--- license: apache-2.0 pretty_name: CC Traces — Weka, With Subagents, v5 only (May 18 2026) task_categories: - text-generation tags: - llm - inference - benchmarking - kv-cache - agentic - multi-turn - claude - subagents size_categories: - n<1K configs: - config_name: default data_files: - split: train path: traces.jsonl --- # CC Traces — Weka, With Subagents, v5 only (May 18 2026) A collection of **96 multi-turn agentic traces** drawn from real production traffic against the Claude Code CLI ≥ 2.1.139. Each trace captures the full request/response sequence of a single agent session, including per-request KV block hashes AND the original sub-agent fan-out structure (Task-tool spawned sub-agents grouped into `WekaSubagentEntry` blocks). **With-subagents, v5-only variant.** Companion to [cc-traces-weka-no-subagents-051826](https://huggingface.co/datasets/semianalysisai/cc-traces-weka-no-subagents-051826), which is the same 96 traces with all `WekaSubagentEntry` blocks stripped. Use this dataset when you want to: - Simulate the full agentic-coding workload including parallel Task-tool sub-agent fan-out - Study sub-agent dispatch patterns, instance counts, durations - Replay against an inference engine that should see the same concurrent- request structure a real Claude Code session generates Use the **no-subagents sibling** when you want a single linear main-agent stream per trace and don't care about the parent / child fan-out. Same filters as the sibling: 1. **v5 only.** Every replayable request in every included session has `trace_version = 5` (the latest proxy schema, o200k_base tokenizer). Sessions containing any earlier-format request are excluded entirely. 2. **≥ 20 main-agent turns.** Per-trace main-agent stream (the would-be no-subagents stream) must have at least 20 turns. Sub-agent fan-out is on top of that. - **Traces:** 96 - **Top-level entries:** 22,184 (21,566 main turns + 618 subagent groups) - **Sub-agent inner requests:** 16,200 - **Total individual model requests:** 37,766 - **Models:** `claude-opus-4-7`, `claude-haiku-4-5-20251001`, `claude-opus-4-6`, `claude-sonnet-4-6` - **KV block size:** 64 tokens - **Hash scope:** `local` — block hash IDs are only comparable *within* a single trace; they are not a global content-addressable identity. ## Important: tokenizer caveat The `in` field on each request and the `hash_ids` array are both measured in **the proxy's tokenizer** (`o200k_base`, GPT-4o family). Anthropic typically reports ~60 % of the o200k token count for the same content. So the ISL numbers below are larger than what the Anthropic API would have billed for the same prompt — but they're self-consistent between `in` and `hash_ids`, which is what matters for KV-cache replay simulation. ## What's in each trace Top-level trace fields: | field | type | description | |-----------------|------------------|-----------------------------------------------------| | `id` | str | Trace identifier (the proxy session id) | | `models` | list[str] | Models used by the trace | | `block_size` | int | KV block size used to derive `hash_ids` (64) | | `hash_id_scope` | str | `local` — hash IDs are per-trace, not global | | `requests` | list[object] | Ordered list of per-request records OR `WekaSubagentEntry` groups | Each entry in `requests` is one of two shapes: ### Main-agent request (`type: "n"` or `"s"`) | field | type | description | |---------------|-----------|--------------------------------------------------------------------------| | `t` | float | Seconds since start of trace | | `type` | str | `n` (non-streaming) or `s` (streaming) | | `model` | str | Target model for this request | | `in` | int | Effective prompt tokens covered by `hash_ids` (proxy tokenizer) | | `out` | int | Output tokens (Anthropic-reported) | | `hash_ids` | list[int] | Per-trace local block-hash IDs for the input, one per 64-token block | | `api_time` | float | End-to-end server time for this call (seconds) | | `think_time` | float? | Wall-clock gap from previous request's end (seconds) | | `ttft` | float? | Time to first token (streaming requests only) | ### Sub-agent group (`type: "subagent"`) | field | type | description | |-----------------|----------------|--------------------------------------------------------| | `t` | float | Start time (seconds since trace start) | | `type` | str | Always `"subagent"` | | `agent_id` | str | Stable per-trace sub-agent instance id (slug + suffix) | | `subagent_type` | str | Sub-agent label (`"Subagent"` for Claude Code ≥ 2.1.139, else the original proxy label) | | `duration_ms` | int | Wall-clock span first→last inner request | | `total_tokens` | int | Sum of `in + out` across inner requests | | `tool_use_count`| null | Not tracked in the proxy DB | | `status` | str | Always `"completed"` | | `requests` | list[object] | Inner main-shape entries (type `n` — sub-agents are non-streaming) | | `models` | list[str] | Distinct models used inside this sub-agent run | Sub-agent groups are emitted at the position of the first inner request in the chronological stream. Their inner requests have absolute `t` values (not relative to the group start), making sub-agent and main- agent timelines directly interleavable for replay. `hash_ids` make the dataset unusually useful for KV-cache work: the contiguous common prefix between turn *t* and turn *t − 1* exactly measures the portion of the input that a local prefix cache would be able to reuse. The same accounting applies to sub-agent inner requests within their group, and to the dispatching parent request that triggered them. ## Summary statistics Across **all** requests (main + sub-agent inner = 37,766 requests): | | p50 | p75 | p90 | p95 | p99 | mean | |------------------------------|---------:|---------:|---------:|---------:|---------:|---------:| | ISL (tokens) | 100,902 | 229,982 | 443,856 | 580,438 | 803,019 | 172,848 | | OSL (tokens) | 224 | 478 | 1,150 | 1,907 | 5,461 | 530 | | Top entries/trace | 115 | — | — | 678 | 1,141 | 231 | ## Plots ### Main-agent stream Histograms across all 21,566 main-agent turns (sub-agent groups skipped). Same view as the [no-subagents sibling](https://huggingface.co/datasets/semianalysisai/cc-traces-weka-no-subagents-051826) — the two datasets share an identical main-agent stream. ![Main-stream distributions — log x](plots/distributions_log.png) ![Main-stream distributions — linear x](plots/distributions_linear.png) ### Sub-agent fan-out analysis Histograms across all 618 sub-agent groups (and their 16,200 inner requests). Companion to the main-stream plots — answers questions about how often sub-agents are spawned, how deep their tool-loops go, how long they take, and how much intra-group prefix cache reuse exists. ![Sub-agent distributions — log x](plots/subagent_distributions_log.png) ![Sub-agent distributions — linear x](plots/subagent_distributions_linear.png) Six panels: 1. **Sub-agent groups per trace** — distribution of how many distinct sub-agent invocations each session spawns 2. **Inner requests per group** — depth of each sub-agent's tool-use loop 3. **Group wall-clock duration** — first→last inner span (seconds) 4. **Group total tokens** — `Σ(in + out)` across each group's inner requests 5. **Inner-request ISL** — per-call input length INSIDE sub-agent loops (smaller than main-agent turns because sub-agents work on focused sub-problems with trimmed context) 6. **Intra-group cache hit rate** — for each non-first inner request, fraction of `hash_ids` already seen in earlier inners of the SAME group. Captures how much KV-cache reuse a local prefix cache would get out of a sub-agent's tool-use loop (typically high — sub-agents iterate on a stable prompt) ## Model composition | model | requests (main + subagent inner) | |-------|---------:| | `claude-opus-4-7` | 36,637 | | `claude-haiku-4-5-20251001` | 702 | | `claude-opus-4-6` | 343 | | `claude-sonnet-4-6` | 84 | ## Source Same proxy → weka pipeline as the [no-subagents sibling](https://huggingface.co/datasets/semianalysisai/cc-traces-weka-no-subagents-051826), just without the post-step that strips `WekaSubagentEntry` blocks. 1. `utils/sample_proxy_traces.py --min-trace-version 5 --min-main-turns 20 --privacy-mode anon` 2. `utils/proxy_to_weka.py` (subagent grouping per the dashboard's algorithm) 3. Concatenate the resulting weka JSONs into `traces.jsonl` — one trace per line. The same-96-trace contract with the no-subagents sibling makes the two variants safe to A/B compare for any benchmark that cares about sub-agent fan-out's impact on cache-hit-rate or throughput.