Choosing Your AI Agent Framework: LangGraph vs. LlamaIndex vs. CrewAI vs. AutoGen

In our previous posts, we built agents from scratch. We wrote Python functions for tools (see our ReAct agents guide), loops for self-correction (see our self-correcting RAG systems), and prompts for routing. This was a great way to learn, but in a production environment, we'd be re-inventing the wheel.

This post is for you if you're ready to build a serious agent and are staring at a wall of new frameworks: LangGraph, LlamaIndex, CrewAI, AutoGen. They all claim to build "agents." They all look powerful. And they all sound the same.

How do you choose?

This is the core "analysis paralysis" problem for modern AI engineers. Today, we'll demystify these four frameworks, not by their features, but by their core philosophy and the engineering problems they are built to solve.

The core concept: Single-Agent vs. Multi-Agent

The most critical distinction is this: are you building one smart agent or a team of collaborating agents?

Single-Agent (Tool-Using): One "brain" (LLM) that can use many tools. It's a "generalist" that can pick up a calculator, then a web search, then a RAG tool.

Multi-Agent (Delegation): A team of "specialist" agents (e.g., a "Researcher", a "Writer", a "Coder") that collaborate and talk to each other to solve a complex problem.

Here is what a Single-Agent System looks like. Notice it's one central brain choosing from a list of tools.

graph TD
subgraph SINGLE["Single-Agent System"]
direction LR
A[User Query] --> B[One Agent Brain LLM]
B --> C[Tool 1 RAG]
B --> D[Tool 2 Web Search]
B --> E[Tool 3 Calculator]
C --> B
D --> B
E --> B
B --> F[Final Answer]
end

style B fill:#e3f2fd,stroke:#0d47a1

Now, here is a Multi-Agent System. Notice the "Manager" agent that delegates tasks to a team of specialized agents.

graph TD
G[User Query] --> H[Manager Agent]
H --> I[Agent A Researcher]
I --> H
H --> J[Agent B Writer]
J --> H
H --> K[Agent C Critic]
K --> H
H --> L[Final Answer]

style H fill:#e8f5e9,stroke:#388e3c

This single distinction will guide 90% of your decision.

1. LlamaIndex: the "Data-First" RAG engine

LlamaIndex is a data framework first and an agent framework second. Its core philosophy is: "Your data is the most important part, and RAG is the key."

Core Concept: Single-Agent (Tool-Using).

Best-in-Class Feature: Data ingestion and retrieval. It has the most advanced, high-performance RAG pipelines (chunking, indexing, query engines) out of the box.

Developer Experience (DX): "Configure-it." For RAG, the DX is phenomenal. You're not building a state machine; you're configuring a high-performance engine.

When to use it: Your problem is data-centric. You need a world-class Q&A bot for your PDFs, Notion, or database.

Use Case: "I have 500 company documents. I need a bot that can answer complex questions like, 'Compare our Q3 sales strategy to our Q4 results.'"

# LlamaIndex is clean and RAG-focused
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.query_engine import SubQuestionQueryEngine
from llama_index.core.tools import QueryEngineTool

# 1. Load, chunk, embed, and index ALL data in a folder
# This one line handles the entire data pipeline.
documents = SimpleDirectoryReader("./data_folder").load_data()
index = VectorStoreIndex.from_documents(documents)

# 2. LlamaIndex provides pre-built, advanced engines
# This engine automatically breaks one question into many
adv_engine = SubQuestionQueryEngine.from_defaults(
query_engine_tools=[
QueryEngineTool.from_defaults(
query_engine=index.as_query_engine(),
description="Useful for all company data"
)
]
)

response = adv_engine.query("Compare our Q3 sales to our Q4 results.")

Observation: We didn't build a Chain-of-Thought prompt. We didn't build a router. We used LlamaIndex's pre-built SubQuestionQueryEngine, and it automatically handled the logic. This is its power.

2. LangGraph: the "Build-it-Yourself" state machine

LangGraph (the new heart of LangChain) is a logic framework. Its core philosophy is: "Your agent's logic is the most important part." It gives you total control over loops, branches, and state.

Core Concept: Single-Agent (Tool-Using).

Best-in-Class Feature: Building stateful, cyclic (looping) agents.

Developer Experience (DX): "Build-it-yourself." It's powerful, but you are responsible for everything. You are defining a state machine, which is a familiar engineering concept.

When to use it: Your problem is logic-centric. You need a single agent to follow a complex, conditional workflow.

Use Case: "I need a bot to read an email. If it's urgent, check my calendar API. If I'm free, draft a 'yes' reply. If I'm busy, draft a 'no' reply. If it's not urgent, check my RAG database for context and draft a 'later' reply."

