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How to measure token and latency savings on a local model with Okf Agent Memory

Run the bundled benchmark suite to check the claimed token reduction on your own hardware.

Who it is forA developer or researcher evaluating whether progressive disclosure cuts prompt size for a locally hosted model.

The problemThe repo claims large token and latency gains, and a team wants to check those numbers on its own machine before adopting the approach.

The outcomeA local benchmark run that reports time-to-first-token and token counts for the progressive disclosure setup.

What you need first

  • A Git repository where the agent works
  • A Go toolchain and make, since the README builds the tool with make build
  • Clone access to github.com/okf-memory/okf-agent-memory

Steps

  1. Run make build to create the tooling.
  2. Start a local model server such as LM Studio or Ollama, as the README suggests.
  3. Run make benchmark from the repository root.
  4. Open the Progressive Disclosure Benchmark Suite in the benchmarks/ folder to read the setup and output.
  5. Compare the time-to-first-token and token results against the README's claims for your own hardware and model.

Prompt for your agent

Using the okf-memory/okf-agent-memory repo, run the make benchmark target against the local model server at <endpoint>. Report time-to-first-token and token counts for the run and compare them with the README's claimed reduction. Verify that the benchmark used the local model and not a remote API.

Caveat. The README's headline figures for speed and token reduction are the author's claims, and the benchmark results will depend on your model and hardware.

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