skills.rest
oabdelmaksoud
oabdelmaksoudCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

autogen-collab

Automate multi-agent debates to reach consensus with configurable rounds and logging.

4|Updated Feb 26, 2026
Search Tags:#logging#multi-agent#orchestration#consensus#debate#autogen
One-click install
npx skills add https://github.com/oabdelmaksoud/Openclaw-skills-Compilations --skill autogen-collab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: autogen-collab
Source: https://github.com/oabdelmaksoud/Openclaw-skills-Compilations/tree/main/autogen-collab
Command: npx skills add https://github.com/oabdelmaksoud/Openclaw-skills-Compilations --skill autogen-collab

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates AutoGen-style debates to reach consensus among specialist agents, reducing manual coordination and accelerating high-stakes decision-making.

Core Features & Use Cases

  • Orchestrates multi-agent debates with configurable agent sets, rounds, and timeouts to surface consensus decisions.
  • Detects consensus using explicit signals and outputs a final result with provenance and audit logs.
  • Logs conversation transcripts and run metadata for auditing, QA, and knowledge capture.
  • Use cases include architectural design reviews, root cause analysis, and research tasks requiring diverse expert input.

Quick Start

Run the autogen_runner.py with mode debate, specifying a task and the agents to start a consensus-driven discussion.

Frequently Asked Questions about autogen-collab

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate multi-agent debates to reach a consensus on architecture decisions?▼

Multi-agent debates for architecture decisions are automated by orchestrating specialist agents through configurable rounds to surface consensus decisions. This AutoGen-style coordination applies explicit signals to detect agreement and generate final results with audit logs.

When do I need multi-agent orchestration for root cause analysis?▼

Multi-agent orchestration is needed for root cause analysis when diverse expert perspectives improve outcomes. Orchestrating specialist agents through structured debate rounds surfaces consensus decisions and logs conversation transcripts for knowledge capture.

How do I coordinate multiple agent turns and log conversation transcripts for design reviews?▼

Coordinate multiple agent turns by running the debate runner with configurable agent sets and timeouts. The system logs conversation transcripts and run metadata alongside the final consensus result, providing provenance for design reviews.

Can I configure agent sets, rounds, and timeouts for consensus-driven research tasks?▼

Consensus-driven research tasks support configurable agent sets, rounds, and timeouts. The orchestration detects consensus using explicit signals, enabling diverse expert input to converge on a final result with provenance and audit logs.

Does multi-agent consensus orchestration require external dependencies to run?▼

Multi-agent consensus orchestration runs without external dependencies, using OpenClaw's gateway to automate AutoGen-style debates. It handles coordination of multiple agent turns, logging, consensus signaling, and final result generation natively.

Related Skills

Explore More Software Engineering Skills→
TheophilusChinomonaTheophilusChinomona
2

openclaw-multi-agent-team-setup

Configure multi-agent teams with roles, bindings, and group chat in OpenClaw.

Community
Advanced
JmarquesJmarques

to-spec

Generate project specifications from codebase analysis and conversation context.

Community
Intermediate
frankxaifrankxai
6

Agent Orchestration Patterns

Coordinate multi-agent collaboration using orchestration patterns like Sequential Pipeline and Parallel Fan-Out.

Community
Intermediate
Geralt1983Geralt1983
1

stream-chain

Execute multi-agent pipelines through sequential prompt chaining and predefined workflows.

Community
Advanced