CrewAI and Google ADK
Practice this questionOverview
- CrewAI organizes agent work around roles, tasks, and orchestration constructs such as flows.
- Google ADK is an agent development toolkit with agent and workflow-oriented components.
- Both provide reusable abstractions; neither makes a multi-agent design necessary.
Why this matters
Different teams prefer different ways to express roles, coordination, model integration, and deployment.
An everyday example
Teams can use different project-management systems while still following the same security and quality standards.
A technical example
CrewAI might express a research-and-review team; ADK might coordinate search, extraction, and response agents within a Google-centered deployment.
How it works
- Define scoped responsibilities
- Select supported orchestration
- Connect tools
- Manage run state
- Validate and evaluate outputs.
Architecture and flow
Application → framework roles/workflow → model + safe tools → evaluated output.
The insurance assistant example
A research agent compares public product documents, while private claim actions remain in the insurer's service boundary.
When to use it
After benchmarking a small proof of concept against a plain loop or LangGraph and confirming integration fit.
When to avoid it
Do not adopt a role-based team for one exact lookup or confuse Google integration with a requirement to use only one provider.
Interview answer
“I compare frameworks by state control, tool scopes, review, recovery, observability, and ecosystem fit, rather than memorizing their agent names.”
Benefits, trade-offs, and production considerations
- Compared with LangGraph, examine whether roles/workflow abstractions express your failure and approval paths clearly.
- Check framework versions, license, hosting, and run-state ownership.
- CrewAI docs, Google ADK docs.
| Framework | Core starting point | Useful enterprise fit | Key question before adoption |
|---|---|---|---|
| LangGraph | Explicit state, nodes, transitions | Stateful support and review flows | Can the team design recovery and state migrations? |
| OpenAI Agents SDK | Agents, runners, tools, handoffs | Agent-first assistants | Do its runtime/state features match the service requirements? |
| MAF | Agents plus explicit workflows | Microsoft-centered applications | Which language/version/hosting capabilities are actually supported? |
| Semantic Kernel | Model/function integration and agents | Existing Microsoft plugin applications | What remains maintained and what should migrate? |
| AutoGen | Agent coordination/conversation | Existing research or multi-agent applications | Does coordination justify cost and align with current support? |
| CrewAI | Roles, tasks, crews/flows | Structured specialist task teams | Are roles measurable or only cosmetic? |
| Google ADK | Agents and workflow tooling | Google-centered agent applications | Are deployment, model, and state contracts a good fit? |
You should be able to explain each framework's purpose, but hands-on depth in LangGraph plus one ecosystem SDK is more valuable than shallow API familiarity across all seven.