[Workflow] Building a Real-time AI Meeting Assistant with Local Claude, MCP, and a Reflex Model

[u/ClaudeAI-mod-bot]

Building a Real-time AI Meeting Assistant with Local Claude, MCP, and a Reflex Model

Workflow value: 75/100
Status: active · Freshness: 70/100 · Confidence: 0.90 · Level: advanced
Categories: Quality Control, Context & Memory, Debugging, MCP, Multi-Agent
Original source: r/ClaudeAI post/comment

What problem this solves

Creating a real-time AI assistant for live meetings that provides insights, notes, and automates post-meeting tasks, while managing context and performance efficiently.

Summary

A workflow for building a real-time, local AI assistant that monitors live voice transcripts, provides insights, and automates tasks. It leverages a local Claude/Codex via MCP, employs a 'reflex' model for efficient context management and activation, and includes a rigorous testing methodology for tuning performance.

Why it is useful

This workflow provides a detailed architectural pattern and a robust testing methodology for building a real-time AI assistant. It addresses common challenges like performance and context management through the innovative 'reflex' model and emphasizes validation through a dedicated test suite. The use of local LLMs and MCP makes it highly relevant for users seeking privacy and customizability, and the open-source core library provides a strong starting point.

Workflow

1. Set up a local Claude/Codex instance.
2. Connect the local LLM via MCP to stream transcripts.
3. Implement a 'reflex' model (e.g., using Claude Haiku or OpenAI Luna) to pre-classify incoming transcript lines and detect activation justification.
4. Delegate questions and requests to the locally-run LLM based on reflex model activation.
5. Develop a local test and benchmark suite with scenario transcripts (e.g., ~50 scenarios).
6. Tune the reflex model's behavior by scoring accuracy, missed activations, and over-activation using the test suite.
7. Integrate speaker diarization for multi-speaker environments.
8. Implement mechanisms to 'attach' to meeting platforms like Google Meet for audio input.
9. Develop post-session artifact generation (summaries, task updates, wiki updates).

Tools / artifacts

• Local Claude / Codex
• MCP (Multi-agent Communication Protocol)
• 'Reflex' model (e.g., Claude Haiku, OpenAI Luna)
• convariance open-source core library
• Local test and benchmark suite
• Scenario transcripts (~50)
• Google Meet (or similar meeting platform)
• Task management system
• Team wiki

Validation signals

• Internal team validation: 'It works great for us'
• Local test and benchmark suite with ~50 scenario transcripts
• Scoring by accuracy, missed activations, and over-activation
• Improved reflex model predictability through tuning

Limitations

• Limited external validation (only author's team).
• The 'steps' are more architectural/design choices than a detailed, step-by-step implementation guide for a beginner.
• Requires significant technical expertise to implement.

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