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The Complete Guide to AI Agent Frameworks in 2024

Andrius Putna • • 10 min read
#ai#agents#frameworks#langchain#autogen#crewai#langgraph#llamaindex#guide#python

The Complete Guide to AI Agent Frameworks in 2024

The AI agent landscape has exploded over the past two years. What started as simple prompt chains has evolved into sophisticated autonomous systems capable of research, coding, data analysis, and complex multi-step reasoning. But with this growth comes a bewildering array of frameworks, each with different philosophies, architectures, and trade-offs.

This guide provides a comprehensive overview of the major AI agent frameworks available today, helping you understand their strengths, weaknesses, and ideal use cases. Whether you’re building a simple chatbot or a complex multi-agent system, you’ll find the framework that fits your needs.

What Makes an AI Agent Framework?

Before diving into specific frameworks, let’s establish what we mean by an “AI agent framework.” (For a complete overview of AI agent terminology, see our AI Agents Glossary.) At minimum, these frameworks provide:

More advanced frameworks add:

With these criteria in mind, let’s explore the major players.

LangChain: The Swiss Army Knife

Best for: General-purpose agent development, rapid prototyping, integration-heavy applications

LangChain has become the de facto standard for LLM application development. Launched in late 2022, it pioneered the concept of “chaining” LLM calls with tools, memory, and external data sources. For an in-depth exploration, see our LangChain Deep Dive.

Architecture Overview

LangChain organizes functionality across several packages:

from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.tools import TavilySearchResults

# Initialize components
llm = ChatOpenAI(model="gpt-4o")
search = TavilySearchResults(max_results=3)

# Create agent
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful research assistant."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}")
])

agent = create_tool_calling_agent(llm, [search], prompt)
executor = AgentExecutor(agent=agent, tools=[search])

result = executor.invoke({"input": "What's the latest news in AI?"})

Strengths

Weaknesses

When to Use LangChain

Choose LangChain when you need:

LangGraph: State Machines for Agents

Best for: Complex workflows, multi-agent systems, production deployments with human oversight

LangGraph emerged from LangChain as a specialized framework for building stateful, graph-based agent applications. It treats agent workflows as directed graphs with nodes (processing steps) and edges (transitions).

Architecture Overview

LangGraph introduces several key concepts:

from langgraph.graph import StateGraph, START, END
from langgraph.prebuilt import create_react_agent
from langgraph.checkpoint.memory import MemorySaver

# Define state
class AgentState(TypedDict):
    messages: list
    context: dict

# Create graph
graph = StateGraph(AgentState)

# Add nodes
graph.add_node("research", research_node)
graph.add_node("analyze", analysis_node)
graph.add_node("respond", response_node)

# Add edges
graph.add_edge(START, "research")
graph.add_conditional_edges("research", route_by_findings)
graph.add_edge("analyze", "respond")
graph.add_edge("respond", END)

# Compile with checkpointing
checkpointer = MemorySaver()
app = graph.compile(checkpointer=checkpointer)

Strengths

Weaknesses

When to Use LangGraph

Choose LangGraph when you need:

LlamaIndex: The Data-First Framework

Best for: RAG applications, knowledge bases, document Q&A systems

LlamaIndex (formerly GPT Index) focuses on connecting LLMs to external data. While it has agent capabilities, its primary strength is sophisticated data ingestion, indexing, and retrieval.

Architecture Overview

LlamaIndex centers on data concepts:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.agent import ReActAgent
from llama_index.core.tools import QueryEngineTool
from llama_index.llms.openai import OpenAI

# Load and index documents
documents = SimpleDirectoryReader("./docs").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()

# Create tool and agent
tool = QueryEngineTool.from_defaults(
    query_engine=query_engine,
    name="documentation",
    description="Search product documentation"
)

agent = ReActAgent.from_tools([tool], llm=OpenAI(model="gpt-4o"))
response = agent.chat("How do I configure authentication?")

Strengths

Weaknesses

When to Use LlamaIndex

Choose LlamaIndex when you need:

Microsoft AutoGen: Multi-Agent Conversations

Best for: Research applications, complex reasoning tasks, conversational agent teams

AutoGen takes a unique approach: agents as conversational participants. Multiple agents chat with each other to solve problems, with optional human participation. Explore the full architecture in our AutoGen Deep Dive.

Architecture Overview

AutoGen’s core concepts:

from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager

# Create specialized agents
researcher = AssistantAgent(
    name="Researcher",
    system_message="You research topics thoroughly.",
    llm_config={"model": "gpt-4o"}
)

critic = AssistantAgent(
    name="Critic",
    system_message="You critically evaluate research findings.",
    llm_config={"model": "gpt-4o"}
)

user = UserProxyAgent(
    name="User",
    human_input_mode="NEVER",
    code_execution_config={"work_dir": "workspace"}
)

# Create group chat
group_chat = GroupChat(agents=[user, researcher, critic], messages=[])
manager = GroupChatManager(groupchat=group_chat)

user.initiate_chat(manager, message="Research the impact of AI on healthcare")

Strengths

Weaknesses

When to Use AutoGen

Choose AutoGen when you need:

CrewAI: Role-Based Agent Teams

Best for: Business process automation, structured team-based tasks

CrewAI organizes agents into crews with defined roles, goals, and tasks. It emphasizes role-playing and delegation, making it intuitive for business workflows. For a comprehensive guide, see our CrewAI Deep Dive.

