Alternative Agent Frameworks

Multi-agent frameworks beyond LangGraph for specialized use cases.

Framework Comparison

Framework	Best For	Key Features	2026 Status
LangGraph 1.0.6	Complex stateful workflows	Persistence, streaming, human-in-loop	Production
CrewAI 0.203.x	Role-based collaboration	Hierarchical crews, a2a, HITL for Flows	Production
OpenAI Agents SDK 0.6.x	OpenAI ecosystem	Handoffs, guardrails, GPT-5.1, RealtimeRunner	Production
MS Agent Framework	Enterprise	AutoGen+SK merger, A2A, compliance	Public Preview
AG2	Open-source, flexible	Community fork of AutoGen	Active

CrewAI Hierarchical Crew (0.203.x)

from crewai import Agent, Crew, Task, Process
from crewai.flow.flow import Flow, listen, start

# Manager coordinates the team
manager = Agent(
    role="Project Manager",
    goal="Coordinate team efforts and ensure project success",
    backstory="Experienced project manager skilled at delegation",
    allow_delegation=True,
    memory=True,
    verbose=True
)

# Specialist agents
researcher = Agent(
    role="Researcher",
    goal="Provide accurate research and analysis",
    backstory="Expert researcher with deep analytical skills",
    allow_delegation=False,
    verbose=True
)

writer = Agent(
    role="Writer",
    goal="Create compelling content",
    backstory="Skilled writer who creates engaging content",
    allow_delegation=False,
    verbose=True
)

# Manager-led task
project_task = Task(
    description="Create a comprehensive market analysis report",
    expected_output="Executive summary, analysis, recommendations",
    agent=manager
)

# Hierarchical crew
crew = Crew(
    agents=[manager, researcher, writer],
    tasks=[project_task],
    process=Process.hierarchical,
    manager_llm="gpt-4o",
    memory=True,
    verbose=True
)

result = crew.kickoff()

OpenAI Agents SDK Multi-Agent (0.6.x)

from agents import Agent, Runner, handoff, tool
from agents.extensions.handoff_prompt import RECOMMENDED_PROMPT_PREFIX
# Note: v0.6.6 adds GPT-5.1 support, shell/apply_patch tools, RealtimeRunner

# Define specialized agents
researcher_agent = Agent(
    name="researcher",
    instructions=f"""{RECOMMENDED_PROMPT_PREFIX}
You are a research specialist. Gather information and facts.
When research is complete, hand off to the writer.""",
    model="gpt-4o"
)

writer_agent = Agent(
    name="writer",
    instructions=f"""{RECOMMENDED_PROMPT_PREFIX}
You are a content writer. Create compelling content from research.
When done, hand off to orchestrator for final review.""",
    model="gpt-4o"
)

# Orchestrator with handoffs
orchestrator = Agent(
    name="orchestrator",
    instructions=f"""{RECOMMENDED_PROMPT_PREFIX}
You coordinate research and writing tasks.
Hand off to researcher for information gathering.
Hand off to writer for content creation.""",
    model="gpt-4o",
    handoffs=[
        handoff(agent=researcher_agent),
        handoff(agent=writer_agent)
    ]
)

# Run with handoffs
async def run_workflow(task: str):
    runner = Runner()
    result = await runner.run(orchestrator, task)
    return result.final_output

Microsoft Agent Framework (2026)

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import TextMentionTermination
from autogen_ext.models.openai import OpenAIChatCompletionClient

# Create model client
model_client = OpenAIChatCompletionClient(model="gpt-4o")

# Define agents
planner = AssistantAgent(
    name="planner",
    description="Plans complex tasks and breaks them into steps",
    model_client=model_client,
    system_message="You are a planning expert. Break tasks into actionable steps."
)

executor = AssistantAgent(
    name="executor",
    description="Executes planned tasks",
    model_client=model_client,
    system_message="You execute tasks according to the plan."
)

reviewer = AssistantAgent(
    name="reviewer",
    description="Reviews work and provides feedback",
    model_client=model_client,
    system_message="You review work and ensure quality standards."
)

# Create team with termination condition
termination = TextMentionTermination("APPROVED")
team = RoundRobinGroupChat(
    participants=[planner, executor, reviewer],
    termination_condition=termination
)

# Run team
async def run_team(task: str):
    result = await team.run(task=task)
    return result.messages[-1].content

Decision Framework

Criteria	Choose
Need persistence & checkpoints	LangGraph
Role-based collaboration	CrewAI
OpenAI-native ecosystem	OpenAI Agents SDK
Enterprise compliance	Microsoft Agent Framework
Open-source flexibility	AG2
Complex state machines	LangGraph
Quick prototyping	CrewAI or OpenAI SDK
Production observability	LangGraph + Langfuse

Key Decisions

Decision	Recommendation
Framework	Match to team expertise + use case
Agent count	3-8 per workflow
Communication	Handoffs (OpenAI) or shared state (CrewAI)
Memory	Built-in for CrewAI, custom for others

Common Mistakes

• Mixing frameworks in one project (complexity explosion)
• Ignoring framework maturity (beta vs production)
• No fallback strategy (framework lock-in)
• Overcomplicating simple tasks (use single agent)

Related Skills

• langgraph-supervisor - LangGraph supervisor pattern
• multi-agent-orchestration - Framework-agnostic patterns
• agent-loops - Single agent patterns

Capability Details

crewai-patterns

Keywords: crewai, crew, hierarchical, delegation, role-based Solves:

• Build role-based agent teams
• Implement hierarchical coordination
• Enable agent delegation

openai-agents-sdk

Keywords: openai, agents sdk, handoffs, guardrails, tracing Solves:

• Use OpenAI Agents SDK patterns
• Implement handoff workflows
• Add guardrails and tracing

microsoft-agent-framework

Keywords: microsoft, autogen, semantic kernel, a2a, enterprise Solves:

• Build enterprise agent systems
• Use AutoGen/SK merged framework
• Implement A2A protocol

framework-selection

Keywords: choose, compare, framework, decision, which Solves:

• Select appropriate framework
• Compare framework capabilities
• Match framework to requirements