I built the Multi‑Agent Research System to speed up evidence-based research by combining multiple agents, live web search, and LLM critique loops into one interactive Streamlit app. The tool produces structured research reports that are reproducible and easy to iterate on.
What it solves?
Eliminates much of the manual searching and first‑draft authoring for research briefs. Adds an LLM-driven critique pass so outputs are iteratively improved. Keeps reports current by sourcing information from live search and scraping.
Key features:
Multi-agent orchestration for parallel information gathering. LangChain chains and prompt strategies to structure LLM interactions. Streamlit UI for non-technical stakeholders to run and review reports.
Source transparency: links and scraped snippets included for verification.
Potential use cases
Market research and competitor scans. Academic or technical literature overviews. Product discovery research and initial PRD inputs. Rapid ideation and technical due diligence.
How to get started?
Visit the GitHub repo for full instructions and demo details. Clone the repo, install dependencies, and run the Streamlit app (see README). Try different prompt templates and agent roles to tune output for your domain.
Contributing & collaboration Open to contributions: issue reports, PRs for scraping adapters, prompt templates, or deployments. If you’re a client or researcher interested in custom integrations (private data, workspace automation), DM me to discuss consulting or collaboration.
Explore the code: https://github.com/Mehadii-Hassan/Multi-Agent-Research-System
#ResearchAssistant #LangChain #Streamlit #LLM #WebSearch #OpenSource #ResearchAutomation #SaaS
0 likes · 0 views · Jul 31, 2026
I built the Multi‑Agent Research System to speed up evidence-based research by combining multiple agents, live web search, and LLM critique loops into one in...
