| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - agent-traces |
| - session-digests |
| - hermes |
| - knowledge-base |
| - training-data |
| pretty_name: Hermes Session Digests |
| size_categories: |
| - n<1K |
| task_categories: |
| - text-generation |
| - reinforcement-learning |
| --- |
| |
| # Hermes Session Digests |
|
|
| Structured, post-hoc summaries of Hermes AI agent sessions. Each digest captures goals, context, actions, decisions, durable learnings, errors, and promotion targets from a single agent session. |
|
|
| ## Purpose |
|
|
| - **Canonical knowledge base**: Digests are the searchable memory stream for the Hermes-powered knowledge management system |
| - **Training data**: When paired with raw agent trajectories, digests serve as teacher signals for instruction tuning, agent trajectory learning, and RAG fine-tuning |
| - **Auditability**: Human-readable, diffable, versioned record of what the agent did and why |
|
|
| ## Format |
|
|
| Two formats are provided for each session: |
|
|
| ### Markdown (`digests/*.md`) |
| Full human-readable digest with YAML frontmatter metadata. Structured sections: Goal, Context, Key Findings, Actions Taken, Decisions, Durable Learnings, Promotion Targets. |
| |
| ### JSON-Lines (`data/sessions.jsonl`) |
| Machine-readable structured extraction suitable for training pipelines. One JSON object per line per session. Fields include session_id, timestamp, model, decisions (list), learnings (list), actions (list), promotion targets (list). |
| |
| ## Model Filtering |
| |
| All sessions are tagged with the model that generated them. For training data, filter by model to avoid inconsistent style: |
| |
| ```python |
| import json |
| sessions = [json.loads(line) for line in open("data/sessions.jsonl")] |
| deepseek_sessions = [s for s in sessions if s["model"] == "deepseek-v4-pro"] |
| ``` |
| |
| ## Privacy |
| |
| All digests undergo PII removal before publishing: local file paths generalized, transient process IDs stripped, channel names abstracted. No API keys, tokens, email addresses, or personal identifiers are included. |
| |
| ## Schema |
| |
| ### Frontmatter Fields |
| | Field | Description | |
| |-------|-------------| |
| | session_date | ISO date of the agent session | |
| | model | Model that produced the agent responses | |
| | model_provider | API provider for the model | |
| | platform | Messaging platform (discord, telegram, cli) | |
| | project | Primary project context | |
| | domain | Knowledge domain | |
| | type | Always `session-digest` | |
| | status | draft / active / canonical / archived | |
| |
| ### JSON-Lines Fields |
| | Field | Type | Description | |
| |-------|------|-------------| |
| | session_id | string | Unique session identifier | |
| | timestamp | ISO datetime | Session start time | |
| | model | string | Agent model | |
| | decisions | string[] | Key decisions made | |
| | learnings | string[] | Durable learnings | |
| | actions_taken | string[] | Concrete actions performed | |
| | promotion_targets | string[] | Pages recommended for promotion | |
| | gaps_identified | string[] | System gaps discovered | |
| | strengths_identified | string[] | System strengths confirmed | |
| |
| ## Related |
| |
| - [r0b0tlabbra1n](https://github.com/r0b0tlab/llm-wiki_obsidian_hermes_r0b0tlabbra1n) — companion agent memory system |
| - [QMD](https://github.com/tobi/qmd) — local hybrid search engine used for retrieval |
| |
