LP.
SYSTEM CASE STUDY

Multi-Agent Research Bot

Agentic workflow system where specialized AI agents collaborate to research topics, gather data, cross-verify facts, and produce comprehensive reports. Role-based agent teams with shared memory.

CrewAILangGraphOpenAIPythonRedis
VERIFIED BENCHMARKS & IMPACT
Benchmark

4 specialized agents coordinated via LangGraph

Benchmark

85% fact-check agreement rate

Benchmark

Reports generated in <5min

01 / THE PROBLEM

Operational Bottleneck

Research tasks require gathering information from multiple sources, verifying facts, and synthesizing findings - a process that's tedious and error-prone when done manually.

02 / SYSTEM ARCHITECTURE
01

Role-based agent teams: Researcher, Fact-Checker, Writer, Editor

02

LangGraph state machine for agent coordination and handoffs

03

Shared Redis memory store for cross-agent context

04

Web scraping and API integration for multi-source data gathering

05

Cross-verification pipeline that flags conflicting information

06

Structured report output with citations and confidence scores

03 / RESULTS & PRODUCTION IMPACT
-Multi-agent orchestration handles complex research tasks autonomously
-Cross-verification catches conflicting information before report generation
-Shared memory enables agents to build on each other's findings

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