/openai/v1 # Remove this line if using OpenAI directly
OPENAI_API_KEY=your_api_key_here
OPENAI_MODEL_NAME=llama-3.3-70b-versatile # Or gpt-4o-mini for OpenAI
SERPER_API_KEY=your_serper_key_here
⚠️ Groq free tier has a 12,000 TPM limit. For full pipeline runs, upgrade to Groq Dev Tier or use OpenAI (gpt-4o-mini).
def get_inputs():
return {
'product_or_company_name': 'YourCompany',
'target_market_or_industry': 'Your Industry',
'your_company_or_product_name': 'YourCompany',
'target_customer_segment_or_use_case': 'Your Target Segment',
'industry_or_market_context': 'Your Market Context',
'product_name_or_category': 'Your Product',
'target_market_or_customer_segment': 'Your Target Customer',
'industry_or_competitive_context': 'Your Competitive Context',
'company_name_or_business_objectives': 'Your Business Goal',
}src/market_researcher_crew/config/agents.yaml— agent roles, goals, backstoriessrc/market_researcher_crew/config/tasks.yaml— task descriptions and expected outputssrc/market_researcher_crew/crew.py— tools, logic, agent-task wiringsrc/market_researcher_crew/main.py— input variables
crewai run| Command | Description |
|---|---|
crewai run |
Run the full 5-agent pipeline |
crewai test -n 3 -m gpt-4o |
Test crew over 3 iterations |
crewai train -n 5 -f training.json |
Train the crew |
crewai replay -t <task_id> |
Replay from a specific task |
crewai log-tasks-outputs |
View latest task outputs |
crewai reset-memories -a |
Clear all agent memories |
market_researcher_crew/
├── src/market_researcher_crew/
│ ├── config/
│ │ ├── agents.yaml # Agent definitions
│ │ └── tasks.yaml # Task definitions
│ ├── tools/
│ │ └── custom_tool.py # Custom tool implementations
│ ├── crew.py # Crew orchestration
│ └── main.py # Entry point & inputs
├── knowledge/
│ └── user_preference.txt # User context for agents
├── .env # API keys (git-ignored)
├── .gitignore
├── pyproject.toml
└── uv.lock
- 📖 crewAI Documentation
- 💬 Join the Discord
- 🐛 crewAI GitHub
- 🔍 Groq Console — manage your API keys and usage