Benchmark how fast your machine serves an AI agent.
An open-source tool from Artificial Analysis.

agentperf-local measures how fast your machine serves an AI agent. It replays recorded agent conversations against an OpenAI-compatible model server and reports throughput and latency. Each request carries the full conversation so far, as a real agent's would. It measures speed without measuring output quality.

It can benchmark a server you already run, or it can download a pinned model, start the server, and benchmark it for you.

You need uv. It fetches Python 3.12 if you do not have it. Docker and Rust are optional. macOS, Linux, and Windows are supported. On Windows, managed runs need an NVIDIA GPU and llama.cpp; vLLM and SGLang run only on Linux.

You also need either a model server that you already run with an OpenAI-compatible API, such as llama.cpp, LM Studio, vLLM, SGLang, or Ollama, or a serving framework that the tool can start. The bundled recipes use llama.cpp (llama-server on your PATH), vLLM or SGLang on NVIDIA GPUs, or Splash on Apple Silicon, at the exact version each recipe pins. The tool does not install frameworks. agentperf-local deployment-options --profile-id <id> shows which frameworks can serve a recipe on this machine.

uv tool install agentperf-local
agentperf-local

To try it without installing, run uvx agentperf-local. pipx install agentperf-local also works.

To work from a source checkout instead:

git clone https://github.com/ArtificialAnalysis/aa-agentperf-local.git
cd aa-agentperf-local
uv sync
uv run agentperf-local

The examples below use uv run agentperf-local from a checkout. With an installed tool, drop the uv run.

The TUI walks you through choosing a model, setting up, and running. Arrow keys move, Enter continues, Escape goes back, ? opens help, and q quits. To prefill settings with flags, use agentperf-local tui. See TEXTUAL_TUI.md.

To run without the TUI, pick a catalog profile and a framework you have installed:

uv run agentperf-local managed-run \
  --profile-id qwen38-27b-q4-k-m \
  --framework llama-cpp \
  --output-dir results/qwen38-27b

managed-run downloads the pinned model into the Hugging Face cache, checks every file's SHA-256, starts the server on localhost, runs the replay, and stops the server. Every recipe it can run is a YAML file in recipes/, by model then hardware.

The qwen38-27b-splash-dflash recipe serves on Splash, Inco AI's Metal engine for Apple Silicon (M3 or newer, macOS 26.4 or later). Install it with brew install incoai/tap/splash. The recipe pins Splash 1.0.2 and the incoai/Qwen3.8-27B-Splash package by commit. managed-run downloads and verifies the package itself, then starts Splash's server on that snapshot, so Splash never resolves a newer revision. Splash drops ignore_eos, so this recipe runs only under the recorded output policy, and its KV cache is always eight-bit. Both make its results not comparable with exact runs:

uv run agentperf-local managed-run \
  --profile-id qwen38-27b-splash-dflash \
  --framework splash \
  --output-token-policy recorded \
  --output-dir results/qwen38-splash

The default replay is agentperf-default-v1: eight recorded agent tasks with 168 model turns. It uses the exact output policy, which makes every turn generate its recorded number of tokens. This is the comparable run.

--replay aa-mini-v1 selects a six-turn synthetic replay. It is a quick install check. Its results are not comparable.

A full run needs a 65,536-token context at batch size 1. Every catalog profile launches at that context. An attached server that serves more, such as 131,072 tokens, also counts as full.

The default replay's largest turn needs about 58,000 tokens, so it only runs at the full 65,536. If your device cannot fit that, you can still check your setup with aa-mini-v1, which needs 8,192:

uv run agentperf-local managed-run \
  --profile-id gemma4-12b-it-q4-0 \
  --framework llama-cpp \
  --replay aa-mini-v1 \
  --context-tokens 8192 \
  --output-dir results/gemma4-12b-mini

In the TUI, start with uv run agentperf-local tui --replay aa-mini-v1 and choose a smaller context on the Setup screen. A run below 65,536 tokens is marked reduced: true and is not comparable with full-context results.

For an attached server, the tool reads the context at the start of the run: meta.n_ctx from llama.cpp, max_model_len from vLLM and SGLang, or maximum_context_tokens from the Splash status endpoint. A server that reports none of them is recorded as not comparable.

Point run at a server you already run:

uv run agentperf-local run \
  --base-url http://127.0.0.1:8080/v1 \
  --model served-model \
  --output-dir results/my-server

Use your server's base URL and the model name it reports:

The exact policy sends ignore_eos, which is not part of the OpenAI API. Before the replay, run and managed-run check that the server honors it. If it does not, they stop and name the flag to change. The TUI switches to the recorded policy instead and says so.

Ollama cannot honor ignore_eos. The tool detects Ollama before the run and warns you. The run then uses the recorded policy, which lets the model stop on its own and reports end-to-end latency as a normalized estimate. That result is not directly comparable with exact runs.

To send an API key, put it in an environment variable and pass the variable's name with --api-key-env. No flag takes a literal key.

By default the replay skips tool time between turns. run --tool-mode fixed_delay sleeps after each turn for the tool time recorded there. run --tool-mode live runs the recorded shell commands in Docker containers, so tool time is real. This mode is opt-in and needs Docker and prebuilt images. The build scripts are in the source checkout. They are bash scripts, so on Windows run them from Git Bash or WSL:

scripts/install-swebench-validation.sh   # arm64 hosts only, once
scripts/build-default-containers.sh

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

Security caveats:

• The recorded commands are untrusted input. Docker reduces the risk but is not a security boundary.
• Access to the Docker daemon is equivalent to root on most hosts.
• Containers have no network by default, but a replay manifest can ask for one. Pass --live-network none to force no network.
• The task workspace is mounted read-write. It lives under the output directory unless you pass --live-workspace-root.
• The scripts pull or build third-party images and clone the SWE-bench harness from GitHub.

Python is the default client. An optional Rust client records timings outside Python. It needs a Rust toolchain:

uv sync --extra rust
uv run agentperf-local tui --client rust

With an installed tool, use uv tool install 'agentperf-local[rust]' instead. A plain uv sync removes the extension again. Both clients produce the same metrics.

Submitting is optional and nothing is uploaded unless you ask. prepare-submission builds the submission file, submit sends it, and submission-status reads it back. A run on a server you started needs run --attached-server FILE. See SUBMITTING.md for details on what is sent to Artificial Analysis.

Run uv run agentperf-local <command> --help for every option. python -m agentperf_local is the same program.

A run writes a new directory:

results/my-server/
├── turns.jsonl
├── tasks.json
├── tools.json
├── failures.json
├── summary.json
└── measurement.json

summary.json holds the headline numbers: output tokens per second and median and p95 first-token and turn times. It is written last, so a crashed run has no summary. A managed run also writes the server log and deployment record. FORMATS.md describes each file.

The results are private. They can contain paths, model labels, and errors. Starting a run sends the recorded prompts to the model server, so a remote URL sends them off your machine.

make lint        # ruff check, ruff format --check, ty check
make test        # pytest
make test-rust   # Rust core tests

CI runs these checks and the Python and Rust client equivalence tests. See AGENTS.md for the full list and the code conventions.

• Recipes: managed-run recipes and instructions for adding more.
• Textual TUI: options, keys, and screens.
• Architecture: measurement rules, evidence boundary, and package layout.
• Data formats: inputs, outputs, and schemas.
• Submitting: what is uploaded and how it is used.

agentperf-local is built and maintained by Artificial Analysis. Code is licensed under Apache-2.0. The Artificial Analysis name and logo are not covered by the code license.