Research & Analysis Team
Automated research pipeline that searches multiple sources, collects data, analyzes findings, and produces comprehensive reports.
🎯 Buy once, deploy on any framework
Includes implementations for OpenClaw, CrewAI, LangGraph, AutoGen, Semantic Kernel, Vanilla Python. One purchase — all platforms.
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- ✓ All 6 platform implementations
- ✓ Full source code & documentation
- ✓ Commercial license included
- ✓ 30-day money-back guarantee
- ✓ Free updates for 1 year
- ✓ 30-day email support
Choose Your Platform
One purchase includes all 6 implementations. Deploy on whichever framework fits your stack.
OpenClaw
Research skill pack with auto-triggered agents, memory integration for persistent research context, and scheduled research runs via cron.
Included in OpenClaw version
- ✓4 agent skill configs (.md)
- ✓AGENTS.md orchestration
- ✓Memory integration for research history
- ✓Cron schedule for recurring research
- ✓Report template configs
⚡ Why OpenClaw?
One-click install, automatic orchestration, built-in cron scheduling, and memory integration. Other platforms require manual setup — OpenClaw gets you to production in minutes.
Code Preview — OpenClaw
# Install the Research & Analysis skill pack
openclaw skills install research-analysis-team
# AGENTS.md orchestration
# ─────────────────────────────────────
# ## Research Pipeline
# When research is requested:
# 1. Search Agent queries web, news, academic sources
# 2. Data Collector deduplicates & validates sources
# 3. Analyst identifies patterns and insights
# 4. Report Writer produces formatted report
#
# If Analyst finds gaps → loop back to Search Agent
#
# ## Memory
# All research stored in memory/ for context across sessions
#
# ## Cron: Weekly Market Scan
# 0 8 * * 1 — Monday morning competitive analysisAgent Architecture
How the 4 agents work together
Your data, triggers, or requests
Search Agent
Multi-Source Information Retrieval
Searches across multiple sources simultaneously using optimized queries. Handles web, news, papers, and internal docs.
Data Collector
Information Organization & Deduplication
Collects, cleans, and organizes raw search results. Removes duplicates and validates source credibility.
Analyst
Pattern Recognition & Insight Generation
Analyzes collected data for patterns, trends, contradictions, and key insights. Identifies gaps requiring further research.
Report Writer
Report Synthesis & Formatting
Synthesizes analysis into structured, professional reports with proper citations and visual elements.
Structured results, reports, and actions
What's Included
Everything you get with this template
The Problem
Research is time-intensive. A typical competitive analysis takes 2-3 days of manual searching, reading, note-taking, and report writing. Analysts spend 80% of their time gathering data and only 20% actually analyzing it.
The Solution
This 4-agent pipeline automates the entire research workflow. Searches multiple sources in parallel, deduplicates and validates findings, performs pattern analysis, and generates citation-rich reports — all in minutes instead of days.
Tools You'll Need
Everything required to build this 4-agent system — click any tool for details
Agent orchestration for the 4-agent research pipeline
LLM provider for analysis and report generation
AI-optimized web search for source discovery
Stateful research workflow with iterative loops
Web search API for broad information retrieval
Google search API for targeted queries
Vector storage for research document embeddings
Document parsing for PDFs and complex files
Tracing and debugging research agent chains
Database for storing research outputs and citations
Implementation Guide
10 steps to build this system • 2-3 hours estimated
📋 Prerequisites
Define your research scope and sources
Configure source types: web, news, academic papers, internal docs. Set depth parameters and output format templates.
Build the multi-source search layer
Wire up search connectors for each source type with parallel execution for speed.
Configure the Data Collector agent
Set up content extraction, deduplication logic, and source credibility scoring.
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Use Cases
Code Preview
Sample agent setup — see platform-specific previews above
from langgraph.graph import StateGraph, END
from typing import TypedDict, List
class ResearchState(TypedDict):
query: str
sources: List[dict]
analysis: dict
report: str
def search(state: ResearchState):
"""Search multiple sources in parallel"""
results = search_agent.run(state['query'])
return {'sources': results}
def analyze(state: ResearchState):
"""Analyze collected data for insights"""
insights = analyst_agent.run(state['sources'])
return {'analysis': insights}
workflow = StateGraph(ResearchState)
workflow.add_node('search', search)
workflow.add_node('collect', collect)
workflow.add_node('analyze', analyze)
workflow.add_node('write', write_report)
workflow.set_entry_point('search')
workflow.add_edge('search', 'collect')
workflow.add_edge('collect', 'analyze')
workflow.add_conditional_edges('analyze', needs_more_research)
workflow.add_edge('analyze', 'write')
workflow.add_edge('write', END)Example Input & Output
See what goes in and what comes out
{
"query": "AI agent framework market landscape 2026",
"depth": "comprehensive",
"sources": ["web", "news", "academic"],
"output_format": "markdown"
}# AI Agent Framework Market Landscape 2026 ## Executive Summary The AI agent framework market has grown 340% YoY... ## Key Players | Framework | Market Share | Funding | Key Differentiator | | CrewAI | 28% | $18M | Role-based agents | | LangGraph | 24% | $25M | Stateful workflows | ... ## Trends 1. Multi-agent systems overtaking single-agent... 2. Enterprise adoption accelerating... [47 sources cited]
Requirements
Reviews
What builders are saying
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Frequently Asked Questions
Do I get all 6 platform implementations?+
Yes — one purchase includes all 6 implementations. Start with OpenClaw for instant setup, or use CrewAI, LangGraph, AutoGen, Semantic Kernel, or vanilla Python if you prefer a specific framework.
What search sources does it support?+
Out of the box: web search (Tavily/SerpAPI), Google News, arXiv, and local documents. Adding custom sources is straightforward via the connector interface.
How does it handle conflicting information?+
The Analyst agent has a dedicated Contradiction Detector tool that flags conflicting data points and presents both sides with source credibility scores.
Can I use it with local/private data?+
Yes. The Search Agent supports local document loaders for PDFs, Word docs, and text files. Great for combining public research with internal knowledge.
What report formats are supported?+
Markdown, HTML, and PDF. The Report Writer uses templates that you can customize to match your organization's branding.
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