Multi-Agent AutoResearch with Open Source Models

Hugging Face · Beginner ·🤖 AI Agents & Automation ·2d ago

Skills: Multi-Agent Systems90%Tool Use & Function Calling70%

In this video, we walk through a multi-agent setup of AutoResearch using open source models and OpenCode. Timestamps 00:00 - Introduction: Multi-agent AutoResearch setup 01:07 - Agent Roles: Researcher, Planner, Worker, and Reporter agents 01:55 - Repository Setup: Exploring the repo structure and files 02:11 - Environment Configuration: Python setup, UVSync, HF Hub login 03:20 - OpenCode Interface: UI walkthrough and agent configuration 03:51 - Running the Experiment: Autonomous research pass execution 05:17 - Sub-agents in Action: Planner and Reviewer agent collaboration 07:08 - Trackio Metrics: Tracking training efficiency and job monitoring 08:23 - Hugging Face Hub: Jobs running on HF infrastructure 08:58 - Conclusion: Try it yourself with OpenCode Links: Repo: https://github.com/burtenshaw/multiautoresearch Twitter post with learnings: https://x.com/ben_burtenshaw/status/2045085809800356112

Watch on YouTube ↗ (saves to browser)

Sign in to unlock AI tutor explanation · ⚡30

More on: Multi-Agent Systems

View skill →

Command: a new tool for building multi-agent architectures in LangGraph

Command: a new tool for building multi-agent architectures in LangGraph

Hierarchical multi-agent systems with LangGraph

Hierarchical multi-agent systems with LangGraph

Building an multi-agent concierge system using LlamaIndex Workflows

Building an multi-agent concierge system using LlamaIndex Workflows

Practical Multi AI Agents and Advanced Use Cases with crewAI

Practical Multi AI Agents and Advanced Use Cases with crewAI

AutoGen: Multi-Agent AI Conversation, complete code

AutoGen: Multi-Agent AI Conversation, complete code

Workshop: Build Multi-Agent Workflows with Low-Code Interfaces - AI PM Community Session #61

Workshop: Build Multi-Agent Workflows with Low-Code Interfaces - AI PM Community Session #61

Product Management Exercises

Related AI Lessons

Sauce Labs Launches AI Agent to Automate Test Creation and Close the DevOps “Velocity Gap”

Sauce Labs launches AI agent to automate test creation, reducing DevOps velocity gap

BizNode runs entirely on your machine — no cloud, no subscriptions, no monthly fees. Your AI business operator that works 24/7

Run your own AI business operator node locally with BizNode, eliminating cloud costs and subscriptions

We Built a 3-Layer Audit Trail (AI + GPS + Blockchain) to Eliminate Greenwashing in Ocean Conservation

Learn how to build a 3-layer audit trail using AI, GPS, and blockchain to ensure transparency in environmental conservation efforts

How to Move Fast Without Breaking Judgment

Learn to balance speed with discipline when working with AI to avoid recklessness

Chapters (10)

Introduction: Multi-agent AutoResearch setup

1:07 Agent Roles: Researcher, Planner, Worker, and Reporter agents

1:55 Repository Setup: Exploring the repo structure and files

2:11 Environment Configuration: Python setup, UVSync, HF Hub login

3:20 OpenCode Interface: UI walkthrough and agent configuration

3:51 Running the Experiment: Autonomous research pass execution

5:17 Sub-agents in Action: Planner and Reviewer agent collaboration

7:08 Trackio Metrics: Tracking training efficiency and job monitoring

8:23 Hugging Face Hub: Jobs running on HF infrastructure

8:58 Conclusion: Try it yourself with OpenCode

How To Win Humanity's Last Hackathon - The hardest agent contest in AI.