Comparisons / CAMEL AI vs Google ADK
CAMEL AI vs Google ADK: Which Agent Framework to Use?
CAMEL AI pioneered role-playing multi-agent conversations in a 2023 NeurIPS paper. Google's Agent Development Kit (ADK) is an open-source framework for building multi-agent systems. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
CAMEL AI
16.6k
1.9k
Python
Apache-2.0
2023-03-17
CAMEL-AI.org (King Abdullah University)
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 | CAMEL AI | Google ADK |
|---|---|---|
| Agent | `ChatAgent` with `role_name`, `role_type`, and `system_message` for behavior | `LlmAgent` class with model, instructions, and `sub_agents` list |
| Tools | Tool modules registered on agents with OpenAI-compatible function schemas | `FunctionTool`, built-in tools (Search, Code Exec), third-party integrations |
| Role-Playing | `RolePlaying` session with `user_agent`, `assistant_agent`, and inception prompting | — |
| Inception Prompting | System prompts that embed the task, roles, and constraints to prevent drift | — |
| Society | Multi-agent societies with role assignment, communication, and voting | — |
| Task Decomposition | AI Society that splits tasks into subtasks assigned to specialist role pairs | — |
| Agent Loop | — | `Runner.run()` with automatic tool dispatch and sub-agent delegation |
| Multi-Agent | — | Hierarchical agent tree with root agent delegating to specialized sub-agents |
| Workflows | — | `SequentialAgent`, `ParallelAgent`, `LoopAgent` workflow primitives |
| Session | — | Session and State service with typed channels and persistence |
CAMEL AI vs Google ADK, head to head
CAMEL AI CAMEL AI pioneered role-playing multi-agent conversations in a 2023 NeurIPS paper.
Google ADK Google's Agent Development Kit (ADK) is an open-source framework for building multi-agent systems.
Both wrap the same underlying agent pattern — an LLM call, a tool dispatch, a loop — in different abstractions. The choice between them is mostly about which mental model and ecosystem fits the team you have, not which one is technically more capable.
Pick CAMEL AI if
Pick CAMEL AI if cAMEL AI's research contribution — role-playing and inception prompting — is a genuinely useful technique for reducing hallucination through multi-agent debate. But the technique is the value, not the framework. Two LLM calls with different system prompts give you the same pattern in plain Python. CAMEL AI is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Google ADK would force you to translate.
Pick Google ADK if
Pick Google ADK if 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. Google ADK is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; CAMEL AI would force you to translate.
What both add
Both CAMEL AI and Google ADK pull in a class hierarchy and a dependency tree to wrap what is, at the core, an HTTP POST in a while loop. If your use case is straightforward — one provider, a handful of tools, a single agent — the framework cost may exceed the framework benefit. The lesson below shows the same pattern in ~60 lines without either dependency.
Or build your own in 60 lines
Both CAMEL AI 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.
No framework. No dependencies. No opinions. Just the code.
Build it from scratch →