---
title: "Building a Research Crew: Multi-Agent Team for Market Analysis"
description: "Build a complete CrewAI multi-agent team with researcher, analyst, and writer agents that collaborate through a task pipeline to produce a comprehensive market analysis report."
canonical: https://callsphere.ai/blog/building-research-crew-multi-agent-team-market-analysis
category: "Learn Agentic AI"
tags: ["CrewAI", "Market Analysis", "Multi-Agent", "Project", "Python"]
author: "CallSphere Team"
published: 2026-03-17T00:00:00.000Z
updated: 2026-05-06T01:02:44.039Z
---

# Building a Research Crew: Multi-Agent Team for Market Analysis

> Build a complete CrewAI multi-agent team with researcher, analyst, and writer agents that collaborate through a task pipeline to produce a comprehensive market analysis report.

## From Theory to a Working Product

The previous posts in this series covered CrewAI's components individually — agents, tasks, tools, memory, and process types. Now it is time to combine everything into a complete, working application: a multi-agent research crew that performs market analysis.

This crew takes a market topic as input and produces a structured report with research findings, competitive analysis, and strategic recommendations. It demonstrates agent specialization, tool usage, context chaining, and output quality techniques.

## Architecture Overview

The crew consists of three specialized agents organized in a sequential pipeline:

```mermaid
flowchart LR
    INPUT(["User intent"])
    PARSE["Parse plus
classify"]
    PLAN["Plan and tool
selection"]
    AGENT["Agent loop
LLM plus tools"]
    GUARD{"Guardrails
and policy"}
    EXEC["Execute and
verify result"]
    OBS[("Trace and metrics")]
    OUT(["Outcome plus
next action"])
    INPUT --> PARSE --> PLAN --> AGENT --> GUARD
    GUARD -->|Pass| EXEC --> OUT
    GUARD -->|Fail| AGENT
    AGENT --> OBS
    style AGENT fill:#4f46e5,stroke:#4338ca,color:#fff
    style GUARD fill:#f59e0b,stroke:#d97706,color:#1f2937
    style OBS fill:#ede9fe,stroke:#7c3aed,color:#1e1b4b
    style OUT fill:#059669,stroke:#047857,color:#fff
```

1. **Market Researcher** — Gathers raw data from web searches and sources
2. **Competitive Analyst** — Analyzes the data, identifies patterns, and scores competitors
3. **Report Writer** — Synthesizes everything into a polished, executive-ready report

Each agent has distinct tools, goals, and backstories that make it genuinely specialized rather than a generic LLM wrapper.

## Setting Up the Environment

```bash
pip install crewai crewai-tools
export OPENAI_API_KEY="sk-your-key"
export SERPER_API_KEY="your-serper-key"
```

## Defining the Agents

```python
from crewai import Agent, LLM
from crewai_tools import SerperDevTool, ScrapeWebsiteTool

search_tool = SerperDevTool()
scrape_tool = ScrapeWebsiteTool()

researcher = Agent(
    role="Senior Market Researcher",
    goal="""Gather comprehensive, up-to-date market data including
    market size, growth rates, key players, and emerging trends.
    Always cite sources and distinguish facts from estimates.""",
    backstory="""You are a senior researcher at Gartner with 12 years
    of experience covering technology markets. You are meticulous about
    data accuracy and always cross-reference multiple sources. You know
    how to find information in earnings reports, industry publications,
    and analyst briefings.""",
    tools=[search_tool, scrape_tool],
    verbose=True,
    max_iter=15,
)

analyst = Agent(
    role="Competitive Intelligence Analyst",
    goal="""Analyze market data to identify competitive dynamics,
    strengths and weaknesses of key players, market gaps, and
    strategic opportunities. Produce quantitative assessments
    wherever possible.""",
    backstory="""You are a former McKinsey associate who transitioned
    to competitive intelligence. You think in frameworks — Porter's
    Five Forces, SWOT, value chain analysis. You are skeptical of
    surface-level analysis and always dig deeper into the 'why'
    behind market movements.""",
    verbose=True,
)

writer = Agent(
    role="Executive Report Writer",
    goal="""Transform research and analysis into a polished,
    executive-ready report that is clear, actionable, and
    well-structured. Use data to support every claim.""",
    backstory="""You are a communications director who has written
    board-level reports for Fortune 500 companies. You know that
    executives have limited time, so you lead with insights, support
    with data, and end with clear recommendations.""",
    verbose=True,
)
```

Notice the specificity in each agent's definition. The researcher is methodical and source-focused. The analyst thinks in frameworks. The writer is executive-oriented. These distinct personas produce genuinely different outputs.

