--- language: - en - zh license: mit task_categories: - text-generation tags: - agent - tool-calls - trajectories - audit - llm-agents size_categories: - 1K 🔗 **This dataset is the runtime trace of [MimirAether](https://github.com/Wanxian-Liu/Mimir-Aether)** — an open-source, self-hosted agent runtime under **active development** (code, docs, roadmap & CI on GitHub). Issues and feedback on data quality are welcome on either repo. > 🔄 **Continuously updated** — new sessions are appended in small batches (~every 100 sessions) as the runtime keeps operating. ## Loading ```python from datasets import load_dataset ds = load_dataset("kelikelibababian/mimir-agent-traces", split="train") print(ds[0]) # one row = one complete agent session ``` The viewer table is served as **Parquet** (`data/train.parquet`) — a unified-schema view over the append-only JSONL source (`data/mimir-agent-traces.jsonl`, which is kept as-is and never rewritten). It covers the **curated main file (4,168 sessions)**; the larger pre-filter archive is kept separately as `data/mimir-agent-traces-legacy-full.jsonl` and is **not** part of this table. Rows uploaded before schema v2 carry `null` in the seven v2-only columns, so the curated corpus loads as a single table. ## What's inside Each row = one complete agent session: > **Schema note**: v1 rows have `task_desc_sanitized` capped at 200 chars; from schema v2 (2026-09-13) the cap is 300. v1 rows (uploaded before 2026-09-13) **lack** the `schema_version` field — absence of the field = v1. The taxonomy is "9 categories designed, 7 reachable" via the tool-sequence heuristic — see the v2 section below. | Field | Type | Description | |---|---|---| | `session_id` | string | unique session identifier (salt-free SHA-1 prefix) | | `n_tool_calls` | int64 | number of tool calls in session | | `duration_seconds` | float64? | wall-clock duration | | `exit_reason` | string? | how the run ended (`natural` / `max_turns` / `interrupt` / `circuit_breaker` / `tool_storm` / `api_failure`); `null`/`unknown` for pre-2026-08-29 sessions | | `tool_sequence` | list | ordered sequence of tool names (capped at 80) | | `has_exit_reason` | bool | whether telemetry recorded an exit reason | | `task_desc_sanitized` | string | task description, truncated (200 chars in v1, 300 in v2) | | `schema_version` | string? | `"2.0"` for v2 rows; **`null` for v1 rows** (absence = v1) | | `agent_id` | string? | which agent ran it — `mimir` / `hermes` / `openclaw` / `loki` | | `model` | string? | batch-level model name (see *Model field caliber*) | | `trace_id_prefix` | string? | first 8 hex of the trace id, for joining the audit stream | | `trigger_source` | string? | `feishu` / `api` / `buzz-watcher` / `watchdog` / `cron` | | `task_category` | string? | heuristic task class (weak signal — see below) | | `task_subcategory` | string? | finer task class | > **v1 rows carry `null` in the seven v2-only columns** — the Parquet view pads them so the whole corpus loads as one table. ## Why this matters Real-world agent behavior data is scarce; **real-world multi-agent failure data is nearly nonexistent**. Most published agent benchmarks are synthetic. This dataset captures what agents actually do in production — including **genuine incident-response sessions** (e.g., a cron job freezing the event loop, diagnosed via py-spy and fixed live; API-provider outages with retry/backoff traces), tool-usage patterns, and self-audit loops where an agent finds its own bug and files a correction. Failure trajectories are included deliberately: they are the hardest data to synthesize and the most valuable for robustness training. ### Dataset statistics (full corpus) | Metric | Value | |---|---| | Sessions | 