Top Agentic AI Frameworks

Technical Comparison of Top Agentic AI Frameworks

In this article, we skip the basics and get straight to the technical details. We first provide a curated list of popular agentic AI frameworks along with links to their repositories. Then, we compare the top four frameworks—Microsoft AutoGen, Microsoft Semantic Kernel, LangChain, and CrewAI—with concrete code examples demonstrating how to build a minimal agent for each.

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Framework Repository Directory

Below is a list of widely used agentic AI frameworks along with direct links to their GitHub repositories (or equivalent):

• Microsoft AutoGen – An open‑source framework for multi‑agent orchestration and autonomous decision‑making.
  GitHub Repo
  citeturn0search7

• Microsoft Semantic Kernel – A production‑grade SDK that modularizes AI “skills” for composing complex workflows.
  GitHub Repo
  citeturn0search7

• LangChain – A modular, LLM‑driven framework that supports prompt chaining, memory management, and external tool integration.
  GitHub Repo

• CrewAI – A role‑based framework focused on collaborative multi‑agent systems where agents share tasks and communicate dynamically.
  GitHub Repo

• Atomic Agents – A minimalistic, open‑source library emphasizing control and consistency in multi‑agent pipelines.
  GitHub Repo

• Hugging Face Transformers Agents – Leverages the Transformers ecosystem to build multimodal, task‑specific agents.
  GitHub Repo (See examples for agent implementations.)

• Langflow – A low‑code, visual interface for quickly prototyping agent workflows.
  GitHub Repo

• Dify – An LLMOps platform with a visual development interface for rapid AI agent deployment.
  GitHub Repo

• Lyzr – A no‑code platform tailored for enterprises with pre‑built agents across multiple business functions.
  closed source

• AgentGPT / BabyAGI / MetaGPT / Swarms – Emerging platforms aimed at both non‑technical users and developers for rapid agent prototyping.

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Comparing the Top 4 Frameworks

For our technical comparison, we now focus on Microsoft AutoGen, Microsoft Semantic Kernel, LangChain, and CrewAI. We demonstrate minimal examples that illustrate how each framework can be used to define and execute a simple task.

1. Microsoft AutoGen

AutoGen is designed for orchestrating multiple agents in an event‑driven, distributed environment. Below is a simplified Python example that demonstrates how you might initialize two collaborating agents that execute a simple function (e.g., data retrieval).

# Example using Microsoft AutoGen (pseudo-code)
from autogen import Agent, TaskOrchestrator

# Define a simple agent function
def fetch_data():
    # Imagine this function retrieves data from an API
    return "Data retrieved"

# Create two agents: one to fetch and one to process
fetch_agent = Agent(name="Fetcher", action=fetch_data)
process_agent = Agent(name="Processor", action=lambda data: f"Processed {data}")

# Orchestrate the agents
orchestrator = TaskOrchestrator(agents=[fetch_agent, process_agent])
result = orchestrator.run()
print(result)  # Expected Output: "Processed Data retrieved"

This example shows how AutoGen can connect separate agent functionalities into a single workflow.

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2. Microsoft Semantic Kernel

Semantic Kernel enables you to compose “skills”—small, reusable AI functions—into larger workflows. Here’s an example in Python that registers a simple text summarization skill and executes it.

# Example using Microsoft Semantic Kernel
from semantic_kernel import Kernel, Skill

# Initialize the kernel
kernel = Kernel()

# Define a summarization skill
def summarize(text: str) -> str:
    # This is a placeholder for an actual summarization model call.
    return "Summary: " + text[:50] + "..."

# Register the skill
kernel.register_skill("TextSummarizer", Skill(function=summarize))

# Execute the skill with sample input
input_text = "This is a long article that needs summarization..."
summary = kernel.run_skill("TextSummarizer", input_text)
print(summary)  # Expected Output: "Summary: This is a long article that needs summar..."

Semantic Kernel’s composable design makes it ideal for enterprise applications that require modular AI components.

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3. LangChain

LangChain excels in building multi‑step workflows by chaining LLM prompts together with memory and tool integration. The following example demonstrates a simple prompt chain that retrieves data and then summarizes it.

# Example using LangChain
from langchain import PromptChain, LLM, Memory

# Initialize an LLM (e.g., GPT-4)
llm = LLM(api_key="YOUR_API_KEY")

# Define a prompt chain: first retrieve data, then summarize
data_prompt = "Retrieve recent sales data for Q1."
summary_prompt = "Summarize the following sales data: {data}"

# Create a prompt chain with memory to pass output between steps
chain = PromptChain(steps=[
    {"prompt": data_prompt, "output_key": "data"},
    {"prompt": summary_prompt, "input_keys": ["data"], "output_key": "summary"}
], llm=llm, memory=Memory())

result = chain.run()
print(result["summary"])

This code illustrates LangChain’s strength in sequentially processing data through chained prompts.

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4. CrewAI

CrewAI is built for collaborative multi‑agent scenarios where agents assume specialized roles. In this example, we simulate a two-agent team where one agent gathers information and another synthesizes it.

# Example using CrewAI (pseudo-code)
from crewai import Crew, Agent

# Define agent functions
def gather_info():
    # Placeholder: Simulate data gathering
    return "Market analysis data"

def synthesize_info(data):
    # Placeholder: Simulate synthesis of gathered data
    return f"Synthesized Report: {data}"

# Create agents with designated roles
info_agent = Agent(name="InfoGatherer", role="Data Collector", action=gather_info)
report_agent = Agent(name="ReportSynthesizer", role="Analyst", action=lambda: synthesize_info(info_agent.run()))

# Build a crew (team of agents)
crew = Crew(agents=[info_agent, report_agent])

# Run the crew workflow
final_report = crew.execute()
print(final_report)  # Expected Output: "Synthesized Report: Market analysis data"

CrewAI’s role-based architecture allows for a clear division of tasks, making it a good fit for applications requiring structured collaboration.

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Conclusion

This technical article presented a curated list of popular agentic AI frameworks, complete with repository links, and then compared four top contenders—Microsoft AutoGen, Microsoft Semantic Kernel, LangChain, and CrewAI—through minimal code examples. Each framework has its own strengths:

• AutoGen is ideal for distributed, multi-agent orchestration.
• Semantic Kernel excels in modular, enterprise-grade skill composition.
• LangChain provides flexible chaining of prompts with memory support.
• CrewAI shines in role‑based, collaborative agent teams.

These examples should give you a starting point to experiment with building your own autonomous workflows and help you choose the right technology stack for your specific use cases.

Feel free to explore the linked repositories for more detailed documentation and advanced examples. Happy coding!

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