07 Frameworks & Tools 3 min read 678 words

AutoGen and CrewAI (March 2026)

In 2025-2026, AutoGen and CrewAI have both undergone major rewrites. AutoGen 0.4 is a full async-first redesign (AgentChat API), while CrewAI added Flows — a state-machine orchestration layer alongside its classic Process model.

frameworkagentsorchestrationcore
01manager perspective

CrewAI: The Manager Perspective

CrewAI is built around the concept of a Process.

  • Role-Based Agents: You define a "Researcher," a "Writer," and a "Manager."
  • Tasks: Explicit goals with specific outputs.
  • Process Orchestration: Sequential, Hierarchical, or Consensual (Consensus-based).
1.1

CrewAI Flows (2025 Addition)

CrewAI Flows add a state-machine layer on top of the classic Crew pattern:

Pythonpython · 17 lines
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from crewai.flow.flow import Flow, listen, start

class ContentFlow(Flow):
    @start()
    def research_topic(self):
        # Returns research output
        return research_crew.kickoff({"topic": self.state["topic"]})
    
    @listen(research_topic)
    def write_article(self, research):
        # Triggered after research completes
        return writing_crew.kickoff({"research": research})
    
    @listen(write_article)
    def publish(self, article):
        # Final step
        return publisher.publish(article)

2026 Use Cases: CrewAI + Flows is the best framework for business process automation (content pipelines, data analysis workflows) where the structure is well-defined.

02developer perspective

AutoGen: The Developer Perspective

2.1

AutoGen 0.4 (Complete Rewrite)

Microsoft released AutoGen 0.4 in late 2025 — a complete rewrite with a new async-first architecture.

Pythonpython · 24 lines
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# AutoGen 0.4: AgentChat API
from autogen_agentchat.agents import AssistantAgent, UserProxyAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_ext.models import OpenAIChatCompletionClient

model_client = OpenAIChatCompletionClient(model="gpt-4o")

coder = AssistantAgent(
    "Coder",
    model_client=model_client,
    system_message="You write Python code."
)
reviewer = AssistantAgent(
    "Reviewer",
    model_client=model_client,
    system_message="You review code for bugs and style."
)

team = RoundRobinGroupChat([coder, reviewer], max_turns=4)

# Async-native
async def run():
    result = await team.run(task="Write a binary search function.")
    print(result.messages[-1].content)

Key 0.4 changes:

  • Event-driven: Agents communicate via typed events, not raw text messages
  • Async-first: Every API is async by default
  • AgentChat high-level API vs Core low-level API
  • Magentic-One: Microsoft's multi-agent system built on AutoGen 0.4 for complex web tasks
03p2p

Swarms and P2P

In late 2025, both frameworks have adopted Swarm Patterns.

  • The Handoff: Instead of a central supervisor, agents "Hand off" the conversation to the most relevant expert.
  • Example: A "Sales Agent" realizes the user is asking a technical question and hands off the thread to the "Support Agent."
04comparison matrix

Framework Comparison Matrix

FeatureCrewAIAutoGen 0.4LangGraph
Core AbstractionTask/Process/FlowEvent/TeamState/Graph
ArchitectureDeclarative + State MachineAsync Event-DrivenImperative DAG
Ease of UseHighMediumLow
ControlLow-MediumMediumHigh
Best ForBusiness AutomationsCollaborative LogicComplex Tool-Use
API StylePython classes + YAMLAsync PythonPython + JSON state
05questions

Interview Questions

Q: When would you use CrewAI instead of LangGraph?

Strong answer: Speed vs. Precision. I use CrewAI when I need to stand up a team of agents for a standard process (like content generation or data analysis) very quickly. It provides high-level abstractions for "Planning" and "Cooperation" out of the box. I switch to LangGraph when I need Granular Control over every state transition, multi-turn human-in-the-loop triggers, or complex error-recovery logic that doesn't fit into the "Role-playing team" metaphor.

Q: How does AutoGen handle "Infinite Loops" where agents keep talking to each other without solving the task?

Strong answer: We use Termination Conditions and Max Conversational Turns. In 2025, we also implement a "Critic Agent" whose only job is to detect if the conversation is stagnant. If the Critic detects circularity, it triggers a UserProxy to interrupt or force-switches the GroupChatManager to a different reasoning path. We also monitor Token Velocity: if an agent pair uses 100K tokens in 2 minutes without progress, we kill the session automatically.

06references

References

  • CrewAI. "The Multi-Agent Process Engine" (2025)
  • Microsoft Research. "AutoGen: Enabling Next-Gen LLM Applications" (2025)
  • OpenAI Swarm. "Lightweight Multi-Agent Orchestration" (2024 tech report)

Next: Framework Selection Guide

summary · added by this rebuild

Key takeaways

01

Both frameworks were rewritten, not patched

AutoGen 0.4 is a full async-first rewrite with typed events and the AgentChat API; CrewAI added Flows, a state-machine layer sitting above its classic Process model.

02

Pick by abstraction, not popularity

The comparison matrix maps CrewAI to Task/Process/Flow, AutoGen to Event/Team and LangGraph to State/Graph, and trades ease of use directly against control.

03

CrewAI fits well-defined business processes

Role-based agents, explicit tasks and sequential or hierarchical processes suit content pipelines and data-analysis workflows where the structure is known before the run starts.

04

Swarm handoffs replace the central supervisor

Rather than a manager routing everything, an agent that recognises a question outside its remit hands the thread directly to the relevant expert agent.