Multi-Agent System Reference
Table of Contents
Overview
Directory: multi_agents/
LangGraph-based system inspired by STORM paper. Generates 5-6 page reports with multiple agents collaborating.
Agent Roles
| Agent | File | Role |
|---|---|---|
| Human | - | Oversees and provides feedback |
| Chief Editor | agents/editor.py | Master coordinator via LangGraph |
| Researcher | Uses GPTResearcher | Deep research on topics |
| Editor | agents/editor.py | Plans outline and structure |
| Reviewer | agents/reviewer.py | Validates research correctness |
| Revisor | agents/revisor.py | Revises based on feedback |
| Writer | agents/writer.py | Compiles final report |
| Publisher | agents/publisher.py | Exports to PDF, DOCX, Markdown |
Workflow
1. Browser (GPTResearcher) → Initial research
2. Editor → Plans report outline
3. For each outline topic (parallel):
a. Researcher → In-depth subtopic research
b. Reviewer → Validates draft
c. Revisor → Revises until satisfactory
4. Writer → Compiles final report
5. Publisher → Exports to multiple formats
Usage
Via API
report_type = "multi_agents"
Via WebSocket
{
"task": "Research query",
"report_type": "multi_agents",
"tone": "Analytical"
}
Directly in Python
from multi_agents import run_research_task
report = await run_research_task(
query="Comprehensive analysis of market trends",
websocket=handler,
tone=Tone.Analytical,
)
Configuration File
File: multi_agents/task.json
Configure the multi-agent research task parameters and agent behaviors.