AG2Open-source framework for building and orchestrating multi-agent LLM workflows.

4.7 (6)

Reviewed by Daniel Nikulshyn·Updated July 2026

Overview

AG2 is a developer framework designed to coordinate multiple LLM-powered agents that can collaborate, delegate tasks, and execute tools to solve complex problems. It provides abstractions for defining agent roles, conversation patterns, and shared memory, making it easier to build systems where several specialized agents work together rather than relying on a single monolithic prompt. The framework supports human-in-the-loop interactions, code execution, and integration with a range of model providers and external tools. It targets use cases such as research assistants, automated coding pipelines, data analysis workflows, and customer-facing agents that need structured reasoning and task decomposition. As an evolution of earlier multi-agent projects in the open-source ecosystem, AG2 emphasizes extensibility and community contribution, giving teams a foundation to prototype, optimize, and deploy agentic applications.

Key features

Multi-agent orchestration patterns
Configurable agent roles and conversations
Tool and function calling integration
Code execution support
Human-in-the-loop workflows
Provider-agnostic LLM connections

Pricing

Model: Free
Category: AI Agents Frameworks
Rating: 4.7 / 5 (6)

Use cases

Multi-agent orchestration

Build, orchestrate, and evolve systems of AI agents to create a cohesive team with AG2.

Cross-platform coordination

Assemble dynamic teams of specialized personas across diverse platforms, such as AG2, Google ADK, OpenAI, and LangChain.

Unified state management

Maintain a shared brain across task lifecycles, ensuring enterprise-level collaboration and decision auditability.

Pros & Cons

Pros

Open-source and extensible
Strong support for multi-agent collaboration
Works with multiple LLM providers
Human-in-the-loop and tool-use built in

Cons

Requires developer expertise to set up
Multi-agent debugging can be complex
Documentation still maturing

Reviews

4.7

Average from 6 ratings.

Yuki Mori

Mar 12, 2026

Years in this space

I've evaluated a lot of these over the years. What stands out here is configurable agent roles and conversations — handled better than most — and strong support for multi-agent collaboration. Requires developer expertise to set up is my one real gripe. Worth the time if this is your use case.

Beatriz Costa

Jan 20, 2026

Years in this space

I've evaluated a lot of these over the years. What stands out here is code execution support — handled better than most — and open-source and extensible. Requires developer expertise to set up is my one real gripe. Worth the time if this is your use case.

Sofia Lindqvist

Dec 6, 2025

Years in this space

I've evaluated a lot of these over the years. What stands out here is tool and function calling integration — handled better than most — and open-source and extensible. Worth the time if this is your use case.

Liam O’Connor

Dec 6, 2025

Years in this space

I've evaluated a lot of these over the years. What stands out here is provider-agnostic LLM connections — handled better than most — and works with multiple LLM providers. Worth the time if this is your use case.

Jamal Carter

Sep 18, 2025

Use it every day

Honestly didn't expect to like it this much. Human-in-the-loop workflows is exactly what I needed, and open-source and extensible. I do wish requires developer expertise to set up, but I reach for it almost every day now and it just clicks.

Leila Hassan

Aug 12, 2025

Years in this space

I've evaluated a lot of these over the years. What stands out here is multi-agent orchestration patterns — handled better than most — and strong support for multi-agent collaboration. Requires developer expertise to set up is my one real gripe. Worth the time if this is your use case.