pretty_name: AgentLoad synthetic agent traces (PrefixAnvil)
license: other
task_categories:
- other
tags:
- llm-inference
- prefix-caching
- kv-cache
- agents
- synthetic
size_categories:
- n<1K
AgentLoad synthetic agent traces
Synthetic OpenAI-compatible chat-completion request logs of tool-using agent sessions, with ground-truth
labels of injected prefix-cache fragmentation (F1–F8). Generated by prefixanvil gen (PrefixAnvil AgentLoad);
fully reproducible from a small JSON config and a seed. No real user data. The traces are generated by
PrefixAnvil — https://github.com/NagaYu/prefixanvil
License
This dataset is made publicly visible solely so it can be read and evaluated. It is All Rights Reserved — no license is granted beyond viewing and downloading for evaluation. Licensing or transfer inquiries: https://nagayu-portfolio.nagayu.workers.dev/en
Files
| file | content |
|---|---|
bench_main.jsonl |
377 requests, 40 sessions, all eight fragmentation types injected at 6% each |
bench_main.labels.jsonl |
one line per injected event: request_index, request_id, fault (F1–F8), scope (within/cross session), reference_request_id, location |
bench_xsession.jsonl |
155 requests, 30 sessions, no faults; each session's client lists the same tools in its own order |
configs/*.json |
the exact generator configs (seeds included) |
Each request line: request_id, session_id, timestamp, model, messages, tools.
How it was generated
prefixanvil gen --config fixtures/bench/bench_main.json --out bench_main.jsonl
prefixanvil gen --config fixtures/bench/bench_xsession.json --out bench_xsession.jsonl
Byte-identical output for the same config (pure-Python RNG; checked in CI). File sha256 of the four .jsonl
files in this folder:
| file | sha256 |
|---|---|
bench_main.jsonl |
cee5445df3364b3fc0a2af4a1f906356d537076857a5c4059ce96a6b800ef000 |
bench_main.labels.jsonl |
5b114c0cf4547eadb77454313d82249e1fdccd869118d72864a31c16415b623a |
bench_xsession.jsonl |
271a8a6f3c2a61e9ae4b0878285e65ac96e17bf0b08202b415bff2c3a2f819df |
bench_xsession.labels.jsonl |
ec1431b9afd077cdc20026ca7886c5169f821ff4f0ec3224bba81a73d9af0b39 |
Fragmentation types
F1 volatile values (timestamps, UUIDs, run ids) · F2 non-deterministic JSON serialization · F3 tool-set churn · F4 per-user interpolation · F5 conditional system sections · F6 whitespace/format drift · F7 retrieved context inserted before stable instructions · F8 rewritten history. At most one event per request/reference pair, so the cause of each first divergence is unambiguous.
Benchmark headline
Measured on Kaggle 2 × Tesla T4 (free tier) with Qwen2.5-7B-Instruct-AWQ and vLLM 0.33.0, replaying these
traces at concurrency 1 (run t4-20261111-0408). Hit rate and prefill are determined by the cache algorithm,
not the GPU; TTFT/GPU-seconds (in BENCH.md) are T4 reference values only.
| trace | policy | token-weighted hit rate | recomputed prefill tokens |
|---|---|---|---|
| bench_main | raw | 74.71% | 241,640 |
| bench_main | L1 canonicalization | 81.79% | 172,268 (−28.7%) |
| bench_main | L2 relocation (reference) | 83.61% | 155,068 (−35.8%) |
| bench_xsession | raw | 86.63% | 50,120 |
| bench_xsession | L1 session-stable (deployed policy) | 86.69% | 49,273 |
| bench_xsession | L1 stateless name-sort (ceiling) | 95.31% | 17,369 |
L1/L2 are prompt transforms; identical generated text is not guaranteed (Phase 3 sampled L1-vs-raw match 10/18,
L2-vs-raw 4/6, against an 18/18 raw-vs-raw control). Full numbers, fidelity and the NVIDIA Dynamo trial are in
BENCH.md.
Limitations
Synthetic text (word-salad sentences), synthetic tools, open-loop (assistant turns are generated, not model outputs). Useful for measuring prefix-cache behaviour, not for training or evaluating language quality.