7,028 unique | | Total tool calls | 36,995+ | | Duration | p50 = 0s (many short automated wakeups) · p90 = 132s | | `exit_reason` | `natural` 247 · `max_turns` 18 · `empty_response` 14 · `api_failure` 8 · `interrupt` 2 · `unknown` 6,739 | | `tool_sequence` non-empty | 6,936 | > **`exit_reason` caveat**: 96% of v1 rows are `unknown` — the recorder only started writing `session_end` telemetry partway through the corpus, and pre-2026-08-29 `session_end` events lack the `exit_reason` key. Even in schema v2 batches, `exit_reason` can be `null` when the underlying telemetry omits it (observed: ~22% in the first v2 preview window, which spans legacy sessions). The v2 gate guarantees the session *ended*; it cannot backfill a missing reason. Useful for: agent behavior modeling, tool-use pattern mining, failure analysis, run-boundary research, agent memory studies. ## Sanitization All identifiers are hashed; user names, paths, emails and internal infrastructure names are redacted. Task descriptions truncated to 200 chars. Tool arguments/outputs NOT included (privacy) — only the tool-name sequence and metadata. Each batch is scanned for personal identifiers before upload; any row that fails the scan is dropped. ## Source Generated by [MimirAether](https://github.com/Wanxian-Liu/Mimir-Aether) — an open-source, auditable agent runtime. ## Schema v2.0（2026-09-13 起） 自 2026-09-13 起新增批次采用 **schema v2**，v1 行保留（混合读取安全）： ### v2 新增字段 | 字段 | 类型 | 说明 | |---|---|---| | `schema_version` | `"2.0"` | 行级版本标识（前向兼容） | | `agent_id` | enum | `mimir` / `hermes` / `openclaw` / `loki` | | `model` | string | 运行模型名 | | `trace_id_prefix` | string | trace id 的 hex 段前 8 位（类型前缀 `run_`/`tr_` 已剥除，join 审计流用；32-bit 熵）。**仅 2026-09-13 X2-a 注入体系上线后的会话有值，历史会话为 null** | | `trigger_source` | enum | `feishu` / `api` / `buzz-watcher` / `watchdog` / `cron`（同上，仅新会话有值） | | `task_category` | enum | 9 类任务分类设计，实际 **7 类可达**（Communication/Creative 极少；见下）；**由工具序列启发式投票推断，非原生标注**；零工具会话归 Other | | `task_subcategory` | string? | 细分（可空） | ### 任务分类法（9 类设计 · 7 类实际可达） `Terminal&Coding` · `Browser` · `Agent Tools` · `Retrieval` · `Memory` · `Communication` · `Analysis` · `Creative` · `Other` > **Heuristic disclosure**: categories are assigned by a deterministic tool-name voting table (no LLM). In the full corpus, `Communication` and `Creative` are structurally rare; ~31% of a recent window lands in `Other` (mix of zero-tool sessions and tied votes). Treat `task_category` as a *weak signal* for stratification — not ground truth. ### Model field caliber `model` is a **batch-level** value (trajectories carry no per-session model). Resolution order: runtime telemetry (`last_context_usage.json`, what the gateway actually used) → `config.yaml` `model.default` → legacy `.env` `MIMIR_MODEL`. A model switch mid-window makes the batch value approximate for sessions before the switch; exact per-session models land once the runtime writes model into `session_start` (planned). ### Known limitations - **Session identity**: `session_id` is a salt-free SHA-1 prefix of Mimir's raw session id. A session continued across midnight (≈4% of sessions) keeps the same id; the dataset keeps the **first uploaded version** of such a session and later continuation files are skipped (no upsert). Rows are always duplicate-free and never wrong — at worst the tail of a continued session is missing. This will disappear naturally once the runtime emits unique ids per session. - **Batch-level `model`**: see *Model field caliber* above. ### Provenance 每批上传记录于 `manifest.jsonl`（批次/日期/会话数/备注）。原始会话文件**永不上传**——只有聚合统计行。 ### 相关项目 - 💻 运行时源码：[MimirAether](https://github.com/Wanxian-Liu/Mimir-Aether)（活跃开发中） - 📖 归因体系：commit trailer `Agent: ` + 双流审计（工具级+钩子级）+ `who_did.py` 自查入口