Agentic AI for Dummies, Part 2: Choosing Your Framework

A No-Nonsense Guide to LangChain, CrewAI, AutoGPT, and the Rest

Reading time: 15 minutes | Difficulty: Beginner to Intermediate

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In Part 1, we learned what AI agents are and how they work. Now comes the practical question: how do you actually build one?

The answer: you pick a framework.

Think of frameworks like construction kits. You could build a house by cutting down trees and forging your own nails. Or you could use pre-made materials that fit together nicely. Frameworks are those pre-made materials for AI agents.

The problem? There are a LOT of them. And picking the wrong one can waste months of your time.

Let's fix that.

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🗺️ The Framework Landscape in 2025

Here's what you need to know: the agentic AI framework space has exploded, but a few clear leaders have emerged.

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The Big Six

Framework	Who Makes It	One-Line Summary
LangGraph	LangChain	Maximum control for complex systems
CrewAI	João Moura	AI teams that work like human organizations
AutoGPT	Toran Bruce Richards	The pioneer, great for learning
Microsoft AutoGen	Microsoft	Enterprise-grade, Azure-integrated
OpenAI Responses API	OpenAI	Managed simplicity, minimal code
Claude SDK	Anthropic	Computer control + MCP protocol

Let's break each one down.

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🔗 LangChain / LangGraph: The Production Powerhouse

Best for: Complex enterprise systems requiring maximum control

The pitch: LangGraph treats agent workflows as graphs — nodes are tasks, edges are transitions. This gives you surgical precision over exactly what happens and when.

Pros

• ✅ 700+ integrations with external services
• ✅ Battle-tested by Klarna, Replit, LinkedIn, Elastic
• ✅ Excellent debugging tools (LangSmith)
• ✅ Graph-based workflows = total control
• ✅ Durable execution (survives failures, resumes from checkpoints)

Cons

• ❌ Steep learning curve — expect 20-30 hours to get comfortable
• ❌ Even simple chatbots need 100-200 lines of code
• ❌ Can feel over-abstracted for simple use cases

When to choose LangGraph

You're building something complex for production. You have experienced Python developers. You need maximum flexibility and don't mind the learning investment.

Code taste

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from langgraph.graph import StateGraph

# Define your workflow as a graph
workflow = StateGraph(AgentState)
workflow.add_node("research", research_node)
workflow.add_node("analyze", analyze_node)
workflow.add_node("write", write_node)
workflow.add_edge("research", "analyze")
workflow.add_edge("analyze", "write")

# Compile and run
app = workflow.compile()
result = app.invoke({"query": "Analyze market trends"})

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👥 CrewAI: AI Teams That Make Sense

Best for: Multi-agent workflows that mirror how human teams work

The pitch: Instead of one AI doing everything, you create a "crew" of specialized agents — a Researcher, an Analyst, a Writer — who collaborate like a real team.

Pros

• ✅ Fastest setup of any major framework — functional systems in minutes
• ✅ Intuitive role-based design (Manager, Researcher, Writer...)
• ✅ 5.76x faster than LangGraph on certain QA benchmarks
• ✅ YAML-based configuration keeps things clean
• ✅ Used by PwC, IBM, Oracle, NVIDIA

Cons

• ❌ 2-3x higher cost compared to LangGraph (more LLM calls)
• ❌ Less flexibility for highly customized use cases
• ❌ Debugging can be tricky inside Tasks

When to choose CrewAI

Your workflow naturally divides into roles — content pipelines, research teams, customer support. You want to get something working fast without learning a complex API.

Code taste

 Copy
from crewai import Agent, Task, Crew

# Define specialized agents
researcher = Agent(
    role="Market Researcher",
    goal="Find comprehensive market data",
    tools=[search_tool, scrape_tool]
)

analyst = Agent(
    role="Data Analyst", 
    goal="Analyze trends and create insights",
    tools=[python_tool]
)

# Create the crew
crew = Crew(
    agents=[researcher, analyst],
    tasks=[research_task, analysis_task],
    process="sequential"  # or "hierarchical" with a manager
)

result = crew.kickoff()

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🤖 AutoGPT: The Pioneer

Best for: Learning, prototyping, simple automation

The pitch: The first widely-accessible demo of GPT-4's autonomous capabilities. Give it a goal, watch it figure out how to achieve it.

