When should I use LangGraph instead of LangChain?

LangGraph is preferred for multi-agent, stateful workflows with conditional logic. LangChain suits linear chains and standalone prompt pipelines.

How can I persist memory in these agent systems?

You can use memory backends like Redis, Pinecone, or ChromaDB to persist interaction context, retrieved documents, and embedding vectors.

Introduction: Deploying AI Agents at Scale

Q: Is CrewAI open-source?

Yes. CrewAI is fully open-source under the Apache license and available on GitHub for use in both academic and commercial projects.

Introduction: Deploying AI Agents at Scale

AI agent frameworks like CrewAI and LangGraph are enabling developers to deploy intelligent, multi-agent systems at scale. As organizations look to automate complex workflows, from research to customer service, deploying AI agents efficiently has become a top priority. This article explores how to combine CrewAI and LangGraph to build, compose, and deploy autonomous agents in production environments.

Why AI agent orchestration matters

Orchestrating multiple large language models (LLMs) through task specialization boosts performance and enables sophisticated capabilities like delegation, memory, and error correction. CrewAI and LangGraph simplify this via structured APIs and graph-based workflows.

Use cases for scaled AI deployments

Automated research and report generation
24/7 intelligent customer agents
Knowledge graph enrichment
Workflow automation in SaaS tools

What is CrewAI and How Does it Work?

Overview of CrewAI architecture

CrewAI is a Python framework for designing AI agents with clearly defined roles who collaborate on goals. Each crew member is backed by an LLM and can autonomously interact with tools, external memory, and users.

Agent roles, memory, and tools

You can define agents like Researcher, Analyst, and Presenter, each with custom prompts, tools (e.g., browser, calculator APIs), and shared memory (e.g., using Redis or Pinecone).

Running your first CrewAI crew

Crews can be defined as YAML or Python objects. For example:

from crewai import Crew, Agent
crew = Crew(agents=[researcher, writer], goal="Write market whitepaper")
crew.kickoff("Generate report on 5G trends")

Introduction to LangGraph for Agent Workflows

How LangGraph models agent interactions

LangGraph lets developers define graph-like workflows that manage agent handoffs, memory flow, and decision logic — ideal for multi-step tasks involving several agents or tools.

Creating conditional logic with graph edges

You can define edges with custom routing logic to choose the next agent based on outputs. This makes LangGraph suitable for complex logic like retries, branching behavior, and multi-user inputs.

LangGraph vs LangChain: What’s different?

While LangChain builds linear chains of prompts and calls, LangGraph introduces conditional routing and persistent state via Nodes and Edges. It’s better suited for multi-agent, multi-turn workflows that require dynamic control flow.

Step-by-Step Tutorial: Deploying Agents with CrewAI + LangGraph

Step 1: Set up your environment

Install the required packages:

pip install crewai langgraph openai

Step 2: Define Agent roles in CrewAI

Create Python classes or instances for each role:

from crewai import Agent
reporter = Agent(role="reporter", tools=[web_search], memory=True)

Step 3: Build a LangGraph workflow

Define a graph with nodes (functions or agents) and edges (transitions):

from langgraph.graph import StateGraph
workflow = StateGraph()
workflow.add_node("Plan", planner_fn)
workflow.add_conditional_edges("Plan", routing_logic)