Google ADK vs LangGraph vs CrewAI

The three leading AI agent frameworks in April 2026 each take a fundamentally different approach to multi-agent orchestration. Google ADK uses hierarchical agent trees, LangGraph uses directed graphs with conditional edges, and CrewAI uses role-based crews with process types. Your choice depends on your team, your cloud provider, and how complex your agents need to be.

Last verified: April 2026

Quick Comparison

Feature	Google ADK	LangGraph	CrewAI
Developer	Google	LangChain	CrewAI Inc.
Languages	Python, TS, Go, Java	Python, JavaScript	Python
Agent model	Hierarchical tree	Directed graph	Role-based crews
A2A protocol	Native	No	No
MCP support	Yes	Yes	Yes
Cloud integration	Vertex AI	LangSmith	CrewAI Enterprise
Learning curve	Medium	High	Low
GitHub stars	~15K	~12K	~25K
License	Apache 2.0	MIT	MIT
Best for	Enterprise, Google Cloud	Complex workflows	Quick prototyping

Architecture Deep Dive

Google ADK: Hierarchical Agent Trees

ADK structures agents in parent-child hierarchies. A root agent delegates to specialized sub-agents, each with their own tools and instructions. Sub-agents can have their own sub-agents, creating a tree.

from google.adk import Agent

root = Agent(
    name="orchestrator",
    model="gemini-3.1-pro",
    sub_agents=[research_agent, coding_agent, review_agent]
)

Best for: Clear delegation patterns, enterprise org structures.

LangGraph: Directed Graphs

LangGraph models agent workflows as directed graphs with conditional edges. Nodes are functions, edges define flow based on state. This gives maximum flexibility but requires more upfront design.

from langgraph.graph import StateGraph

graph = StateGraph(AgentState)
graph.add_node("research", research_node)
graph.add_node("write", write_node)
graph.add_conditional_edges("research", should_continue)

Best for: Complex branching logic, conditional workflows, retry patterns.

CrewAI: Role-Based Crews

CrewAI assigns roles to agents (Researcher, Writer, Reviewer) and processes them sequentially or in parallel. The abstraction is intuitive and requires minimal code.

from crewai import Agent, Crew, Task

researcher = Agent(role="Researcher", goal="Find data")
writer = Agent(role="Writer", goal="Create content")
crew = Crew(agents=[researcher, writer], tasks=[...])

Best for: Quick prototyping, teams new to multi-agent systems.

Language Support

This is where Google ADK pulls ahead:

Language	Google ADK	LangGraph	CrewAI
Python	✅	✅	✅
TypeScript	✅	✅	❌
Go	✅	❌	❌
Java	✅	❌	❌

For backend teams using Go or Java, ADK is the only real option.

Production Deployment

Google ADK

Deploy to Vertex AI with managed infrastructure, auto-scaling, and monitoring. Tight integration with Google Cloud IAM, logging, and billing.

LangGraph

Deploy via LangSmith for observability, or self-host with LangGraph Platform. Good integration with most cloud providers.

CrewAI

CrewAI Enterprise offers managed hosting. Self-hosting is straightforward with Docker. Less mature production tooling than the other two.

Cost Considerations

All three frameworks are open-source and free. Your costs come from:

1. LLM API calls — The biggest expense. Gemini 3.1 Pro ($2/$12 per 1M tokens) is cheapest. Claude and GPT cost 2-5x more.
2. Cloud infrastructure — Vertex AI, LangSmith, or self-hosted servers.
3. Enterprise features — LangSmith Plus, CrewAI Enterprise, Vertex AI pricing.

If you’re optimizing for cost, Google ADK + Gemini 3.1 Pro is the cheapest full-stack option.

When to Choose Each

Google ADK: Your team uses Google Cloud, needs Go/Java support, or wants native A2A protocol for distributed agent systems.

LangGraph: You need complex conditional workflows, have experienced Python developers, and want the most flexible orchestration model.

CrewAI: You’re building your first multi-agent system, want to prototype fast, or prefer the simplest possible API.

The Bottom Line

In April 2026, LangGraph is the most battle-tested choice for complex production agent systems. CrewAI is the fastest to learn and has the largest community. Google ADK is the best enterprise option, especially for teams that need multi-language SDKs and Google Cloud integration. All three are excellent — your choice should be driven by your existing stack and team expertise.