Alternative Agent Frameworks

Multi-agent frameworks beyond LangGraph for specialized use cases.

Framework Comparison

Framework Best For Key Features Status

LangGraph 1.0.6 Complex stateful workflows Persistence, streaming, human-in-loop Production

CrewAI 1.8.x Role-based collaboration Flows, hierarchical crews, a2a, HITL Production

OpenAI Agents SDK 0.7.0 OpenAI ecosystem Handoffs, guardrails, MCPServerManager, Sessions Production

GPT-5.2-Codex Long-horizon coding Context compaction, project-scale, security Production

MS Agent Framework Enterprise AutoGen+SK merger, A2A, compliance Public Preview

AG2 Open-source, flexible Community fork of AutoGen Active

CrewAI Hierarchical Crew (1.8.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-5.2", memory=True, verbose=True )

result = crew.kickoff()

OpenAI Agents SDK Multi-Agent (0.7.0)

from agents import Agent, Runner, handoff, RunConfig from agents.extensions.handoff_prompt import RECOMMENDED_PROMPT_PREFIX

Note: v0.7.0 adds MCPServerManager, opt-in nested handoffs, requires openai v2.x

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-5.2" )

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-5.2" )

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-5.2", handoffs=[ handoff(agent=researcher_agent), handoff(agent=writer_agent) ] )

Run with handoffs (v0.7.0: nested handoffs are opt-in)

async def run_workflow(task: str): runner = Runner() config = RunConfig(nest_handoff_history=True) # Opt-in for history packaging result = await runner.run(orchestrator, task, run_config=config) return result.final_output

Microsoft Agent Framework ()

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-5.2")

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

Long-horizon coding tasks GPT-5.2-Codex

Project-scale refactors GPT-5.2-Codex

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)

Reference Documents

• references/gpt-5-2-codex.md

• GPT-5.2-Codex agentic coding model

• references/openai-agents-sdk.md

• OpenAI Agents SDK patterns

• references/crewai-patterns.md

• CrewAI hierarchical crews

• references/microsoft-agent-framework.md

• Microsoft Agent Framework

• references/framework-comparison.md

• Decision matrix for framework selection

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

gpt-5-2-codex

Keywords: gpt-5.2-codex, codex, openai, agentic, coding, long-horizon, refactor, migration Solves:

• Long-horizon coding sessions

• Project-scale refactors and migrations

• Context compaction for extended tasks

• Security-aware code generation

• IDE integration with Cursor, Windsurf, GitHub