hermes-session-digests

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

JSON-Lines Fields

Related

• r0b0tlabbra1n — companion agent memory system
• QMD — local hybrid search engine used for retrieval