hersona

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Build once. Keep personality everywhere. Composable personalities for every LLM.

346 reusable character attributes for AI agent personas — compose a persona from personality / speech / archetype / visual / hobby templates, measure that it actually holds up in conversation, and port it to any LLM or agent framework. MIT (code) + CC0 (templates). CLI, MCP server, and Hermes Agent skill.

Docs · PyPI · Full reference

Quick start (30 seconds)

pip install hersona          # Python >= 3.11
hersona blend personality/tsundere speech/keigo --weight strong   # injection block → stdout
hersona export personality/tsundere speech/keigo --format openai_assistants > persona.json
hersona persistent personality/tsundere speech/keigo --target claude   # writes CLAUDE.md
hersona bench tsundere keigo --cost-only                          # measure the injection cost

No install? The live demo site runs the attribute catalog, blending, and a 9-question diagnostic quiz in the browser (auto-detects EN/JA).

Measured, not vibes

Personas drift: they lose their voice mid-conversation, get talked out of character, and cost tokens every turn. hersona ships a deterministic benchmark (hersona bench — no LLM, no embeddings, reproducible) that scores maintenance rate, decay curve, lock resistance under persona-override attacks, and per-weight token cost. What that buys you, measured (2026-07-12, minimax/MiniMax-M3, tsundere + keigo at --weight strong, persona-override attack scenario):

A hand-written prompt encodes one fixed voice — ask for strong and it can't follow; hersona re-renders the same attributes at the new weight. Honest caveat: this is one model / scenario pair, and repeat runs swing — never read a single run as a ranking. Full tables, all caveats, and the run-it-yourself comparison recipe: docs/BENCHMARKS.md.

Drop it into the config your agent already reads

hersona persistent --target writes the persona straight into the convention file of your coding agent:

hersona export hands the same persona to everything else — json, messages (chat array), markdown, openai_assistants, langchain_system_message.

What's inside

A typed, schema-validated library of 346 attributes (personality 43 / speech 140 / archetype 66 / visual 46 / hobby 51):

• Personality — tsundere, kuudere, yandere, airhead, intellectual, …
• Speech — kansai_ben, keigo, mandarin_casual, banmal, british_en, valley_girl_en, …
• Archetype — heroine, mentor, rival, idol, knight, villain, …
• Visual — silver_hair, glasses, petite, animal_ears, heterochromia, …
• Hobby — cooking, gamer, music, reading, astronomy, …

Each attribute declares core_traits, catchphrases, tone, and a compatible_archetypes / conflicts_with matrix, so the blend engine warns about incompatible mixes; intensity is tunable per attribute (mild / moderate / strong, or tsundere:strong keigo:mild inline).

hersona is a persona layer, not an agent framework — it keeps a character, branded voice, or roleplay partner consistent; it does not improve reasoning, retrieval, or tool-calling. One fixed persona? A hand-written prompt is fine. hersona pays off once you switch, blend, measure, or reuse personas (when to use hersona).

Use with Hermes Agent

No registry approval needed — works right now via tap:

hermes skills tap add shiro-0x/hersona
hermes skills install hersona
hermes skills install hersona-initializer

Then attach attributes in conversation:

/hersona list                         # list available attributes
/hersona personality/tsundere single  # attach a single attribute
/hersona personality/tsundere speech/keigo multi  # blend multiple attributes
/hersona personality/tsundere strong speech/keigo mild  # per-attribute intensity
/hersona default                      # detach

Command recipes (presets, preview, stacking layers) are in docs/REFERENCE.en.md; skill behavior notes in skills/hersona/SKILL.md.

Use as an MCP server (optional)

Expose the catalog, blending, exports, and the deterministic persona scorer (measure_intensity / bench_transcript — agents can score their own replies and self-correct) to MCP-aware agents like Claude Desktop:

pip install "hersona[mcp]"
hersona-mcp        # stdio MCP server

The full tool table is in docs/REFERENCE.en.md.

Beyond blending

• More CLI — recommend (diagnostic quiz), measure (score any text), diff, save/load presets, create (your own attributes), update (refresh templates without reinstalling): all in the CLI reference.
• Use cases (20) — --use-case programmer layers professional task discipline on top of the persona (hersona use-case list).
• Persona packs (14) — named, conflict-checked recipes for Hermes' multi-personality registry (hersona personas list).
• Guides — cross-persona playbooks such as self-introduction.
• Optional extras — pip install "hersona[tui]" for rich CLI output, "hersona[completion]" for shell tab-completion.

All documented in detail in docs/REFERENCE.en.md.

Data format

Every attribute is a YAML file under attributes/<category>/<name>.yaml, validated against schema/attribute.schema.json (python scripts/validate.py). The full 346-attribute catalog and the field-by-field schema reference are in docs/REFERENCE.en.md.

License

See also DISCLAIMER.md and SECURITY.md (what hersona update's checksum verification does and doesn't protect against).

Contributing

1. Add attribute templates as attributes/<category>/<name>.yaml — no proper nouns or specific works in examples / core_traits / catchphrases
2. Validate with python scripts/validate.py before opening a PR
3. 1 PR = 1 attribute as a rule; for multiple additions, agree in an Issue first

See CONTRIBUTING.md for details. Using hersona in a project? Add yourself to USED_BY.md. The implementation guide for agents / developers is at docs/IMPLEMENTATION_GUIDE.md.