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

The Real AI War of 2026 Isn’t About Models — It’s About Platforms

The AI war of 2026 is about platforms, not models, and companies are racing to own the agent stack

Agent Failure Classifier: Post-Hoc Root Cause Analysis for Failed LLM Agent Runs

Learn to classify agent failures in LLMs using post-hoc root cause analysis to improve reliability

Medium · Machine Learning

1X Technologies — Deep Dive

Learn about 1X Technologies' mission to create general-purpose humanoid robots for home environments and its transition from a research startup to a consumer-facing hardware company

Agentic Workflow" the death of traditional Microservices Architecture?

Learn how Agentic Workflow is revolutionizing traditional Microservices Architecture with AI Agents and dynamic workflows

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

RL for Agents Workshop - Deep Dive on Training Agents with RL and Open Source