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Published: Sep 30, 2026 License: Apache-2.0 Imports: 10 Imported by: 0

README

Scale and concurrency benchmark

cmd/bench is a load harness that drives many durable agent runs concurrently in one process against a stub model, so it measures what the framework adds (orchestration, journaling, the agent loop, concurrency) rather than LLM latency. Optional -latency simulates per model-call I/O wait to show how Go absorbs large concurrent I/O-bound fan-out.

go run ./cmd/bench -runs 20000 -concurrency 5000 -latency 50ms

What it shows

Agent workloads are overwhelmingly I/O-bound: a run spends its wall-clock waiting on model and tool calls. Go's scheduler and cheap goroutines mean one process can keep a very large number of those durable, journaled runs in flight at once. That is the "library, not cluster" pillar with the concurrency turned up.

Measured

Two environments, medians reported. Every run journals to the in-memory MemStore, so the journal-records figure is an in-memory write rate, not a durable-store one. Each run writes five journal records in these scenarios.

A standard GitHub Actions runner (ubuntu-latest, 4 vCPU AMD EPYC 7763, Go 1.27), each scenario run 21 times, from the Benchmark workflow at v0.8.0. Anyone can reproduce these: Actions, Benchmark, Run workflow. Runner CPU models vary between jobs, so compare runs on the same model.

Scenario Wall-clock Runs/s Journal records/s p50 p99 Peak goroutines Heap delta
Overhead: -runs 5000 -concurrency 256 ~211 ms ~23,700 ~118,400 0.98 ms 52 ms 304 ~14 MB
I/O fan-out: -runs 20000 -concurrency 5000 -latency 50ms ~1.05 s ~19,000 ~95,000 222 ms 419 ms 5,805 ~64 MB

A 10-core Apple silicon Mac (darwin/arm64, Go 1.27), measured at v0.7.0, each scenario run seven times, when each run wrote four journal records. It was measured with other work running (a virtual machine using a full core, load average about 4), so treat it as a lower bound for that machine.

Scenario Wall-clock Runs/s Journal records/s p50 p99 Peak goroutines Heap delta
Overhead: -runs 5000 -concurrency 256 ~95 ms ~52,700 ~210,700 2.4 ms 25 ms 319 ~9 MB
I/O fan-out: -runs 20000 -concurrency 5000 -latency 50ms ~469 ms ~42,600 ~170,500 101 ms 139 ms 5,563 ~62 MB

The fan-out rows are the point. Each run makes two model calls at 50ms each, so ~100ms of unavoidable wait, and 20,000 of them, 5,000 in flight at a time, finish in about half a second on the Mac (measured at v0.7.0), where the median run takes the model's own ~100ms because the runs fully overlap. On the 4-vCPU runner the same work takes about one second: with fewer cores the framework's CPU work, not the model wait, sets the pace. Either way it is a few thousand goroutines and tens of MB in one commodity process instead of a cluster.

What this does NOT claim

  • Not lower latency than the model. The provider dominates per-call wall-clock; the win is throughput, concurrency, and operational simplicity, not per-call speed.
  • The stub model has no real inference; this measures framework overhead and concurrency, not a provider.
  • MemStore is the in-memory floor. In production the durable store's write throughput (SQLite, Postgres, partitioned) is the real ceiling at high fan-out, not goroutines. Benchmark against your store to find that limit; do not quote these numbers as a production SLA.

Documentation

Overview

Command bench is a concurrency/scale load harness: it drives N durable agent runs concurrently in one process against a stub model (so it measures the FRAMEWORK's orchestration, journaling, and concurrency overhead, not LLM latency), and reports throughput, latency percentiles, peak goroutines, and memory. Optional -latency simulates per model-call I/O wait to show how Go absorbs large concurrent I/O-bound fan-out.

The journal-records figure counts the records the runs actually wrote (read back from the store after the timed section) and divides by the elapsed time. The store is the in-memory MemStore, so it is not a durable-store write rate.

go run ./cmd/bench -runs 20000 -concurrency 5000 -latency 50ms

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