kelikelibababian/mimir-agent-traces

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.

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.

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)

`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 — 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

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（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