BatchBench

BatchBench ships a Rust benchmarking core with a thin Python wrapper.

You can install it with:

uv pip install batchbench

The Python package exposes Rust functionality for request generation and benchmark execution.

Python API

import batchbench

config = {
    "endpoint": "https://example.com/v1/chat/completions",
    "user_count": 1,
    "mode": batchbench.finite_mode(requests_per_user=1),
    "request_body": batchbench.request_entry(
        {
            "model": "gpt-4o-mini",
            "messages": [{"role": "user", "content": "ping"}],
        },
        line_idx=0,
        input_tokens=1,
    ),
    "requests": [
        batchbench.request_entry(
            {
                "model": "gpt-4o-mini",
                "messages": [{"role": "user", "content": "ping"}],
                "max_tokens": 4,
            },
            line_idx=0,
            input_tokens=1,
        )
    ],
    "dry_run": True,
}

report = batchbench.run_benchmark(config)
print(report)

Request generation:

requests = batchbench.generate_requests(
    {
        "count": 16,
        "prefix_overlap": 0.2,
        "target_tokens": 128,
        "tokenizer_model": "Qwen/Qwen3-VL-235B-A22B-Instruct-FP8",
        "dist_mode": "fixed",
    },
    model="Qwen/Qwen3-VL-235B-A22B-Instruct-FP8",
)

Python CLI

The package installs batchbench, which forwards directly to the Rust CLI implementation. Use the same flags as the Rust binary:

batchbench \
  --model gpt-4o-mini \
  --users 8 \
  --requests-per-user 2 \
  --input-tokens 256 \
  --output-tokens 64 \
  --output-vary 0

Use --sglang to apply output token constraints via min_new_tokens/max_new_tokens instead of min_tokens/max_tokens.

Press Ctrl+C during a run to cancel active requests and print a partial summary.

Agent-loop benchmark

batchbench-agent is a separate entrypoint for stateful agent workloads. It starts --agents independent loops concurrently. Within each agent, model requests remain sequential: the returned assistant message and a synthetic tool/environment response are appended to messages, then the complete growing conversation is sent again. This makes every request after the first one reuse that agent's previous prompt as a prefix and exercises server-side KV caching.

Fixed-length example:

batchbench-agent \
  --model Qwen/Qwen3-8B \
  --host http://localhost:8000 \
  --agents 16 \
  --input-tokens 256 \
  --output-tokens 64 \
  --environment-tokens 128 \
  --tool-invocations 8 \
  --tool-call-latency-ms 250

Log-normal example:

batchbench-agent \
  --model Qwen/Qwen3-8B \
  --host http://localhost:8000 \
  --agents 16 \
  --input-lognorm-median 256 \
  --input-lognorm-sigma 0.5 \
  --input-lognorm-max 2048 \
  --output-lognorm-median 64 \
  --output-lognorm-sigma 0.4 \
  --output-lognorm-max 512 \
  --environment-lognorm-median 128 \
  --environment-lognorm-sigma 0.6 \
  --environment-lognorm-max 1024 \
  --tool-invocations-lognorm-median 8 \
  --tool-invocations-lognorm-sigma 0.3 \
  --tool-invocations-lognorm-max 32 \
  --tool-call-latency-lognorm-median-ms 250 \
  --tool-call-latency-lognorm-sigma 0.5 \
  --tool-call-latency-lognorm-max-ms 2000 \
  --seed 42

Each log-normal family accepts either *-lognorm-median or *-lognorm-mu, requires *-lognorm-sigma, and optionally accepts *-lognorm-max. Samples are independent between agents and turns; --seed makes the sampled workload reproducible. --tokenizer-model can be supplied when the endpoint's model name is not also a Hugging Face tokenizer identifier. It accepts a Hugging Face model ID, a local tokenizer.json file, or a local directory containing tokenizer.json.

Trajectory replay and rolling admission

Use --agent-plans-jsonl to replay complete empirical token shapes instead of sampling initial prompts, outputs, environment results, and invocation counts independently. Each non-empty JSONL line is one trajectory, and file order is the FIFO admission order:

{"schema_version":1,"trajectory_id":"agent-001","requests":[{"prompt_tokens":13381,"output_tokens":288},{"prompt_tokens":14800,"output_tokens":512,"delay_after_ms":25},{"prompt_tokens":7000,"output_tokens":128,"reset_before":true}]}

prompt_tokens and output_tokens are required and must be positive. delay_after_ms defaults to zero. For a normal transition, BatchBench infers the synthetic environment growth as:

next prompt - current prompt - current output

If a trajectory compacts or resets such that this value would be negative, mark the next request with "reset_before": true. BatchBench then starts that request from a fresh synthetic prompt while preserving the trajectory's user/routing identity. A reset deliberately claims no prefix-cache reuse from the preceding request.

Absolute prompt counts commonly include chat-template and tool-envelope tokens that are not part of the synthetic message content. After calibrating those values against the target backend, use --replay-initial-overhead-tokens for a first request or reset, and --replay-turn-overhead-tokens for each normal appended turn. BatchBench subtracts these values when generating content while retaining the manifest's absolute prompt targets for reporting.

