Why use a framework at all?
You can build an agent in plain Python — and on Page 7 you will do exactly that. But frameworks give you pre-built tool integrations, memory management, observability, and agent orchestration so you are not reinventing the wheel for every project.
Rule of thumb: Build plain Python first so you understand what is happening. Then graduate to a framework when the boilerplate becomes a real burden.
LangChain
The most widely used LLM framework. LangChain provides "chains" (sequences of LLM calls and tools), pre-built tool integrations (100+ tools), memory modules, and a graph-based orchestration layer called LangGraph for stateful, multi-step agents.
Best for: Teams that want a large ecosystem of integrations, good documentation, and the flexibility to build anything from simple chains to complex agents.
Watch out for: Can be over-engineered for simple tasks. The abstraction layers can make debugging harder.
CrewAI
CrewAI is built around the concept of "crews" — teams of agents with defined roles, goals, and backstories. You define agents like "Senior Researcher" and "Technical Writer", assign them tasks, and CrewAI orchestrates how they collaborate.
Best for: Workflows that naturally map to human teams — content creation pipelines, research + writing, planning + execution.
Watch out for: The role-and-goal abstraction is great for clear tasks but can feel forced for fluid, exploratory work.
AutoGen (Microsoft)
AutoGen (by Microsoft Research) models multi-agent interactions as conversations between agents. Agents can be LLMs, humans, or code executors — any mix. It is excellent for scenarios where you want a human to be one of the participants in the loop.
Best for: Research, coding assistants with human oversight, complex back-and-forth workflows between agents.
Watch out for: The conversational model can be confusing to debug — tracking which agent said what across many turns.
Phidata (now Agno)
Phidata (recently rebranded to Agno) focuses on production-ready agents with built-in memory, storage, and a clean API. It ships with integrations for databases, web search, and file systems out of the box, and includes a UI for testing agents locally.
Best for: Developers who want a batteries-included, opinionated framework without stitching together many separate libraries.
OpenAI Swarm
OpenAI Swarm is a lightweight, experimental framework focused on one concept: handoffs. Agents can transfer control to other agents mid-task. It is intentionally minimal — more a reference implementation than a production framework.
Best for: Learning how multi-agent handoffs work. Not recommended for production use.
Framework comparison
| Framework | Best for | Learning curve |
|---|---|---|
| LangChain | General purpose, large ecosystem | Medium–High |
| CrewAI | Role-based multi-agent workflows | Low–Medium |
| AutoGen | Conversational agents, human-in-loop | Medium |
| Phidata / Agno | Production agents, batteries included | Low |
| OpenAI Swarm | Learning handoffs, prototyping | Very Low |
How to choose the right framework
Start by asking what the agent needs to do, not which framework is most popular. A single-agent workflow that calls two APIs does not need the orchestration layer of CrewAI or AutoGen. Conversely, a pipeline where multiple agents hand off work to each other will fight you in plain Python long before it works in LangGraph or CrewAI.
Single agent, simple tools
Use plain Python or Phidata. You control the loop, the prompt, and the error handling directly. No framework overhead, no abstraction surprises. Move to a framework when you find yourself rewriting memory, retry, or observability logic for the third time.
Single agent, many tools or complex state
LangChain shines here. Its tool registry, structured output parsing, and LangGraph's state machine give you a clean way to manage branching logic and checkpoints without hand-rolling a state graph.
Multi-agent team with defined roles
CrewAI is purpose-built for this. Define a Researcher, a Writer, and an Editor — each with its own prompt and tools — and let the framework handle task delegation and output passing between them.
Human-in-the-loop or conversational agents
AutoGen treats humans as first-class participants in the agent conversation. If your workflow requires human approval at checkpoints, code review steps, or interactive feedback loops, AutoGen's conversational model maps naturally to that pattern.
Production tip: Whichever framework you choose, make sure it supports observability (tracing, logging, step replay). Debugging agent failures in production without traces is like debugging a distributed system with only print statements.
Quick code comparison
Here is what a minimal agent setup looks like in three different approaches. Each creates an agent that can call a search tool — the same task, different levels of abstraction.
Plain Python (no framework)
response = client.chat.completions.create(
model="gpt-4o", messages=messages, tools=tools
)
while response.choices[0].message.tool_calls:
# execute tool, append result, call again
...Full control. You write the loop, the retry logic, and the memory management yourself.
LangChain + LangGraph
from langgraph.prebuilt import create_react_agent
agent = create_react_agent(model, tools=[search_tool])
result = agent.invoke({"messages": [("user", query)]})One function call. LangGraph handles the loop, state, and tool execution.
CrewAI
from crewai import Agent, Task, Crew
researcher = Agent(role="Researcher", tools=[search_tool])
task = Task(description=query, agent=researcher)
Crew(agents=[researcher], tasks=[task]).kickoff()Role-based. The agent has a persona, and the Crew orchestrates the workflow.
AI Agent Frameworks FAQ
What is an AI agent framework?
An AI agent framework is a library that provides pre-built tool integrations, memory management, observability, and agent orchestration so you do not have to build these from scratch for every project.
Which AI agent framework should I start with?
Start with plain Python to understand how agents work, then move to LangChain for its large ecosystem and documentation, or CrewAI if your workflow maps to role-based teams.
Is LangChain the best AI agent framework?
LangChain has the largest ecosystem and the most integrations, but it can be over-engineered for simple tasks. CrewAI, AutoGen, and Phidata are better choices for specific use cases like multi-agent teams or production-ready deployments.
What is the difference between CrewAI and AutoGen?
CrewAI organises agents into role-based teams with defined goals and backstories, making it ideal for content and research pipelines. AutoGen models agents as conversational participants and excels at human-in-the-loop workflows.
Can I build an AI agent without a framework?
Yes. A basic agent loop in plain Python is about 30 lines of code. Frameworks add convenience — pre-built tools, memory, orchestration — but are not required to build a working agent.
Continue learning
Ready to write code? The next page walks you through building your first AI agent in plain Python. If you want to understand the building blocks first, revisit core components of an AI agent. For a deep dive into LangChain specifically, check out the LangChain tutorial series.
Quick summary
- Start in plain Python — frameworks add convenience, not magic
- LangChain: biggest ecosystem, best for general-purpose agents
- CrewAI: role-based teams, great for content and research pipelines
- AutoGen: conversational multi-agent with human-in-the-loop support
- Phidata/Agno: batteries-included, production-friendly