We came across your repo and we like how you are creating a framework to bridge the gap between human interactions and AI capabilities in video conferencing. We scanned the repo, and noticed agent runtime reliability findings that might be worth reviewing.

1. [MEDIUM] LangChain tool returns its output directly, bypassing the model
   File: examples/client_example.py
   What it means: This tool sets return_direct=True, so LangChain returns the tool's raw output straight to the caller and stops the agent loop — the model never sees the result, cannot validate or summarize it, and any output guardrail or post-processing step is skipped.

2. [MEDIUM] Mutating tool has no idempotency key
   File: joinly/server.py
   What it means: The tool name signals a side effect (create/send/refund/…), but there's no way to ensure repeated invocations don't lead to unintended duplicate actions if the context isn't perfectly managed.

Recommendations are based on our understanding of agent runtime reliability, some findings may be intentional. Please let us know if this was intentional or if our findings are helpful so we can improve the accuracy of the scanner.

Best,
Trustabl.ai
Open-source AI agent reliability scanner (runs locally, GitHub Action)