librefang-registry/plugins

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.