Evan Hu
afcb260554
All plugins are stdlib-only Python with no external dependencies. - auto-summarizer: extractive conversation summary for long context compression, persists per-agent summaries to disk - conversation-logger: JSONL audit logs per agent with ISO 8601 timestamps, auto-creates log directory tree - guardrails: safety filter detecting PII (email, phone, SSN, CC), prompt injection patterns, and credential exposure via regex - keyword-memory: extracts entities (emails, URLs, dates, technical terms like camelCase/snake_case/dotted identifiers) as memories - sentiment-tracker: keyword-based sentiment scoring with intensifiers and negation handling, only injects context for non-neutral sentiment - todo-tracker: detects action items via 7 task patterns, tracks completion, deduplicates, persists per-agent with 20-item FIFO limit
conversation-logger
Logs all conversations to JSONL files for auditing, analytics, and debugging. Each agent gets its own log file at ~/.librefang/logs/conversations/{agent_id}.jsonl.
Log Format
Each line is a JSON object:
{
"timestamp": "2026-03-21T12:34:56Z",
"agent_id": "agent-abc123",
"turn_number": 5,
"message_count": 10,
"last_user_message": "truncated to 200 chars...",
"last_assistant_message": "truncated to 200 chars..."
}
Hooks
| Hook | Script | Description |
|---|---|---|
| after_turn | hooks/after_turn.py |
Appends a log entry after each conversation turn |
How It Works
After each conversation turn the hook extracts summary information from the messages array and appends a single JSON line to the agent's log file. User and assistant messages are truncated to 200 characters to keep log files manageable.
Errors from the filesystem (permissions, disk full, etc.) are caught silently so the agent conversation is never interrupted by a logging failure.
Usage
Installed automatically when enabled in agent configuration. Log files are created on first write -- no manual setup required.