LangGraph vs AutoGen comparison

from langgraph import Graph, Node

# Define nodes
class Summarizer(Node):
    def run(self, text):
        # Imagine LLM call here
        return f"Summary of: {text[:50]}..."

class QuestionGenerator(Node):
    def run(self, summary):
        return [f"What is the main point of '{summary}'?"]

# Build graph
graph = Graph()
sum_node = Summarizer(name="summarizer")
qgen_node = QuestionGenerator(name="question_generator")
graph.add_node(sum_node)
graph.add_node(qgen_node)
graph.add_edge(sum_node, qgen_node)

# Run graph
output = graph.run("This is a long document text to summarize and question.")
print(output)

from autogen import Agent, MultiAgentSystem

class SummarizerAgent(Agent):
    def respond(self, message):
        return f"Summary of: {message[:50]}..."

class QuestionAgent(Agent):
    def respond(self, message):
        return [f"What is the main point of '{message}'?"]

# Setup multi-agent system
system = MultiAgentSystem()
summarizer = SummarizerAgent(name="summarizer")
questioner = QuestionAgent(name="questioner")
system.add_agents([summarizer, questioner])

# Run workflow
summary = summarizer.respond("This is a long document text to summarize and question.")
questions = questioner.respond(summary)
print(questions)