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Find the right AI tool in 2 minutes. Independent reviews and honest comparisons of 770+ AI tools.

  1. Home
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  3. AG2 Framework
OverviewPricingReviewWorth It?Free vs PaidDiscount
Multi-Agent Builders🔴Developer
A

AG2 Framework

The next-generation AG2 platform with AgentOS runtime, framework interoperability, teachable agents, and enhanced planning for production multi-agent systems.

Starting atFree
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💡

In Plain English

Next-generation version of Microsoft's AutoGen framework — enables AI agent teams to collaborate through natural conversations for complex problem solving.

OverviewFeaturesPricingUse CasesLimitationsFAQSecurityAlternatives

Overview

AG2 Framework (v0.11+) is the evolved version of AG2, pushing beyond basic multi-agent conversations into a full agent operating system. If AG2 is the conversation framework, AG2 Framework is the production platform.

The big addition: AgentOS. This runtime lets you connect agents built in different frameworks into one team. An AG2 agent, a LangChain agent, a Google ADK agent, and an OpenAI SDK agent can all participate in the same workflow. No other framework offers this level of cross-platform interoperability.

What's Different from Base AG2

Teachability: Agents learn from interactions and improve over time. You don't retrain the model. The framework stores lessons and applies them to future conversations. This makes agents better at recurring tasks without manual prompt engineering. Captain Agents: An agent that manages other agents. It can spin up specialized sub-agents on the fly, assign tasks, and dissolve them when finished. Think of it as a project manager agent that builds its own team based on the problem. Enhanced Planning Engine: Pluggable planning algorithms that handle multi-step workflows with conditional logic. The base AG2 framework uses simple conversation flow. AG2 Framework adds explicit planning that breaks complex goals into steps. Persistent Memory: Context survives across conversation sessions. Agents remember what happened yesterday. This matters for long-running projects where dropping context means redoing work.

Hosted Platform

Unlike base AG2 (self-host only), AG2 Framework offers a hosted option. You get 50 free executions/month to test. The $25/month Pro tier gives 100 executions. Enterprise scales to 30,000 executions with self-hosted Kubernetes deployment.

Pricing

  • Open Source: $0. Full framework, self-hosted, unlimited local use.
  • Hosted Free: 50 executions/month. Cloud hosting, basic support.
  • Hosted Pro: $25/month. 100 executions/month, priority support.
  • Enterprise: Custom pricing. Up to 30,000 executions, self-hosted K8s/VPC, enterprise support.

Source: ag2.ai

The Pricing Gotcha

"Executions" on the hosted platform mean workflow runs, not individual API calls. A single execution might contain 20 agent conversations burning $2-5 in LLM tokens. So 100 executions at $25/month could mean $200-500 in underlying API costs on top of the platform fee. Factor that into your budget.

Common Questions

Should I use AG2 or AG2 Framework? If you're prototyping multi-agent conversations, start with AG2. If you need persistent memory, cross-framework agents, or a hosted platform, use AG2 Framework. Is the hosted platform production-ready? For internal tools, yes. The framework itself rates as "medium" production readiness in independent comparisons. LangGraph and CrewAI have more production battle-testing. Can I mix agents from different frameworks? Yes. The AgentOS runtime is the standout feature. Connect LangChain, OpenAI, Google ADK, and native AG2 agents in one workflow.

What Real Users Say

Developers building with AG2 Framework call it "the right level of abstraction" for rapid iteration. The teachability feature gets specific praise for reducing prompt engineering over time. The main complaint: token consumption. Conversation-driven coordination generates verbose agent exchanges that cost more than structured task pipelines in CrewAI. Some users note confusion about the relationship between AG2, AutoGen, and Microsoft's Agent Framework.

Value Math

AG2 Framework Hosted Pro at $25/month + estimated $200-500/month in API costs for 100 executions. Compare to CrewAI Enterprise at $200/month (includes managed hosting) or LangGraph Cloud at usage-based pricing. AG2 Framework is cheaper at low volumes but less battle-tested. For self-hosted deployments, the framework is free and you pay only API costs.

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Editorial Review

AG2 Framework adds production features (persistent memory, cross-framework agents, hosted platform) on top of AG2's conversation-driven foundation. The AgentOS interoperability is unique. Token costs run high, and production readiness trails LangGraph and CrewAI.

Key Features

Conversation-Driven Coordination+

Agents coordinate through natural language conversations rather than rigid task definitions, enabling flexible problem-solving approaches.

Use Case:

Building a research team where agents debate methodologies and collaboratively refine their approach to complex analysis tasks.

Enhanced Planning Engine+

Improved planning algorithms that can handle complex, multi-step workflows with dependencies and conditional logic.

Use Case:

Creating software development workflows where agents plan features, implement code, and conduct reviews collaboratively.

Persistent Memory Management+

Better context preservation across conversation turns and agent interactions for long-running collaborative sessions.

Use Case:

Long-term research projects where agents need to remember findings and decisions from previous sessions.

Human-in-the-Loop Integration+

Seamless integration of human oversight and guidance within multi-agent conversations.

