Files
librefang-registry/plugins
Evan 7881d327a5 refactor: migrate icon fields from emoji to lucide:<name> tokens (#63)
* refactor: migrate icon fields from emoji to lucide:<name> tokens

Every TOML manifest's `icon = "<emoji>"` line is replaced with
`icon = "lucide:<kebab-name>"` — a reference to a lucide-react icon,
which the librefang.ai site and dashboard render as crisp SVG. Reasons
for the switch:

- Emoji render very differently across OS/browser/font stacks; the
  registry catalog looked inconsistent from one row to the next.
- Five manifests (clip / creator / linkedin / reddit / twitter) had
  their icons stored as literal Python-style escape strings
  ("\\U0001F3AC") because the TOML parser upstream never decoded
  them. Switching away from emoji drops that class of bug entirely.
- As a drive-by, also decode the \\uXXXX accent escapes in the
  [i18n.fr] block of hands/creator/HAND.toml so "Créateur" shows
  up correctly.

87 files touched. example manifests left untouched (still "TODO").

* fix: backfill i18n name + drop the single-member email category

- Every existing [i18n.<lang>] block now has a `name` field. 60 files
  previously translated description but kept the English name
  implicitly — which rendered as "some English some Chinese" in the
  registry UI. Fill in the missing name from the English brand (or a
  known localized equivalent: DingTalk→钉钉, Feishu→飞书, Email→
  电子邮件 / メール / E-Mail / Correo / Courriel, and a handful of
  hands that have Chinese product names like 视频剪辑 Hand).
- channels/email.toml was the only item under category="email";
  reclassify it as "messaging" so the sub-category filter chip list
  on the category page isn't littered with singletons.

* feat(i18n): localize 76 agents/integrations/plugins into 7 languages

Adds full [i18n.zh], [i18n.zh-TW], [i18n.ja], [i18n.ko], [i18n.de],
[i18n.es], [i18n.fr] blocks with name + description to every manifest
that previously shipped English-only.

Coverage:
- 32 agents (academic-researcher, analyst, architect, assistant,
  code-reviewer, coder, customer-support, data-scientist, debugger,
  devops-lead, doc-writer, email-assistant, health-tracker,
  hello-world, home-automation, legal-assistant, meeting-assistant,
  ops, orchestrator, personal-finance, planner, recipe-assistant,
  recruiter, researcher, sales-assistant, security-auditor,
  social-media, test-engineer, translator, travel-planner, tutor,
  writer)
- 33 integrations (AWS, Azure, Bitbucket, Brave Search, Discord,
  Dropbox, Elasticsearch, Exa Search, Fetch, Filesystem, GCP, Git,
  GitHub, GitLab, Gmail, Google Calendar, Google Drive, Google Maps,
  Jira, Linear, Memory, MongoDB, Notion, PostgreSQL, Puppeteer, Redis,
  Sentry, Sequential Thinking, Slack, SQLite, Teams, Time, Todoist) —
  brand names kept as-is across all locales, only descriptions
  translated.
- 11 plugins (auto-summarizer, context-decay, conversation-logger,
  episodic-memory, guardrails, keyword-memory, mempalace-indexer,
  sentiment-tracker, todo-tracker, topic-memory, user-profile)

The descriptions are one-line summaries — hand-translated rather than
machine-generated, so technical terms (MCP, PR, CI/CD, etc.) stay
consistent across locales.

* feat(i18n): close remaining per-lang gaps for channels, workflows, devteam

Third pass on i18n coverage. Every non-example manifest now carries a
full set of [i18n.zh], [i18n.zh-TW], [i18n.ja], [i18n.ko], [i18n.de],
[i18n.es], [i18n.fr] blocks.

- 44 channel adapters: added French descriptions (zh/zh-TW/ja/ko/de/es
  were already present). Brand names kept as-is in all locales so users
  recognize Discord / Slack / LINE / etc. consistently.
- 22 workflows: filled zh-TW / ja / ko / de / es / fr blocks. Each
  translation mirrors the existing zh one in structure and tone so the
  catalog reads consistently across locales.
- hands/devteam/HAND.toml: added the four langs that were missing
  (zh-TW, de, es, fr).

Only the 6 templates under examples/ are left without i18n blocks on
purpose — they still contain "TODO:" placeholders.
2026-04-17 22:04:26 +09:00
..

Plugins

Plugin packages for LibreFang. Plugins extend agent behavior through lifecycle hooks -- they can inject memories, modify context, or perform side effects during conversations.

Structure

plugins/
└── <plugin-name>/
    ├── plugin.toml          # Plugin manifest
    ├── hooks/
    │   ├── ingest.py        # Called on user message
    │   └── after_turn.py    # Called after each turn
    └── requirements.txt     # Python dependencies

plugin.toml Format

name = "plugin-name"             # Must match directory name
version = "0.1.0"
description = "What this plugin does"
author = "author-name"

[hooks]
ingest = "hooks/ingest.py"       # Receives user message, can return memories
after_turn = "hooks/after_turn.py" # Post-turn processing

Hook Protocol

Hooks communicate via stdin/stdout JSON:

ingest hook

stdin:  {"type": "ingest", "agent_id": "...", "message": "user message"}
stdout: {"type": "ingest_result", "memories": [{"content": "..."}]}

after_turn hook

stdin:  {"type": "after_turn", "agent_id": "...", "messages": [...]}
stdout: {"type": "ok"}

Current Plugins (10)

Plugin Hooks Description
auto-summarizer ingest, after_turn Running conversation summary for long context compression
context-decay ingest, after_turn Time-based memory decay with relevance scoring for natural forgetting
conversation-logger after_turn Logs conversations to JSONL files for auditing and analytics
episodic-memory ingest, after_turn Episode-based conversation segmentation and cross-session recall
guardrails ingest Safety filter detecting PII, prompt injection, and credential exposure
keyword-memory ingest Extracts keywords and named entities as contextual memories
sentiment-tracker ingest Analyzes user sentiment and injects emotional context
todo-tracker ingest, after_turn Detects, persists, and recalls action items from conversations
topic-memory ingest, after_turn Topic-aware keyword clustering with cross-conversation context recall
user-profile ingest, after_turn Persistent user profiling from conversation patterns for personalization

Adding a New Plugin

  1. Create plugins/<name>/plugin.toml
  2. Add hook scripts in hooks/
  3. List dependencies in requirements.txt (prefer stdlib-only)
  4. Run python scripts/validate.py
  5. Submit a PR

See CONTRIBUTING.md for the full guide.