This requires a graph with conditional logic, which LangGraph is built for.

graph TD
A[Start Read Email] --> B{Check Urgency}
B -->|Urgent| C{Check Calendar}
B -->|Not Urgent| D[Retrieve RAG Context]
C -->|Free| E[Draft Yes Reply]
C -->|Busy| F[Draft No Reply]
D --> G[Draft Later Reply]
E --> H[End]
F --> H
G --> H

# LangGraph is all about defining nodes and edges (logic)
from langgraph.graph import StateGraph, END

# 1. Define the "memory" (state)
class MyAgentState(TypedDict):
email_content: str
is_urgent: bool
# ... more state variables ...

# 2. Define nodes as Python functions
def check_urgency(state):
# ... (LLM call to grade urgency)
if is_urgent:
return {"is_urgent": True}
return {"is_urgent": False}

def check_calendar(state):
# ... (Call Calendar API tool)
return {"calendar_is_free": False}

# ... (other nodes for retrieving, drafting, etc.) ...

# 3. Define the graph (the agent's "brain")
workflow = StateGraph(MyAgentState)
workflow.add_node("check_urgency", check_urgency)
workflow.add_node("check_calendar", check_calendar)
# ...
workflow.set_entry_point("check_urgency")

# 4. Define the *conditional logic*
workflow.add_conditional_edges(
"check_urgency",
lambda state: "check_calendar" if state["is_urgent"] else "retrieve_context"
)
# ... (more edges) ...

app = workflow.compile()

Observation: This is complex, but it's explicit. LangGraph is the clear choice when your agent's logic involves loops and many if/else branches.

3. CrewAI: the "Collaborative Team" framework

CrewAI is a multi-agent framework. Its philosophy is: "Agents are your employees." You don't build a single complex "brain"; you build a team of simple, specialized agents and give them a process to follow.

Core Concept: Multi-Agent (Delegation).

Best-in-Class Feature: Role-based collaboration. It's incredibly intuitive to set up a "crew" of agents.

Developer Experience (DX): "Manage-it." The DX is fantastic. You write "job descriptions" (roles) and "to-do lists" (tasks) in plain English.

When to use it: Your problem is task-centric and can be broken down into a sequence of specialized roles.

Use Case: "I need to write a blog post about a new AI trend. I need one agent to research the trend, a second to write a draft, and a third to edit it for style."

from crewai import Agent, Task, Crew, Process

# 1. Define your "employees" (Agents)
researcher = Agent(
role="Senior AI Researcher",
goal="Find the latest trends in Generative AI",
backstory="You are an expert at web search and analysis.",
tools=[DuckDuckGoSearchRun()]
)

writer = Agent(
role="Tech Content Writer",
goal="Write an engaging 500-word blog post on the trend",
backstory="You write in a clear, witty, and accessible style."
)

# 2. Define their "to-do list" (Tasks)
research_task = Task(
description="Find the top 3 emerging trends in GenAI for 2025",
agent=researcher
)

write_task = Task(
description="Write a 500-word blog post on the trends found. Make it engaging.",
agent=writer
)

# 3. Form the "Crew" and define the process
crew = Crew(
agents=[researcher, writer],
tasks=[research_task, write_task],
process=Process.sequential # Run Task 1, then Task 2
)

result = crew.kickoff()

Observation: The power here is abstraction. We didn't build a state machine. We defined roles and a process. CrewAI handles the "collaboration" (passing the research doc to the writer) automatically.

4. AutoGen: the "Conversational" research framework

AutoGen (from Microsoft) is also a multi-agent framework, but its philosophy is different: "Agents are in a chat room." It's less about a fixed process (like CrewAI) and more about a dynamic conversation between agents.

Core Concept: Multi-Agent (Conversation).

Best-in-Class Feature: Flexible, conversational agent interactions. It's built for research and complex, back-and-forth problem-solving.

Developer Experience (DX): "Research-it." The DX is more complex and code-heavy than CrewAI. You are defining how agents talk to each other and react to messages.

When to use it: Your problem is exploratory. You don't know the exact steps. You want agents to chat, debate, and even ask you (the human) for help.

Use Case: "I need to write and execute Python code to solve a complex data problem. I want a 'Coder' agent to write the code, an 'Executor' agent to run it, and a 'Human' (me) to approve the code before it runs."

import autogen

# 1. Define the participants in the "chat room"
coder = autogen.AssistantAgent(name="Coder", ...)

executor = autogen.UserProxyAgent(
name="Executor",
human_input_mode="NEVER",
code_execution_config={"work_dir": "coding"}, # Can run code
...
)

human_proxy = autogen.UserProxyAgent(
name="Human_in_the_loop",
human_input_mode="ALWAYS", # Will always ask me for input
...
)

# 2. Define the chat
groupchat = autogen.GroupChat(agents=[coder, executor, human_proxy], messages=[])
manager = autogen.GroupChatManager(groupchat=groupchat, ...)