Use Case:

Complex decision-making scenarios where human expertise is needed to guide agent collaboration.

Code Execution & Testing+

Built-in sandboxed environments for collaborative code development, testing, and debugging by multiple agents.

Use Case:

Software development teams where multiple agents write, review, and test code together.

Flexible Termination Conditions+

Sophisticated conversation ending criteria based on goal achievement, consensus, or custom conditions.

Use Case:

Research collaborations that continue until agents reach consensus or meet quality thresholds.

Pricing Plans

Open Source

Free

forever

  • ✓Full framework/library
  • ✓Self-hosted
  • ✓Community support
  • ✓All core features
See Full Pricing →Free vs Paid →Is it worth it? →

Ready to get started with AG2 Framework?

View Pricing Options →

Best Use Cases

🎯

Use Case 1

Complex research and analysis projects requiring multiple specialized agent perspectives and collaborative reasoning

⚡

Use Case 2

Software development workflows where agents collaboratively plan features, implement code, and conduct reviews

🔧

Use Case 3

Open-ended problem solving scenarios where solution paths are not predetermined and benefit from agent debate and consensus

🚀

Use Case 4

Educational environments where understanding multi-agent conversations provides insight into collaborative AI approaches

💡

Use Case 5

Human-AI collaborative workflows requiring seamless integration of human expertise with agent capabilities

🔄

Use Case 6

Content creation and review processes benefiting from multiple agent perspectives and iterative refinement

📊

Use Case 7

Strategic planning and decision-making processes where multiple viewpoints enhance outcome quality

Limitations & What It Can't Do

We believe in transparent reviews. Here's what AG2 Framework doesn't handle well:

  • ⚠Higher token costs due to conversation overhead
  • ⚠Can be overkill for simple automation tasks
  • ⚠Requires careful prompt engineering for best results

Pros & Cons

✓ Pros

  • ✓AgentOS runtime connects agents from AG2, LangChain, OpenAI, and Google ADK in one workflow
  • ✓Teachable agents that improve over time without model retraining
  • ✓Captain Agents dynamically spawn and manage sub-agent teams
  • ✓Persistent memory preserves context across conversation sessions
  • ✓Hosted platform available with a free tier for testing
  • ✓Enhanced planning engine with pluggable algorithms for complex workflows
  • ✓Backward compatible with all existing AutoGen and AG2 code

✗ Cons

  • ✗Higher token consumption than structured task frameworks like CrewAI
  • ✗Production readiness rated "medium" compared to LangGraph in independent reviews
  • ✗Hosted platform execution limits (50/month free, 100/month for $25) don't include LLM costs
  • ✗Community confusion about AG2 vs AutoGen vs Microsoft Agent Framework
  • ✗Overkill for simple automation that doesn't need multi-agent coordination

Frequently Asked Questions

How does AG2 differ from the original AutoGen?+

AG2 includes enhanced planning, better memory management, more flexible termination conditions, and improved conversation patterns.

Can AG2 work with local LLMs?+

Yes, AG2 supports any OpenAI-compatible API including local models through Ollama, vLLM, or LiteLLM.

Is AG2 suitable for production use?+

Yes, but consider token costs and conversation management for high-volume applications. Best for complex, high-value tasks.

How do I migrate from AutoGen to AG2?+

AG2 maintains backward compatibility with most AutoGen patterns while offering new features. Migration guides are available in the documentation.

🦞

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What's New in 2026

AG2 v0.11.2 released February 2026. Introduced AgentOS vision for universal framework interoperability. Enhanced planning engine and memory management. Captain Agents and teachability features. Featured as leading framework in 2026 Agentic Frameworks Guide.

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Comparing Options?

See how AG2 Framework compares to AutoGen and other alternatives

View Full Comparison →

Alternatives to AG2 Framework

AutoGen

Agent Frameworks

Open-source multi-agent framework from Microsoft Research with asynchronous architecture, AutoGen Studio GUI, and OpenTelemetry observability. Now part of the unified Microsoft Agent Framework alongside Semantic Kernel.

CrewAI

AI Agent Builders

CrewAI is an open-source Python framework for orchestrating autonomous AI agents that collaborate as a team to accomplish complex tasks. You define agents with specific roles, goals, and tools, then organize them into crews with defined workflows. Agents can delegate work to each other, share context, and execute multi-step processes like market research, content creation, or data analysis. CrewAI supports sequential and parallel task execution, integrates with popular LLMs, and provides memory systems for agent learning. It's one of the most popular multi-agent frameworks with a large community and extensive documentation.

LangGraph

AI Agent Builders

Graph-based stateful orchestration runtime for agent loops.

Microsoft Semantic Kernel

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SDK for building AI agents with planners, memory, and connectors. - Enhanced AI-powered platform providing advanced capabilities for modern development and business workflows. Features comprehensive tooling, integrations, and scalable architecture designed for professional teams and enterprise environments.

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Quick Info

Category

Multi-Agent Builders

Website

ag2ai.github.io/ag2/
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