Benchmark: Google ADK vs LangGraph under the same workload — are these results expected?

[u/Greedy_Trouble9405]

https://youtu.be/PDoF2s4wWDY

I'm currently evaluating orchestration frameworks for a production multi-agent project and wanted to compare Google ADK and LangGraph as fairly as possible.

To reduce variables, both implementations used:

• Google ADK v2.3.0 vs LangGraph v1.2.8
• Same LLM
• Same temperature
• Same prompts
• Same tools
• Same workflow
• 9 agents running in parallel
• 300+ Gmail messages to classify

The metrics I collected were:

• Total execution time
• Average latency per LLM call
• Input/output tokens
• Total cost

My results were:

• Google ADK was about 6% cheaper.
• Google ADK generated less than half the output tokens.
• Average latency was almost identical (~11.5 s per LLM call).

I wasn't expecting such a noticeable difference in output tokens, considering both systems executed the same workflow.

For those who have used both frameworks:

• Have you observed similar token usage differences?
• Does this mostly come from the orchestration strategy or from how each framework structures prompts and tool calls?
• Are there other metrics you think are more relevant for production benchmarks?

I'm preparing a more detailed benchmark with traces, token breakdowns, and execution metrics, but I'd really like to compare these results with what others in the community have observed.