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How to measure real tool time with live Docker tool calls with Aa Agentperf Local

Run the recorded shell commands in Docker containers so tool time is measured, not skipped.

Remote script. The build scripts pull or build third-party images and clone the SWE-bench harness from GitHub, and the recorded shell commands are untrusted input.

Who it is forA researcher or infrastructure engineer who needs agent timings that include real tool execution, not sleep estimates.

The problemBy default the replay skips tool time between turns, so end-to-end agent timing can be understated.

The outcomeA run with live tool timing and a results directory that includes tools.json.

What you need first

  • uv installed (it fetches Python 3.12 if needed)
  • A model server you already run with an OpenAI-compatible API, or a framework the tool can start (llama.cpp, vLLM, SGLang, or Splash on Apple Silicon)
  • For full results, a server or device that can run a 65,536-token context at batch size 1
  • Docker only if you want live tool calling

Steps

  1. Run scripts/install-swebench-validation.sh on arm64 hosts only, once
  2. Run scripts/build-default-containers.sh
  3. Run uv run agentperf-local run --base-url http://127.0.0.1:8080/v1 --model served-model --output-dir results/live-tools --tool-mode live (inferred, check the README)
  4. Read tools.json and summary.json in results/live-tools

Prompt for your agent

Using the agentperf-local repo from ArtificialAnalysis, build the default containers, then run `agentperf-local run` with --tool-mode live against <base-url> with model <model-name>, writing to results/<name>. Expect tools.json and summary.json in that directory. Verify that --live-network none was passed unless network access was explicitly needed.

Caveat. Docker is not a security boundary and access to the Docker daemon is equivalent to root on most hosts, so review the security caveats before running.

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