README.md

---
language:
  - en
  - zh
license: mit
task_categories:
  - text-generation
tags:
  - agent
  - tool-calls
  - trajectories
  - audit
  - llm-agents
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.parquet
---

# MimirAether Agent Traces

**7,124 unique real agent sessions · 35,000+ tool calls** from a **multi-agent system in daily production** — four cooperating agents (orchestrator, executor, red-team reviewer, verifier) running real workloads since May 2026.

**What makes this corpus different**: it is not single-agent coding transcripts. It captures **inter-agent collaboration in production** — task dispatch, cross-audits, failure & recovery (real incidents: gateway freezes, API outages, deadlocks — with their debug sessions included), and self-correction loops. If you train or evaluate agents on *cooperation*, failure recovery, or long-horizon operations, this is the rare real-world source.

> 🔗 **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<string> | 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: <id>` + 双流审计（工具级+钩子级）+ `who_did.py` 自查入口