Can I combine CrewAI and AutoGen in one project?

Yes, you can use CrewAI for high-level task orchestration and AutoGen for agent dialog execution.

How do I deploy my AI agent pipeline to the cloud?

Use Docker containers or serverless architectures like AWS Lambda, with API key management and structured logging.

Introduction: Deploying AI Agent Workflows with AutoGen and CrewAI

Q: What LLMs are compatible with AutoGen and CrewAI?

They support OpenAI, Anthropic, and any model integrated via LangChain, including local LLMs.

Introduction: Deploying AI Agent Workflows with AutoGen and CrewAI

As adoption of large language models (LLMs) grows, so does interest in orchestrating multiple agents to perform complex, autonomous workflows. Frameworks like AutoGen (by Microsoft) and CrewAI simplify how developers build and deploy these agent ecosystems. This tutorial walks you through how to deploy AI agent workflows using both AutoGen and CrewAI in production-ready formats.

What are AI agent frameworks?

AI agent frameworks are tools that manage autonomous LLM-based agents designed to complete specific tasks. These agents can collaborate with other agents or humans, use memory, and invoke tools like databases or APIs.

Why AutoGen and CrewAI are gaining traction

AutoGen offers structured LLM conversations and multi-agent flows with flexibility; CrewAI introduces human-readable agent definitions and task assignment graphs. Together they support roles, memory, and orchestration—ideal for building autonomous research assistants, code reviewers, or multi-agent task chains.

Prerequisites and Tooling Setup

System requirements and Python environment

You’ll need:

Python 3.10+
Virtualenv or Conda
OpenAI API key (or alternative LLM provider)

Installing AutoGen and CrewAI via pip

pip install pyautogen crewai

Key dependencies

CrewAI may require langchain, chromadb (optional), and pydantic. For memory and message persistence, Redis can be integrated. Add dependencies as needed:

pip install langchain redis openai chromadb

Building a Multi-Agent Workflow with AutoGen

Defining agents and conversation flows

AutoGen lets you define agents using a declarative interface. For example, you can create a code assistant and a code reviewer that interact:

from autogen import AssistantAgent, UserProxyAgent

reviewer = AssistantAgent(name="CodeReviewer", system_message="Review code for bugs.")
developer = AssistantAgent(name="CodeDeveloper", system_message="Write Python scripts.")

Incorporating tools, memory, and human input

Use UserProxyAgent to bring human feedback into the loop. You can also add tool invocation capabilities like Python execution or DB access via tool wrappers.

Structuring an AutoGen controller script

Create a main dialog runner to manage task flow:

group_chat = GroupChat(agents=[reviewer, developer], messages=[], max_round=5)
chat_manager = GroupChatManager(groupchat=group_chat, name="ChatManager")
chat_manager.initiate_chat()

Coordinating Agent Roles and Tasks with CrewAI

Creating roles and task graphs

CrewAI architecture is based on roles, tasks, tools, and memory. Here’s an example role with a defined output:

from crewai import Crew, Agent, Task

summarizer = Agent(role="summarizer", goal="Summarize reports", backstory="Loves distilling information")
summary_task = Task(description="Summarize technical document", expected_output="1-paragraph summary")

Assembling a crew using memory and tools

Combine agents, memory modules, and tools to build a collaborative workflow:

crew = Crew(agents=[summarizer], tasks=[summary_task])
crew.kickoff()

Running and debugging the crew pipeline

CrewAI workflows print intermediate outputs to console. For observability, you can log agent actions or errors using logging or integrate with log aggregation tools (e.g., DataDog).

Deploying Your AI Agent Workflow

Option 1: Local Docker deployment

Build a Dockerfile to package your agent logic:

# Dockerfile
FROM python:3.10
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["python", "main.py"]

Option 2: Cloud functions (AWS Lambda, Azure Functions)

Deploy your code via serverless backend for scalability:

For AWS Lambda, use a handler function for the agent pipeline
Environment variables should store API keys securely

Logging, observability, and scaling tips

Enable structured logging and use frameworks like FastAPI to expose APIs for inference or status. Use autoscaling GPUs or managed endpoints (via Azure OpenAI or AWS SageMaker) where needed.