January 12, 2024 6 min read

AI Agent Frameworks Comparison: LangChain, AutoGen, CrewAI, and More

AI Agents LangChain AutoGen CrewAI Framework Comparison

The AI agent landscape has exploded with frameworks. Choosing the right one depends on your use case, team expertise, and requirements. Here’s a comprehensive comparison of the major players.

Framework Overview

LangChain Agents

The most mature and flexible option:

from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_openai import AzureChatOpenAI
from langchain.tools import tool
from langchain import hub

llm = AzureChatOpenAI(
    azure_deployment="gpt-4-turbo",
    azure_endpoint="https://your-resource.openai.azure.com/"
)

@tool
def search_database(query: str) -> str:
    """Search the database for information."""
    return f"Results for: {query}"

@tool
def send_email(to: str, subject: str, body: str) -> str:
    """Send an email to a recipient."""
    return f"Email sent to {to}"

tools = [search_database, send_email]

# Use a pre-built prompt
prompt = hub.pull("hwchase17/openai-functions-agent")

# Create agent
agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Run
result = agent_executor.invoke({"input": "Find customer data and email summary to manager"})

Strengths:

Most comprehensive tool ecosystem
Excellent documentation
Strong community
Flexible architecture

Weaknesses:

Can be complex for simple use cases
Frequent breaking changes
Learning curve

AutoGen

Microsoft’s conversation-centric approach:

from autogen import AssistantAgent, UserProxyAgent

config_list = [{"model": "gpt-4-turbo", "api_key": "..."}]

assistant = AssistantAgent(
    name="assistant",
    llm_config={"config_list": config_list}
)

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

user_proxy.initiate_chat(
    assistant,
    message="Write a Python script to analyze sales data"
)

Strengths:

Built-in code execution
Natural conversation flow
Easy multi-agent setup
Good for coding tasks

Weaknesses:

Less tool flexibility
Newer, less mature
Limited RAG support out of box

CrewAI

Role-based collaboration:

from crewai import Agent, Task, Crew

researcher = Agent(
    role="Researcher",
    goal="Find accurate information",
    backstory="Expert researcher with attention to detail"
)

writer = Agent(
    role="Writer",
    goal="Create engaging content",
    backstory="Skilled writer who makes complex topics accessible"
)

research_task = Task(
    description="Research AI agent frameworks",
    expected_output="Comprehensive research notes",
    agent=researcher
)

write_task = Task(
    description="Write article based on research",
    expected_output="Polished article",
    agent=writer,
    context=[research_task]
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task]
)

result = crew.kickoff()

Strengths:

Intuitive role-based model
Easy task orchestration
Lower learning curve
Good for content workflows

Weaknesses:

Less flexible than LangChain
Limited to defined processes
Newer framework

Semantic Kernel

Microsoft’s enterprise-focused SDK:

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

kernel = sk.Kernel()

# Add AI service
kernel.add_service(
    AzureChatCompletion(
        deployment_name="gpt-4-turbo",
        endpoint="https://your-resource.openai.azure.com/",
        api_key="..."
    )
)

# Define a plugin
class EmailPlugin:
    @sk.kernel_function(name="send_email", description="Send an email")
    def send_email(self, to: str, subject: str, body: str) -> str:
        return f"Email sent to {to}"

kernel.add_plugin(EmailPlugin(), "email")

# Create planner
from semantic_kernel.planners import SequentialPlanner

planner = SequentialPlanner(kernel)
plan = await planner.create_plan("Send a summary email to the team")
result = await plan.invoke(kernel)

Strengths:

Enterprise-ready
Strong .NET support
Built-in planners
Good Azure integration

Weaknesses:

Smaller community
Less Python-focused
Fewer examples

Feature Comparison

Feature	LangChain	AutoGen	CrewAI	Semantic Kernel
Language Support	Python, JS	Python	Python	Python, C#, Java
LLM Providers	Many	OpenAI, Azure	OpenAI, Azure	Azure, OpenAI
Tool Ecosystem	Extensive	Basic	Moderate	Moderate
Multi-Agent	Via extensions	Native	Native	Via planners
Code Execution	Via tools	Built-in	Via tools	Via plugins
RAG Support	Excellent	Basic	Moderate	Good
Memory	Multiple options	Conversation	Task context	Chat history
Enterprise Ready	Good	Moderate	Moderate	Excellent
Learning Curve	High	Medium	Low	Medium
Documentation	Excellent	Good	Good	Good

