Comparisons / CrewAI vs Google ADK
CrewAI vs Google ADK: Which Agent Framework to Use?
CrewAI crewai organizes work into agents, tasks, and crews. Google ADK google's agent development kit (adk) is an open-source framework for building multi-agent systems. Here is how they compare — and what the same patterns look like in plain Python.
By the numbers
CrewAI
48.0k
6.5k
Python
MIT
2023-10-27
João Moura
Google ADK
18.7k
3.2k
Python
Apache-2.0
2025-04-01
Google/Alphabet
Vertex AI
Yes
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | CrewAI | Google ADK | Plain Python |
|---|---|---|---|
| Agent | Agent(role, goal, backstory, tools, llm) | LlmAgent class with model, instructions, and sub_agents list | A function with a system prompt and a tools dict |
| Tools | Tool registration with @tool decorator, custom Tool classes | FunctionTool, built-in tools (Search, Code Exec), third-party integrations | A dict: tools[name](**args) |
| Agent Loop | Internal to Agent execution, hidden from user | Runner.run() with automatic tool dispatch and sub-agent delegation | A while loop over messages with tool_calls check |
| Task Delegation | Crew(agents, tasks, process=sequential/hierarchical) | — | A task queue processed in a while loop with a budget cap |
| Memory | ShortTermMemory, LongTermMemory, EntityMemory | — | A dict injected into the system prompt |
| State | Task output passed between agents via Crew orchestration | — | A dict tracking tool calls and results |
| Multi-Agent | — | Hierarchical agent tree with root agent delegating to specialized sub-agents | Functions calling other functions: research = agent(prompt, tools=research_tools) |
| Workflows | — | SequentialAgent, ParallelAgent, LoopAgent workflow primitives | Sequential: call functions in order. Parallel: asyncio.gather(). Loop: while condition |
| Session | — | Session and State service with typed channels and persistence | A dict passed between function calls: state = {"turns": 0, "context": []} |
What both do in plain Python
Every concept in the table above — agent, tools, loop, memory, state — maps to a handful of Python primitives: a function, a dict, a list, and a while loop. Both CrewAI and Google ADK wrap these primitives in their own class hierarchies and APIs. The underlying pattern is the same ~60 lines of code. The difference is how much ceremony each framework adds on top.
When to use CrewAI
CrewAI shines for multi-agent setups where you want named roles ("researcher", "writer"). But the core mechanics — tool dispatch, the agent loop, task scheduling — are the same patterns you can build in plain Python.
What CrewAI does
CrewAI models multi-agent systems as a crew of specialists. Each Agent has a role ("Senior Researcher"), a goal ("Find the best data sources"), a backstory that shapes its behavior, and a set of tools it can use. Tasks define discrete units of work with expected outputs. The Crew orchestrates execution — sequentially, hierarchically, or with a custom process. CrewAI also provides memory systems (short-term, long-term, entity) and delegation, where one agent can hand off subtasks to another. The mental model is a team of people collaborating on a project. For prototyping multi-agent workflows where you want to reason about roles and responsibilities, it provides a clean vocabulary.
The plain Python equivalent
An Agent in CrewAI is a function with a system prompt that includes the role, goal, and backstory. The tools dict maps names to callables. Task delegation is a list of tasks processed in order — each task calls the assigned agent function with the task description appended to the messages. Hierarchical execution is a manager agent that decides which sub-agent to call next (just another tool choice). Memory is a dict injected into the system prompt. The entire crew pattern — multiple agents, task queue, delegation — is a for-loop over tasks, where each iteration calls the right agent function. No Crew class, no process kwarg. Just functions calling functions with a shared state dict passed between them.
When to use Google ADK
ADK earns its complexity when you need multi-agent orchestration on Google Cloud with Vertex AI deployment. If you're using Gemini and need production-grade agent infrastructure, it's well-designed. For single-agent use cases or non-Google stacks, plain Python keeps things simpler.
What Google ADK does
Google ADK provides a code-first framework for building agents that can delegate work to other agents in a hierarchy. You define an LlmAgent with a model, instructions, tools, and optionally a list of sub-agents. The root agent decides when to hand off tasks to specialized children. ADK ships with workflow primitives — SequentialAgent runs steps in order, ParallelAgent fans out concurrently, LoopAgent repeats until a condition is met. The framework handles session management, state persistence, and streaming out of the box. It's optimized for Gemini models and Vertex AI but works with other providers. For teams already on Google Cloud, the deployment story is seamless: containerize your agent and deploy to Vertex AI Agent Engine or Cloud Run.
The plain Python equivalent
A hierarchical agent is just functions calling other functions. Your root agent calls the LLM, and if the response indicates a sub-task, you call a different function with its own system prompt and tool set. Workflow orchestration is equally straightforward: sequential is calling functions in order, parallel is asyncio.gather(), and looping is a while loop with a condition check. Session state is a dict you pass between calls and optionally serialize to disk or a database. The entire pattern — root agent, sub-agents, workflows, state — fits in about 80 lines of Python. No class hierarchies, no Runner abstraction, no Agent Engine. When your agent misbehaves, you read your functions instead of tracing through framework internals.
Or build your own in 60 lines
Both CrewAI and Google ADK implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.
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