---
title: trie-workloads
canonical_url: "https://www.modelscope.cn/datasets/evalscope/trie-workloads"
md_url: "https://www.modelscope.cn/datasets/evalscope/trie-workloads.md"
repository: evalscope/trie-workloads
last_updated: 2026-05-26
license: apache-2.0
storage_size: "13 MB"
downloads: 343
stars: 0
---

# trie-workloads

> trie-workloads - evalscope 在 ModelScope 开源的数据集。Agentic inference benchmark traces re-hosted for use with evalscope perf.

evalscope/trie-workloads 是 ModelScope 魔搭社区上的数据集，存储大小 13 MB，采用 apache-2.0 许可。

- **Repository**: evalscope/trie-workloads
- **License**: apache-2.0
- **Storage size**: 13 MB
- **Downloads**: 343
- **Stars**: 0
- **Last updated**: 2026-05-26

Source: https://www.modelscope.cn/datasets/evalscope/trie-workloads

---

# trie-workloads

Agentic inference benchmark traces re-hosted for use with **evalscope perf**.

The traces originate from [applied-compute/trie](https://github.com/applied-compute/trie) (Apache-2.0), released alongside the blog post *"Benchmarking Inference Engines on Agentic Workloads"* (Applied Compute, April 2026). Each file is a trace-replay workload: every line describes one multi-turn agent conversation by token-length sequences and tool-call wait times, enabling reproducible serving benchmarks that capture multi-turn KV-cache reuse and tool-stall behavior absent from single (P, D) benchmarks.

## Files

| File | # traces | Source domain |
| --- | --- | --- |
| `agentic_coding_8k.jsonl` | 8192 | Coding-agent production traces, ~8k context |
| `code_qa_8k.jsonl` | 8192 | Code Q&A traces, ~8k context |
| `office_work_8k.jsonl` | 8192 | Office-work agent traces, ~8k context |

## Schema (one JSON object per line)

| Field | Type | Meaning |
| --- | --- | --- |
| `input_prompt_length` | `int` | Initial user prompt length in tokens (turn 1 input) |
| `num_turns` | `int` | Number of assistant-tool turns excluding the final assistant reply |
| `assistant_response_length` | `list[int]` | Tokens emitted by the assistant on each turn |
| `tool_call_output_length` | `list[int]` | Tokens injected as tool output before the next assistant turn |
| `tool_call_latency` | `list[float]` | Seconds the client sleeps before sending the next turn (simulated tool-call wait) |
| `final_assistant_response_length` | `int` | Tokens emitted in the final assistant message |

The replay rule (per trace, one concurrency slot held throughout):

```
context_0 = synth_prompt(input_prompt_length)
for i in range(num_turns):
    resp_i  = generate(context_i, max_tokens=assistant_response_length[i])
    sleep(tool_call_latency[i])
    tool_i  = synth_prompt(tool_call_output_length[i])
    context_{i+1} = context_i + resp_i + tool_i
generate(context_{num_turns}, max_tokens=final_assistant_response_length)
```

Requests must set `extra_body={"ignore_eos": true}` so `min_tokens == max_tokens` and the recorded length sequences are honored exactly.

## Usage with evalscope

```bash
evalscope perf \
  --model <served-model> \
  --url http://localhost:8000/v1/chat/completions \
  --tokenizer-path <hf-or-modelscope-id> \
  --dataset trace_replay \
  --subset agentic_coding_8k \
  --multi-turn \
  --parallel 24 \
  --duration 3600 \
  --ignore-eos \
  --stream
```

`evalscope` downloads this dataset automatically from `evalscope/trie-workloads` when `--dataset trace_replay` is selected and `--dataset-path` is not provided.

## License

Apache License 2.0 — copyright Applied Compute (2026). The original `LICENSE` file is included in this repository. When citing, please credit both the original blog and this re-hosted copy.

## References

- Blog: [Benchmarking Inference Engines on Agentic Workloads](https://blog.appliedcompute.ai/) — Applied Compute, April 2026
- Source: <https://github.com/applied-compute/trie>