--max-active-agents bounds simultaneous trajectories while retaining every plan in the manifest. BatchBench initially admits up to that limit. Whenever an active trajectory terminates, the next queued trajectory prepares its initial prompt and then inherits the freed scheduler/routing slot. Multiple replacements prepare concurrently through the same bounded worker pool while preserving FIFO admission order, keeping active load balanced across data-parallel ranks when perfect routing is enabled.

batchbench-agent \
  --model Qwen/Qwen3-8B \
  --host http://localhost:8000 \
  --agent-plans-jsonl examples/trajectory-replay/plans.jsonl \
  --max-active-agents 2 \
  --replay-initial-overhead-tokens 0 \
  --replay-turn-overhead-tokens 0 \
  --agent-events-jsonl agent-events.jsonl \
  --seed 42 \
  --dry-run

Do not combine --agent-plans-jsonl with --agents or the synthetic workload-shape flags. --seed remains useful because it makes generated placeholder text reproducible. Generated content is accepted only when it re-encodes to the requested token count; an unconstructable field fails with its trajectory context instead of silently changing the workload.

Replay manifests remain compact in memory: BatchBench materializes the initially active prompts before the benchmark clock starts, then generates only the current environment response and the next reset prompt while the current HTTP request is in flight. Token generation uses a bounded worker pool, and its per-agent/per-turn random streams are independent of asynchronous scheduling. Request JSON is serialized from the live conversation without first cloning the complete message tree.

--agent-events-jsonl writes each trajectory's queue/admission time, reusable routing slot, optional DP rank, finish time, runtime, and completion status. The summary separately reports the final admission time and final drain duration.

Trajectory replay reproduces the joint request-count and nominal token-shape plan, not the original text. Prompt targets include workload-specific framing, while BatchBench generates its own chat/tool framing and exact-length synthetic message content. Uncalibrated framing can therefore make live prompts drift systematically from their targets over a long trajectory. Live usage.prompt_tokens is authoritative and should be compared with the plan before a capacity result is accepted. Likewise, exact output lengths require a backend that honors the requested minimum/maximum token constraints.

Every request includes a user field and an X-SMG-Routing-Key header containing the same UUID, which remains stable for the life of that agent and differs between agents. Use --disable-user-tagging to omit both, or --user-prefix <prefix> to use deterministic <prefix>-<agent_id> values instead of UUIDs.

For deterministic data-parallel routing, pass both --dp-rank-perfect-routing and --dp-rank-perfect-routing-num <ranks>. Each request then includes X-SMG-Target-Worker: <routing_slot % ranks>. Initial agents occupy consecutive slots; under rolling admission, a replacement inherits the slot freed by the trajectory that just terminated. This preserves the active per-rank balance and is independent of user tagging.

Tool-call latency defaults to zero. Set a fixed delay with --tool-call-latency-ms, or sample milliseconds independently for every invocation with --tool-call-latency-lognorm-median-ms (or --tool-call-latency-lognorm-mu), --tool-call-latency-lognorm-sigma, and the optional --tool-call-latency-lognorm-max-ms. After a model response succeeds, the agent sleeps for the sampled duration before making the environment result available and submitting its next model request.

One tool invocation means one unconstrained model request followed by one synthetic environment response. BatchBench treats generated assistant output as opaque state: it preserves content and reasoning output but ignores any model-generated tool calls. It then adds its own valid synthetic environment tool call and appends the sampled environment response. This keeps the trajectory protocol-valid without assuming anything about the generated output.

The final report includes:

• total input tokens sent, from successful responses' usage.prompt_tokens;
• total output tokens generated, from usage.completion_tokens;
• estimated cached input tokens under perfect prefix caching;
• total simulated tool-call latency across all agents;
• request-latency p50, p90, and p99 across successful requests;
• end-to-end p50, p90, and p99 across completed agents, measured from the start of each agent loop through its final synthetic tool delay.

Failed agents are excluded from the end-to-end latency distribution because their lifetimes end early. Their request failures are still included in the failure report.

For each successful request after an agent's first, the cache estimate adds that same agent's preceding prompt-token count (capped by the current prompt count). Retries and failed requests are excluded because they do not provide reliable usage data. Use --results-csv <path> to persist the same totals, or --dry-run to inspect the sampled or replayed plans without sending requests.

Rust CLI

The existing Rust CLI is unchanged:

cargo build --release --manifest-path rust/Cargo.toml --bin batchbench
./rust/target/release/batchbench --help

The agent-loop binary can likewise be run directly:

cargo build --release --manifest-path rust/Cargo.toml --bin batchbench-agent
./rust/target/release/batchbench-agent --help

Releases and PyPI

• CI (.github/workflows/ci.yaml) checks Rust build/test, builds a wheel, and runs smoke tests.
• Release Please (.github/workflows/release-please.yaml) opens/updates release PRs and, on merge, creates v* tags/releases.
• Python release workflow (.github/workflows/python-release.yaml) builds and publishes prebuilt platform wheels to PyPI on v* tag pushes.