Introduction
Single-agent LLM systems are limited by perspective, task scope, and memory. Real world problems require role-based multi-agent collaboration.
In this article, you will learn how to:
- Orchestrate multi-agent systems using LangGraph
- Delegate tasks across CrewAI agents
- Pass memory, output, and control between agents
- Design workflows that are fault-tolerant and observable
Why Multi-Agent Design Matters
No single agent can:
- Research, decide, write, validate, and deploy
- Maintain context across roles
- Route decisions or escalate failures
Multi-agent systems solve this by using separation of concern and role-specific expertise. Architecturally, this is similar to service-oriented design.
LangGraph as the Execution Backbone
LangGraph provides deterministic, memory-aware graphs that connect agents into stateful workflows. Each agent is modeled as a node, and state is passed through edges.
Minimum Required Version
Use LangGraph v0.0.20 or newer to ensure full support for memory propagation and multi-agent graphs.
Reference: LangGraph Concepts
Agent Roles in a Coordinated System
Let us define three distinct agents:
- Planner Agent — receives the task and breaks it into steps
- Researcher Agent — runs tools like web search and document summarizers
- Writer Agent — composes summaries or reports using LLMs
Each will live in a CrewAI definition but execute within a LangGraph context.
Role-Based Multi-Agent Workflow
All agents share a memory backend and communicate via LangGraph edges.
Example: LangGraph Multi-Agent Orchestration
from langgraph.graph import StateGraph
from agents import planner, researcher, writer
graph = StateGraph()
graph.add_node("plan", planner)
graph.add_node("research", researcher)
graph.add_node("write", writer)
graph.add_edge("plan", "research")
graph.add_edge("research", "write")
compiled = graph.compile()
compiled.invoke({"task": "Write a technical report on LLM security."})
CrewAI for Agent Role Management
CrewAI structures agents into collaborative crews with:
- Role definitions
- Shared goals
- Sequential or conditional task flow
- Integrated memory tools (LangMem compatible)
CrewAI Quick Example
from crewai import Crew, Agent, Task
planner = Agent(name="Planner", role="Decompose tasks")
researcher = Agent(name="Researcher", role="Find source material")
writer = Agent(name="Writer", role="Draft final content")
task1 = Task(agent=planner, description="Break down the user's request")
task2 = Task(agent=researcher, description="Research key topics", depends_on=[task1])
task3 = Task(agent=writer, description="Write final content", depends_on=[task2])
crew = Crew(agents=[planner, researcher, writer], tasks=[task1, task2, task3])
crew.kickoff()
Documentation: https://docs.crewai.com
Comparison Table: LangGraph vs CrewAI
| Feature | LangGraph | CrewAI |
|---|---|---|
| Execution model | State graph with explicit edges | Task and role-based flows |
| Tool integration | Manual; pluggable nodes | Built-in via tool registry |
| Role specialization | Requires manual node configuration | Declarative agent definition |
| Memory sharing | Full support via graph state | Supported via agent memory layers |
| Ideal for | Complex orchestrations | Structured team workflows |
Handoff Design Patterns
Multi-agent systems need reliable handoff mechanisms. LangGraph supports:
- Edge-based handoffs
- Memory context carry-over
- Result chaining between node outputs
CrewAI supports:
- Task dependency graphs
- Inter-agent variable passing
- Memory recall between agent runs
Example: Controlled Handoff in LangGraph
def planner(state):
return {"step": "Research LLM attacks"}
def researcher(state):
return {"findings": "Prompt injection, data poisoning, jailbreaking"}
def writer(state):
return f"Security risks identified: {state['findings']}"
Engineering Considerations
Before deploying multi-agent systems, evaluate:
- How will you monitor each agent’s performance?
- Will agents operate sequentially or in parallel?
- Are your memory systems tenant-aware?
- What happens when one agent fails or produces invalid output?
- How do you log, audit, and replay agent decisions?
External References
- LangGraph Multi-Agent Guide: https://langchain-ai.github.io/langgraph/concepts/multi_agent/
- CrewAI Quickstart: https://docs.crewai.com/quickstart
- LangMem Tools for Shared Memory: https://github.com/langchain-ai/langmem
- LangChain Memory Architecture: https://python.langchain.com/docs/modules/memory/
Conclusion
LangGraph and CrewAI allow engineers to move from tool-calling agents to collaborative, memory-aware teams. When you model agents as planners, researchers, validators, or writers, you can orchestrate pipelines that scale.
In the next article, we will cover infrastructure hardening, including:
- Retry logic
- Human-in-the-loop checkpoints
- Role-based access control
- Cost management
- Observability strategies