## Defining the Task Pipeline

```python
from crewai import Task

research_task = Task(
    description="""Research the {market} market comprehensively. Cover:
    1. Current market size (2025-2026 estimates)
    2. Projected growth rate (CAGR) through 2030
    3. Top 5 companies by market share
    4. 3 emerging trends reshaping the market
    5. Key risks or headwinds facing the market

    Use web search to find recent data. Cross-reference at least
    2 sources for market size figures.""",
    expected_output="""A structured research document with sections for
    market size, growth projections, competitive landscape (table format),
    trends (numbered list with explanations), and risks. Include source
    URLs where available.""",
    agent=researcher,
)

analysis_task = Task(
    description="""Using the research data, perform a competitive analysis:
    1. Rank the top 5 players by competitive strength (1-10 scale)
    2. Identify each player's primary competitive advantage
    3. Identify 2 underserved market segments
    4. Assess barriers to entry for new competitors
    5. Provide a SWOT analysis for a hypothetical new entrant""",
    expected_output="""A competitive analysis report containing:
    - Competitive ranking table (company, score, rationale)
    - Market gap analysis (2 gaps with size estimates)
    - Barriers to entry assessment (high/medium/low with explanation)
    - New entrant SWOT analysis in quadrant format""",
    agent=analyst,
    context=[research_task],
)

report_task = Task(
    description="""Write an executive market analysis report combining
    the research and competitive analysis. The report should:
    1. Open with a 3-sentence executive summary
    2. Present key market data with supporting figures
    3. Include the competitive landscape analysis
    4. Identify the top 3 strategic opportunities
    5. Close with actionable recommendations

    Write for a C-suite audience. Use data from the research
    and analysis — do not make up statistics.""",
    expected_output="""A 800-1000 word executive report with clear
    sections: Executive Summary, Market Overview, Competitive Landscape,
    Strategic Opportunities, and Recommendations. Professional tone,
    data-driven, with specific numbers and actionable next steps.""",
    agent=writer,
    context=[research_task, analysis_task],
)
```

The explicit `context` parameter on the report task ensures the writer receives both the raw research and the competitive analysis, not just the immediately preceding task output.

## Assembling and Running the Crew

```python
from crewai import Crew, Process

market_research_crew = Crew(
    agents=[researcher, analyst, writer],
    tasks=[research_task, analysis_task, report_task],
    process=Process.sequential,
    memory=True,
    verbose=True,
)

result = market_research_crew.kickoff(
    inputs={"market": "AI-powered customer service automation"}
)

print("=" * 60)
print("FINAL REPORT")
print("=" * 60)
print(result.raw)
```

The `{market}` placeholder in the research task description is replaced at runtime. This makes the crew reusable for any market topic.

## Improving Output Quality

Three techniques significantly improve the quality of crew output:

**Technique 1: Guardrails in expected_output**

```python
analysis_task = Task(
    description="Analyze the competitive landscape.",
    expected_output="""Provide scores on a 1-10 scale. A score of 10 means
    market dominance with no significant vulnerabilities. A score below 5
    means the company faces existential threats. Justify every score with
    at least one specific data point from the research.""",
    agent=analyst,
)
```

**Technique 2: Output validation with Pydantic**

```python
from pydantic import BaseModel
from typing import List

class MarketReport(BaseModel):
    executive_summary: str
    market_size_usd: str
    growth_rate: str
    top_players: List[str]
    recommendations: List[str]

report_task = Task(
    description="Write the market analysis report.",
    expected_output="A structured market report.",
    agent=writer,
    output_pydantic=MarketReport,
)
```

**Technique 3: Enable memory for iterative improvement**

Running the crew multiple times with memory enabled lets agents build on past research, producing progressively better reports.

## FAQ

### How long does a full crew execution take?

A three-agent sequential crew with web search typically takes 2 to 5 minutes, depending on the number of search queries and the complexity of analysis. The researcher usually takes the longest because of tool calls. Budget 10 to 15 LLM calls total.

### How do I save the output to a file?

After `kickoff()`, write the result to disk. You can also use the `output_file` parameter on the final task: `Task(..., output_file="report.md")`. CrewAI writes the task output directly to the specified file path.

### Can I add a review or editing step?

Yes. Add a fourth agent with a "Quality Reviewer" role and a task that takes the report as context and returns feedback or a revised version. This adds cost but catches errors, inconsistencies, and quality issues that a single pass might miss.

---

#CrewAI #MarketAnalysis #MultiAgent #Project #Python #AgenticAI #LearnAI #AIEngineering

---

Source: https://callsphere.ai/blog/building-research-crew-multi-agent-team-market-analysis