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:
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 — companion agent memory system
- QMD — local hybrid search engine used for retrieval
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