---
license: apache-2.0
pretty_name: agentx-mini
task_categories:
  - text-generation
tags:
  - llm
  - inference
  - agentic
  - benchmarking
  - kv-cache
  - multi-turn
  - subagents
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: traces.jsonl
---

# agentx-mini

**agentx-mini** is a smaller workload for benchmarking local LLM servers, derived from
[semianalysisai/cc-traces-weka-062126-256k](https://huggingface.co/datasets/semianalysisai/cc-traces-weka-062126-256k).
It contains **200 sessions and 9,439 model calls**, including **1,363 subagent calls**.

Changes from the original dataset:

- Divide input and output lengths by **8**, rounding each output up. Hash IDs stay the same; logical blocks shrink from 64 to 8 tokens.
- Divide active request time by **8** and shorten idle gaps. Replay uses a **1-second idle cap**.
- Keep complete sessions with **less than 10,000 requested output tokens**, counting all subagents and auxiliary calls.
- Preserve request grouping, spawn/join dependencies, shared prompt content, and reference overlaps. Actual replay overlap depends on server speed.

## Stats

```text
traces:                       200
main_turns:                 8,076
subagent_groups:               76
subagent_inner_requests:    1,363
total_model_requests:       9,439
total_input_tokens:   126,413,728
total_output_tokens:      962,854
```

Main turns are top-level calls. Input totals include repeated prefixes; actual
prefill depends on the server's prefix caching. Rendered prompts add
tokenizer/chat-template overhead.

## Files and reproduction

`traces.jsonl` contains the scaled Weka records; `stats.txt` contains their totals.
AgentX Harness handles prompt synthesis and replay using the target model's tokenizer.

To rebuild agentx-mini using [distill.py](distill.py):

```sh
pip install huggingface-hub orjson
python distill.py --output rebuilt
```

## Run agentx-mini with AgentX Harness

Requires Python 3.11–3.13. Install in a dedicated virtual environment, following
the [AgentX Harness README](https://github.com/SemiAnalysisAI/agentx-harness#install-and-run-agentx):

```bash
python3 -m venv .venv
source .venv/bin/activate
pip install agentx-harness==1.0.7
```

**Set the block size for agentx-mini.** AgentX 1.0.7 needs an adjustment for this
dataset's 8-token blocks. In the activated virtual environment, locate the
installed plugin configuration:

```bash
python -c 'from pathlib import Path; import aiperf.plugin; print(Path(aiperf.plugin.__file__).with_name("plugins.yaml"))'
```

Open that file. Under `public_dataset_loader` → `weka_hf` → `metadata`, change
`default_block_size: 64` to `default_block_size: 8`. This setting applies to all
`weka_hf` datasets in this environment; reinstalling the harness can reset it.

1. **Download agentx-mini** into a local directory.
2. **Start your local LLM server** and load your model, for example using
   `llama-server`. It must expose an OpenAI-compatible API. Enable parallel
   request slots to exercise subagents. Allow up to 32,000 tokens per request
   for input plus output, with additional room for chat-template overhead.
   Keep the server running during the benchmark.
3. **Configure AgentX.** Save the configuration below as `benchmark.yaml` beside
   `traces.jsonl`. Replace the served model name, tokenizer, and server URL with
   your server's settings. Set `hf_weka_dataset` to the **absolute path** of the
   downloaded dataset directory (run `pwd` there to get it).

```yaml
random_seed: 42
benchmark:
  models: [YOUR_SERVED_MODEL_NAME]
  tokenizer:
    name: YOUR_TOKENIZER
  endpoint:
    urls: [http://127.0.0.1:8000]
    streaming: true
    extra: {ignore_eos: true}
    use_legacy_max_tokens: true
  datasets:
    - name: default
      type: public
      dataset: weka_hf
      hf_weka_dataset: /absolute/path/to/agentx-mini
      sampling: sequential
      trace_idle_gap_cap_seconds: 1
  phases:
    - name: profiling
      type: concurrency
      timing_mode: agentic_replay
      concurrency: 1
      duration: 1200
      trajectory_start_min_ratio: 0
      trajectory_start_max_ratio: 0
```

Run from that directory:

```bash
agentx profile \
  --config benchmark.yaml \
  --output-artifact-dir ./results/agentx-mini
```

**Automatic download:** instead of downloading first, set `hf_weka_dataset` to
`SocialLocalMobile/agentx-mini` and run the same command.

## License

[Apache 2.0](LICENSE), matching the source dataset published by SemiAnalysis
from its Claude Code proxy traces. The dataset, script, and documentation in
this repository are distributed under these terms.

**Modified files:** `traces.jsonl` is a scaled and filtered derivative of source
revision [`8fecd2f`](https://huggingface.co/datasets/semianalysisai/cc-traces-weka-062126-256k/tree/8fecd2fc56694469f758f0afbbb6335ad3043740);
`stats.txt` is regenerated, and this README and `.gitattributes` are adapted for
agentx-mini. The data changes are listed above. References to SemiAnalysis
identify the source and do not imply endorsement.