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.
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).
CrewAI Flows (2025 Addition)
CrewAI Flows add a state-machine layer on top of the classic Crew pattern:
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.
AutoGen: The Developer Perspective
AutoGen 0.4 (Complete Rewrite)
Microsoft released AutoGen 0.4 in late 2025 — a complete rewrite with a new async-first architecture.
# 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
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."
Framework Comparison Matrix
| Feature | CrewAI | AutoGen 0.4 | LangGraph |
|---|---|---|---|
| Core Abstraction | Task/Process/Flow | Event/Team | State/Graph |
| Architecture | Declarative + State Machine | Async Event-Driven | Imperative DAG |
| Ease of Use | High | Medium | Low |
| Control | Low-Medium | Medium | High |
| Best For | Business Automations | Collaborative Logic | Complex Tool-Use |
| API Style | Python classes + YAML | Async Python | Python + JSON state |
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.
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)
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.