Local Multi-Agent Research Assistant Saves 15-25 Minutes Per Task

Practical Multi-Agent Research Pipeline

A Reddit user shared their working local LLM setup for research tasks. As an IT admin with 7 weeks of local LLM experience, they built a system that significantly reduces research time.

Hardware and Software Setup

• Hardware: RTX 5090, 64GB RAM
• All models run locally via Ollama
• System runs inside OpenClaw for agent sessions, cron scheduling, memory hooks, and Discord integrations

Research Pipeline Comparison

Before: Google search → open 5-10 tabs → read → take notes → summarize (20-30 minutes)

Now: Type topic → structured brief in ~2 minutes

Agent Architecture

• Researcher agent: qwen3.5:35b local model searches via Brave API and synthesizes information
• Analyst + Writer: GPT-5.4-mini (local GPU still being optimized) adds analysis and formatting
• Runtime: Average 150 seconds depending on topic

Time Savings

• 15-25 minutes saved per research task
• 1-2 hours weekly for regular researchers
• User notes: "Still need to verify outputs. AI assistance, not replacement."

Additional Features

• Persistent memory using PostgreSQL + pgvector
• Daily briefs
• Automated cron jobs
• User describes it as: "Nothing fancy, just practical automation."

The user is seeking feedback from others who have built similar systems and has published a full writeup with more details.