# 3. Start the chat
coder.initiate_chat(
manager,
message="Write a python script to analyze 'data.csv' and find the average sales."
)

Observation: AutoGen's power is its conversational flexibility. The Human_in_the_loop agent is a first-class participant. The agents will "chat" with each other, with the coder suggesting code, the executor running it, and the human proxy (you) giving feedback.

The Engineer's Choice: Head-to-Head

Framework | Core Concept | Primary Use Case | Developer Experience
LlamaIndex | Single-Agent (Data-First) | High-Performance RAG (Q&A over docs) | "Configure-it." Clean, high-level abstractions for data.
LangGraph | Single-Agent (Logic-First) | Complex, Stateful Agents (Loops, branches, tool-use) | "Build-it." Low-level control over a state machine.
CrewAI | Multi-Agent (Delegation) | Role-Based Collaboration (e.g., Researcher → Writer) | "Manage-it." High-level, intuitive, and task-oriented.
AutoGen | Multi-Agent (Conversation) | Dynamic, Conversational Agents (Research, coding, human-in-loop) | "Research-it." Flexible but complex. Great for experimentation.

The Pro Solution: Hybrid Agents

As an engineer, your final realization should be that these frameworks are not mutually exclusive. They are components.

The "pro" move is to use them together.

You can have a LangGraph agent (for complex logic).

One of its "tools" can be a LlamaIndex query engine (for expert RAG).

Another "tool" can be an entire CrewAI "crew" (to handle a complex sub-task like "write a blog post").

This gives you the best of all worlds: LangGraph's total control over logic, LlamaIndex's high-performance data retrieval, and CrewAI's collaborative workflows.

graph TD
A[User Query] --> B[LangGraph Brain State Machine]

B --> C[Tool 1 Web Search]
B --> D[LlamaIndex Engine]
D --> D1[PDFs]
D --> D2[Notion]
B --> E[CrewAI Crew]
E --> E1[Agent A]
E --> E2[Agent B]

C --> B
D --> B
E --> B
B --> F[Final Answer]

style B fill:#e3f2fd,stroke:#0d47a1
style D fill:#e8f5e9,stroke:#388e3c
style E fill:#e0f2f1,stroke:#00695c

The "How" (in pseudo-code):

# 1. Build your high-performance RAG engine in LlamaIndex
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

doc_index = VectorStoreIndex.from_documents(
SimpleDirectoryReader("./data").load_data()
)

rag_engine = doc_index.as_query_engine(
description="Use this for any questions about 'Project Nova' or internal company documents."
)

# 2. Create a LlamaIndex "Tool"
from llama_index.core.tools import QueryEngineTool
llama_tool = QueryEngineTool.from_defaults(rag_engine)

# 3. Build your LangGraph agent
from langchain_community.tools import DuckDuckGoSearchRun

web_tool = DuckDuckGoSearchRun(description="Use this for public web searches.")
tools = [llama_tool, web_tool]

# ... (build your LangGraph agent as shown earlier) ...

# 4. Run your Hybrid Agent
# The agent's "Router" will now correctly choose
# the LlamaIndex tool for internal questions and
# the web tool for public questions.

Challenge for you

Use Case: You need to build an "Email Assistant" that automatically processes your inbox.

The Goal: The agent should read an email, check your personal calendar (a local .ics file) for conflicts, and then draft a reply.

Your Task: Which framework(s) would you choose?

Which framework would you use for the main agent brain to handle the if/else logic?

Which framework would you use to create the "calendar search" tool?

How would you combine them?

Key takeaways

Single-agent vs. multi-agent is the key distinction: Single-agent systems use one brain with many tools; multi-agent systems use teams of specialists

LlamaIndex excels at data-first problems: Use it when RAG and data retrieval are your primary concerns. Start with LlamaIndex if your problem is: "I need to build a high-quality chatbot over my data."

LangGraph excels at logic-first problems: Use it when you need complex state machines, loops, and conditional workflows. Start with LangGraph if your problem is: "I need to build an agent that can make complex decisions, loop, and use tools."

CrewAI excels at role-based collaboration: Use it when your problem can be broken into specialized tasks for a team. Start with CrewAI if your problem is: "I need to automate a multi-step process that can be done by a team of specialists."

AutoGen excels at exploratory, conversational problems: Use it when you need flexible agent interactions and human-in-the-loop workflows. Start with AutoGen if your problem is: "I need to explore a complex problem with a team of agents that can write and run code."

Hybrid approaches combine the best of all worlds: Use LangGraph for orchestration, LlamaIndex for RAG, and CrewAI for complex sub-tasks