The future will be millions agents running task everyday?
A new benchmark project evaluates the performance of various programming language runtimes for coding agents. The study focuses on the orchestration layer that manages the execution of tasks by agents, isolating the runtime's costs from the model's performance. Results indicate that the C++ runtime can handle 100 concurrent agents with a peak memory usage of approximately 93 MiB and a high success rate in task completion.
- ▪The benchmark compares agent runtimes across C++, Python, TypeScript, and Rust under controlled conditions.
- ▪The C++ runtime achieved a 96% pass rate on the HumanEval tasks with a maximum of three self-review retries.
- ▪Peak memory usage for 100 concurrent agents running on a single GPU was around 93 MiB.
Opening excerpt (first ~120 words) tap to expand
agent-runtime-bench A controlled, apples-to-apples benchmark of agent runtimes — the orchestration layer that drives an LLM through a write → execute → self-correct loop — across C++, Python, TypeScript, and Rust. Why this matters When people compare "coding agents" they almost always compare the model (pass@1 on HumanEval, SWE-bench, etc.). But in production the model runs behind a runtime: the code that fans out hundreds of agents, streams tokens, spawns test processes, retries on failure, and tracks state. That runtime — not the model — decides: Memory footprint when you run 100+ agents at once, Concurrency ceiling and tail behavior under load, Overhead added on top of model latency.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.