Pros

• ✅ 180,000+ GitHub stars — most-starred AI agent project ever
• ✅ Visual agent builder (drag-and-drop)
• ✅ Great for understanding how agents work
• ✅ Active community, lots of examples

Cons

• ❌ Not production-ready for mission-critical applications
• ❌ Can get stuck in loops or forget what it's doing
• ❌ Unpredictable API costs (recursive calls add up)

When to choose AutoGPT

You're learning about agent architectures. You want to prototype quickly. You're building simple automation that doesn't need enterprise reliability.

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🏢 Microsoft AutoGen: Enterprise Grade

Best for: Microsoft/Azure environments, complex multi-agent scenarios

The pitch: Event-driven multi-agent architecture based on the actor model. Agents communicate through conversations, enabling sophisticated coordination.

Pros

• ✅ Deep Azure integration
• ✅ GroupChat for agent collaboration
• ✅ No-code GUI option (AutoGen Studio)
• ✅ Cross-language support (Python + .NET)

Cons

• ❌ In transition — merging with Semantic Kernel into unified "Agent Framework"
• ❌ Azure-centric (less portable to AWS/GCP)
• ❌ Complex setup

When to choose AutoGen

You're in a Microsoft shop. You have complex multi-agent scenarios. You want native Azure integration and enterprise support.

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✨ OpenAI Responses API: Managed Simplicity

Best for: Quick prototyping, minimal code, managed infrastructure

The pitch: OpenAI handles the complexity — conversation history, tool orchestration, state management. You just define what you want.

Pros

• ✅ Minimal code required
• ✅ Built-in tools (Code Interpreter, File Search)
• ✅ Excellent documentation
• ✅ Always has access to latest OpenAI models

Cons

• ❌ OpenAI lock-in
• ❌ Less customizable than open frameworks
• ❌ Note: Assistants API being deprecated (migrate to Responses API)

When to choose OpenAI APIs

You want something working fast. You're okay with OpenAI lock-in. You value managed infrastructure over flexibility.

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🧠 Anthropic Claude SDK: Computer Control + MCP

Best for: Coding agents, computer use, research assistants

The pitch: Claude can literally control computers — take screenshots, move the cursor, click buttons. Plus the Model Context Protocol (MCP) is becoming the universal standard for tool connectivity.

Pros

• ✅ Computer use capability — Claude can operate software like a human
• ✅ MCP protocol becoming industry standard (supported by OpenAI, Google, Microsoft)
• ✅ Same infrastructure powering Claude Code (state-of-the-art coding agent)
• ✅ Built-in web search

Cons

• ❌ Newer ecosystem (less mature than LangChain)
• ❌ Computer use still in beta (accuracy limitations)
• ❌ Anthropic-specific

When to choose Claude SDK

You're building coding assistants. You need computer control capabilities. You want to bet on MCP as the future standard.

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⚡ Quick Decision Guide

<!-- INSERT VISUAL: part2-framework-decision-tree.svg -->

Not sure where to start? Use this flowchart:

Your Situation	Best Choice
"I need maximum control"	LangGraph
"I want intuitive AI teams"	CrewAI
"I'm learning/prototyping"	AutoGPT or OpenAI API
"I'm in a Microsoft shop"	AutoGen → Agent Framework
"I need computer control"	Claude SDK
"I want managed simplicity"	OpenAI Responses API

The Good News: Standardization Is Coming

Two developments are making framework choice less permanent:

1. MCP (Model Context Protocol) — A universal connector for AI tools. Build a tool once, use it with any framework. Being adopted by everyone.

2. A2A (Agent2Agent Protocol) — Lets agents from different vendors collaborate. Your CrewAI agent can work with someone else's LangGraph agent.

So pick what gets you started fastest. You can always change later.

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🎯 Key Takeaways

1. No "best" framework — only the best framework for your situation

2. LangGraph = Maximum control, steep learning curve, production-ready

3. CrewAI = Fastest setup, intuitive teams, good for role-based workflows

4. AutoGPT = Great for learning, not for production

5. OpenAI/Anthropic APIs = Managed simplicity, less flexibility

6. Standardization (MCP, A2A) is reducing lock-in — pick what works now

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🔜 What's Next

In Part 3, we'll go deeper into how agents actually use tools — the mechanics of function calling, what tools are available, and the security considerations that matter.

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Series Navigation:

• Part 1: What is Agentic AI?
• Part 2: Choosing Your Framework ← You are here
• Part 3: How Agents Use Tools
• Part 4: Real-World Impact & The Future

Last updated: December 2025