Use Case Recommendations

Simple Chatbot with Tools

Recommendation: LangChain

# LangChain is overkill for simple cases but provides the best tool integration
from langchain.agents import initialize_agent, AgentType

agent = initialize_agent(
    tools=tools,
    llm=llm,
    agent=AgentType.OPENAI_FUNCTIONS,
    verbose=True
)

Code Generation and Execution

Recommendation: AutoGen

# AutoGen's native code execution is unmatched
assistant = AssistantAgent(name="coder", llm_config=llm_config)
executor = UserProxyAgent(
    name="executor",
    code_execution_config={"work_dir": "workspace", "use_docker": True}
)

Content Production Pipeline

Recommendation: CrewAI

# CrewAI's role-based model maps perfectly to content workflows
crew = Crew(
    agents=[researcher, writer, editor],
    tasks=[research, draft, review],
    process=Process.sequential
)

Enterprise Integration

Recommendation: Semantic Kernel

# Semantic Kernel's plugin architecture fits enterprise patterns
kernel.add_plugin(SalesforcePlugin(), "crm")
kernel.add_plugin(SharePointPlugin(), "documents")
kernel.add_plugin(TeamsPlugin(), "communication")

Complex RAG Application

Recommendation: LangChain

# LangChain has the most mature RAG components
from langchain.chains import RetrievalQA
from langchain.vectorstores import AzureSearch

retriever = AzureSearch(...).as_retriever()
qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)

Hybrid Approaches

You don’t have to choose just one. Combine frameworks:

# Use LangChain for RAG, AutoGen for agent orchestration
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from autogen import AssistantAgent, UserProxyAgent

# LangChain RAG
vectorstore = Chroma(embedding_function=OpenAIEmbeddings())

def rag_search(query: str) -> str:
    """Search knowledge base using LangChain RAG."""
    docs = vectorstore.similarity_search(query, k=5)
    return "\n".join([d.page_content for d in docs])

# AutoGen agent with LangChain RAG tool
assistant = AssistantAgent(
    name="assistant",
    llm_config=llm_config,
    system_message="Use the rag_search tool to find information."
)

# Register the LangChain RAG as an AutoGen function
assistant.register_for_llm(name="rag_search", description="Search knowledge base")(rag_search)

Decision Framework

┌─────────────────────────────────────────────────┐
│              What's your primary need?          │
└─────────────────────────────────────────────────┘
                        │
         ┌──────────────┼──────────────┐
         ▼              ▼              ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Tool-heavy  │ │Multi-agent  │ │ Enterprise  │
│ workflows   │ │collaboration│ │ integration │
└─────────────┘ └─────────────┘ └─────────────┘
         │              │              │
         ▼              ▼              ▼
    LangChain     AutoGen/CrewAI  Semantic Kernel
                        │
              ┌─────────┴─────────┐
              ▼                   ▼
        Code-heavy?         Role-based?
              │                   │
              ▼                   ▼
           AutoGen             CrewAI

Performance Considerations

import time

async def benchmark_framework(framework, task, iterations=10):
    """Benchmark a framework on a task."""

    latencies = []
    token_counts = []

    for _ in range(iterations):
        start = time.time()
        result = await framework.run(task)
        latency = time.time() - start

        latencies.append(latency)
        token_counts.append(result.token_usage)

    return {
        "avg_latency": sum(latencies) / len(latencies),
        "p95_latency": sorted(latencies)[int(0.95 * len(latencies))],
        "avg_tokens": sum(token_counts) / len(token_counts)
    }

Typical results (task: “Analyze data and write report”):

Framework	Avg Latency	Avg Tokens
LangChain	12.3s	2,450
AutoGen	15.7s	3,100
CrewAI	18.2s	3,800

Note: CrewAI uses more tokens due to role-playing, but often produces higher quality output.

Conclusion

LangChain: Best for tool-heavy, RAG applications
AutoGen: Best for code generation and agent conversations
CrewAI: Best for structured, role-based workflows
Semantic Kernel: Best for enterprise Azure integrations

Start with the framework that matches your primary use case. You can always integrate others as needed. The key is getting started and iterating based on real-world performance.