---
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