Architecture Overview

CrewAI’s model mirrors human team structures:

from crewai import Agent, Task, Crew, Process

# Define agents with roles
researcher = Agent(
    role="Senior Research Analyst",
    goal="Uncover cutting-edge developments in AI",
    backstory="You're a veteran analyst with deep expertise.",
    tools=[search_tool, scrape_tool]
)

writer = Agent(
    role="Content Strategist",
    goal="Create compelling content from research",
    backstory="You transform complex info into engaging narratives."
)

# Define tasks
research_task = Task(
    description="Research the latest AI agent frameworks",
    expected_output="Comprehensive research report",
    agent=researcher
)

writing_task = Task(
    description="Write a blog post based on the research",
    expected_output="Polished blog post ready for publication",
    agent=writer
)

# Create and run crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    process=Process.sequential
)

result = crew.kickoff()

Strengths

Weaknesses

When to Use CrewAI

Choose CrewAI when you need:

Semantic Kernel: Enterprise Microsoft Integration

Best for: Microsoft ecosystem integration, enterprise deployments, C#/.NET applications

Microsoft’s Semantic Kernel provides a lightweight SDK for integrating LLMs into applications, with first-class support for Azure services.

Architecture Overview

Semantic Kernel organizes around:

import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion

# Initialize kernel
kernel = sk.Kernel()
kernel.add_service(AzureChatCompletion(
    deployment_name="gpt-4",
    endpoint="https://your-endpoint.openai.azure.com",
    api_key="your-key"
))

# Add plugins
kernel.add_plugin(parent_directory="plugins", plugin_name="WriterPlugin")

# Create and execute plan
planner = ActionPlanner(kernel)
plan = await planner.create_plan("Write a poem about AI agents")
result = await plan.invoke(kernel)

Strengths

Weaknesses

When to Use Semantic Kernel

Choose Semantic Kernel when you need:

Emerging Frameworks to Watch

OpenAI Assistants API

OpenAI’s hosted solution offers managed state, file handling, and tool use without infrastructure concerns. Great for rapid development but with less control and portability.

Anthropic Claude Tool Use

Claude’s native tool use capabilities enable building agents directly with the API. Excellent for Anthropic-focused applications with simpler requirements.

Haystack

Deepset’s Haystack focuses on production RAG systems with extensive preprocessing pipelines. Strong alternative to LlamaIndex for document processing.

DSPy

Stanford’s DSPy takes a programmatic approach to prompt optimization. Promising for applications where prompt engineering is a bottleneck.

Choosing the Right Framework

Decision Framework

Ask yourself these questions:

  1. What’s your primary use case?

    • Data retrieval and Q&A → LlamaIndex
    • General agent development → LangChain
    • Complex workflows with state → LangGraph
    • Multi-agent collaboration → AutoGen or CrewAI
    • Microsoft/Azure integration → Semantic Kernel

  2. How complex is your workflow?

    • Simple chains → LangChain
    • Branching logic with cycles → LangGraph
    • Team-based tasks → CrewAI
    • Research conversations → AutoGen

  3. What’s your production timeline?

    • Need something fast → LlamaIndex or LangChain
    • Building for scale → LangGraph
    • Enterprise deployment → Semantic Kernel

  4. What’s your team’s expertise?

    • Python-focused → Any framework
    • C#/.NET shop → Semantic Kernel
    • New to agents → CrewAI (most intuitive)
    • Experienced developers → LangGraph (most control)

Framework Comparison Matrix

FrameworkLearning CurveProduction ReadyMulti-AgentRAG StrengthCommunity Size
LangChainMediumHighMediumGoodVery Large
LangGraphHighVery HighHighGoodLarge
LlamaIndexLowHighLowExcellentLarge
AutoGenMediumMediumExcellentMediumMedium
CrewAILowMediumHighMediumGrowing
Semantic KernelMediumHighLowMediumMedium

Combining Frameworks

These frameworks aren’t mutually exclusive. Common patterns include:

LlamaIndex + LangChain: Use LlamaIndex for data handling, wrap query engines as LangChain tools for broader orchestration.

from langchain.tools import Tool
from llama_index.core import VectorStoreIndex

# LlamaIndex for retrieval
index = VectorStoreIndex.from_documents(docs)
query_engine = index.as_query_engine()

# Wrap as LangChain tool
doc_tool = Tool(
    name="documentation",
    func=lambda q: str(query_engine.query(q)),
    description="Search documentation"
)

# Use in LangChain agent
agent = create_tool_calling_agent(llm, [doc_tool, web_search], prompt)

AutoGen + LangGraph: Use LangGraph for overall workflow control, AutoGen for specific multi-agent reasoning steps.

CrewAI + Custom Tools: CrewAI for orchestration with custom tools built using any underlying framework.

Getting Started Recommendations

For Beginners

Start with LlamaIndex if you have documents to query, or CrewAI if you want multi-agent systems. Both have gentle learning curves and produce results quickly.

For Production Applications

Invest in learning LangGraph. Its explicit state management and checkpointing are essential for production reliability. See our Building Production AI Agents guide for deployment best practices.

For Research and Experimentation

Try AutoGen for exploring multi-agent dynamics. Its conversational approach reveals interesting emergent behaviors.

For Enterprise

Evaluate Semantic Kernel if you’re in the Microsoft ecosystem, or LangGraph with LangSmith for observability if not.

Conclusion

The AI agent framework landscape offers solutions for every use case, from simple chatbots to complex autonomous systems. The key is matching your requirements to framework strengths:

Don’t agonize over the perfect choice. Start with the framework that matches your immediate needs, learn its patterns, and expand as requirements evolve. Most importantly, build something. The best framework is the one you understand well enough to ship production applications.

Ready to dive deeper? Check out our Framework Deep Dive series for in-depth tutorials on each framework, or start with our LangGraph tutorial for hands-on agent building.

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