AG2 (formerly AutoGen)¶

What it is¶

AG2 is the next-generation evolution of the AutoGen framework. It is an open-source framework for building multi-agent AI applications that can converse with each other and interact with tools and environments. In early January 2027, it serves as a universal runtime (AG2 AgentOS) for orchestrating specialized agents from various frameworks, fully integrated with SOTA models like Claude 5.6, GPT-5.6, Gemini 4.0 Ultra, DeepSeek-V4, and Gemma 4 for local reasoning and the Model Context Protocol (MCP 3.1) and FastMCP 3.1 task execution standards.

What problem it solves¶

It simplifies the development of complex AI systems where multiple agents need to collaborate, reason, and act. AG2 addresses "islands of intelligence" by providing a universal runtime for framework interoperability, unified state management ("shared brain"), and standardized protocols (A2A and FastMCP 3.1) for secure agent-to-agent and agent-to-tool communication. It specifically solves the orchestration and latency bottleneck in large-scale multi-agent deployments.

Where it fits in the stack¶

Framework / Multi-Agent Orchestrator / Agent Runtime. AG2 AgentOS sits at the orchestrator layer, organizing and coordinating individual agents and routing messages.

Typical use cases¶

Multi-Framework Orchestration: Connecting agents built in different frameworks (e.g., a LangChain researcher and a PydanticAI analyst) into a single cohesive team.
Cross-Platform Coordination: Assembling dynamic teams of specialized personas that can operate across local (Gemma 4) and cloud (Claude 5.6/GPT-5.6/Gemini 4.0 Ultra) environments.
Unified State Management: Maintaining consistent context and task state across long-running, multi-step agentic workflows.
Visual Team Composition: Using Waldiez (the community-led visual companion) to design, validate, and debug multi-agent group chats.

Strengths¶

Protocol-First Interoperability: Native support for A2A (Agent-to-Agent) and MCP 3.1 / FastMCP 3.1 Task Protocols.
Flexible Conversational Design: Support for group chats, hierarchical orchestration, and custom state-based transitions.
Enterprise-Ready Security: Features like Agent Cards, guardrails, and secure tool-calling authorization for production environments.
Shared Brain Architecture: Advanced state management that prevents context loss or dilution in complex multi-step tasks.

Limitations¶

Transition Complexity: Migrating from legacy AutoGen (v0.2) to the AG2 AgentOS architecture requires refactoring of orchestration logic.
Orchestration Overhead: The high level of abstraction can make fine-grained control over individual LLM parameters more complex than using low-level SDKs.

When to use it¶

When you need to build sophisticated multi-agent systems involving agents from multiple different providers or frameworks.
When you require a proven, enterprise-grade foundation for collaborative AI workflows.
When building AI-native organizations where specialized agents must discover and delegate to each other dynamically.

When not to use it¶

For simple, single-agent tasks where a direct SDK call is sufficient.
If you prefer a rigid DAG-based workflow without conversational flexibility.

Getting started¶

Installation¶

pip install ag2 pydantic>=2.0

Basic Multi-Agent Setup¶

AG2 maintains compatibility with the autogen package name:

import autogen
from ag2 import AgentOS

# Initialize the universal runtime
runtime = AgentOS.init()

# Define agents
assistant = autogen.AssistantAgent("helper", llm_config={"model": "gpt-5.6"})
user_proxy = autogen.UserProxyAgent("user", code_execution_config={"use_docker": False})

# Orchestrate
user_proxy.initiate_chat(assistant, message="Analyze our cross-framework dependencies.")

CLI examples¶

Initializing a Project¶

ag2 init my-agent-org

Running in Studio Mode¶

ag2 studio --port 8081

Managing Agent Cards¶

ag2 cards list

API examples¶

Python (Universal AgentOS Config & FastMCP 3.1 Card Validation)¶

AG2 relies on Agent Cards and Runtime Configurations to coordinate multi-agent teams. The following example validates an AG2 runtime and agent cards setup using Pydantic v2:

import os
from typing import List, Dict, Any, Optional
from pydantic import BaseModel, Field, field_validator

# 1. Define robust Pydantic v2 schemas for AG2 Agent Cards & AgentOS Configurations
class AG2AgentCard(BaseModel):
    agent_id: str = Field(..., serialization_alias="agentId", validation_alias="agentId")
    name: str = Field(..., description="Name of the agent.")
    role: str = Field(..., description="The expertise or primary function of this agent.")
    model_name: str = Field(..., serialization_alias="modelName", validation_alias="modelName")
    mcp_tools: List[str] = Field(default_factory=list, serialization_alias="mcpTools", validation_alias="mcpTools")

    @field_validator("model_name")
    @classmethod
    def validate_model(cls, v: str) -> str:
        allowed = ["Claude 5.6", "GPT-5.6", "Gemini 4.0 Ultra", "DeepSeek-V4", "Gemma 4"]
        if not any(m in v for m in allowed):
            raise ValueError(f"Model {v} must be an early 2027 SOTA model: {allowed}")
        return v

class AG2RuntimeConfig(BaseModel):
    session_id: str = Field(..., serialization_alias="sessionId", validation_alias="sessionId")
    agents: List[AG2AgentCard] = Field(...)
    enable_shared_brain: bool = Field(default=True, serialization_alias="enableSharedBrain", validation_alias="enableSharedBrain")
    max_turns: int = Field(default=20, ge=1, le=100, serialization_alias="maxTurns", validation_alias="maxTurns")

# 2. Setup configuration payload for a collaborative analyst-researcher team
runtime_payload = {
    "sessionId": "session-ag2-9904",
    "enableSharedBrain": True,
    "maxTurns": 30,
    "agents": [
        {
            "agentId": "agent-researcher-1",
            "name": "Local Researcher",
            "role": "Retrieves local documentation data",
            "modelName": "Gemma 4",
            "mcpTools": ["fetch_file", "search_directory"]
        },
        {
            "agentId": "agent-analyst-1",
            "name": "Lead Analyst",
            "role": "Synthesizes final reports",
            "modelName": "Claude 5.6",
            "mcpTools": ["generate_chart"]
        }
    ]
}

# 3. Validate AG2 configuration payload
try:
    config = AG2RuntimeConfig(**runtime_payload)
    print("AG2 AgentOS runtime configuration verified successfully!")
    print(f"Session ID: {config.session_id}")
    print(f"Shared Brain Enabled: {config.enable_shared_brain}")
    print(f"Total Configured Agents: {len(config.agents)}")
    for agent in config.agents:
        print(f" - Agent: {agent.name} backed by {agent.model_name}")
except Exception as e:
    print(f"Configuration validation failed: {e}")

Gemma 4 — Canonical local LLM for agentic reasoning.
AutoGen — The original legacy framework.
CrewAI — Role-based multi-agent framework.
LangGraph — Graph-based agent orchestration.
Mastra — TypeScript-native agent framework.
Rivet — Visual AI programming environment.
MCP — Standardized tool-calling protocol.
PydanticAI — Type-safe agent framework.
Semantic Kernel — Microsoft's agentic framework.

Sources / references¶

Contribution Metadata¶

Last reviewed: 2027-01-07
Confidence: high