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
50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
#!/usr/bin/env python3
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"""Auto-summarizer ingest hook.
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Returns the stored conversation summary as a memory fragment so agents
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maintain awareness of prior conversation context.
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Receives via stdin:
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{"type": "ingest", "agent_id": "...", "message": "user message text"}
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Prints to stdout:
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{"type": "ingest_result", "memories": [{"content": "..."}]}
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"""
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import json
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import os
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import sys
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def get_summary_path(agent_id):
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"""Return the path to the summary file for the given agent."""
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home = os.path.expanduser("~")
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return os.path.join(
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home, ".librefang", "plugins", "auto-summarizer", f"{agent_id}.summary"
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)
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def main():
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request = json.loads(sys.stdin.read())
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agent_id = request.get("agent_id", "unknown")
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memories = []
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summary_path = get_summary_path(agent_id)
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if os.path.isfile(summary_path):
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try:
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with open(summary_path, "r", encoding="utf-8") as f:
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summary = f.read().strip()
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if summary:
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memories.append(
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{"content": f"[summary] Conversation so far: {summary}"}
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)
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except (OSError, IOError):
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# If we cannot read the file, return no memories silently.
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pass
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print(json.dumps({"type": "ingest_result", "memories": memories}))
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if __name__ == "__main__":
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main()
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