Meeting Notes Agent

Turn a meeting recording into a concise summary, action items, and a follow-up Q&A agent using three stock primitives.

Quick Start

Simple Usage

Transcribe a recording, then produce notes.

from praisonaiagents import Agent, AudioAgent

transcript = AudioAgent(llm="openai/whisper-1").transcribe("meeting.mp3")

notes = Agent(
    name="Meeting Notetaker",
    instructions="Produce a concise summary, key decisions, and action items with owners.",
).start(f"Transcript:\n\n{transcript}")

print(notes)

Add Q&A

Save the transcript to disk and index it so an agent can answer follow-up questions.

with open("meeting_transcript.txt", "w") as f:
    f.write(transcript)

qa = Agent(
    name="Meeting Q&A",
    instructions="Answer using only the indexed meeting transcript. Cite what was said.",
    knowledge=["meeting_transcript.txt"],
)
print(qa.start("What were the action items and who owns them?"))

How It Works

Three primitives run in sequence: transcribe, summarise, then answer questions.

Stage	Primitive	Purpose
Transcribe	`AudioAgent.transcribe()`	Convert audio to text (Whisper / any LiteLLM STT provider)
Summarise	`Agent(...).start()`	Extract summary, decisions, action items
Q&A	`Agent(knowledge=[...])`	RAG over the transcript for follow-up questions

User Interaction Flow

Drop a recording into the folder, run the script, read the notes, then keep asking questions.

from praisonaiagents import Agent, AudioAgent

transcript = AudioAgent(llm="openai/whisper-1").transcribe("meeting.mp3")

with open("meeting_transcript.txt", "w") as f:
    f.write(transcript)

qa = Agent(
    name="Meeting Q&A",
    instructions="Answer using only the indexed meeting transcript.",
    knowledge=["meeting_transcript.txt"],
)

while True:
    question = input("\nAsk about the meeting (or 'quit'): ")
    if question.strip().lower() in {"quit", "exit"}:
        break
    print(qa.start(question))

Full Example

The complete script composes all three primitives end to end.

Full script
Shortened

from praisonaiagents import Agent, AudioAgent

TRANSCRIPT_PATH = "meeting_transcript.txt"


def transcribe_meeting(audio_path: str) -> str:
    """Convert a meeting recording to text with Whisper."""
    audio = AudioAgent(llm="openai/whisper-1")
    return audio.transcribe(audio_path)


def write_notes(transcript: str) -> str:
    """Summarise the transcript into notes and action items."""
    notetaker = Agent(
        name="Meeting Notetaker",
        instructions=(
            "Produce a concise summary, key decisions, and action items with "
            "owners and due dates. Only use what is in the transcript."
        ),
    )
    return notetaker.start(f"Transcript:\n\n{transcript}")


def build_qa_agent(transcript: str) -> Agent:
    """Index the transcript so an agent can answer follow-up questions."""
    with open(TRANSCRIPT_PATH, "w") as f:
        f.write(transcript)

    return Agent(
        name="Meeting Q&A",
        instructions="Answer using only the indexed meeting transcript. Cite what was said.",
        knowledge=[TRANSCRIPT_PATH],
    )


def main():
    transcript = transcribe_meeting("meeting.mp3")

    print("=== Notes ===")
    print(write_notes(transcript))

    qa = build_qa_agent(transcript)
    print("\n=== Q&A ===")
    print(qa.start("What were the action items and who owns them?"))


if __name__ == "__main__":
    main()

from praisonaiagents import Agent, AudioAgent

transcript = AudioAgent(llm="openai/whisper-1").transcribe("meeting.mp3")

print(Agent(
    name="Meeting Notetaker",
    instructions="Summarise decisions and action items with owners.",
).start(f"Transcript:\n\n{transcript}"))

with open("meeting_transcript.txt", "w") as f:
    f.write(transcript)

qa = Agent(name="Meeting Q&A", knowledge=["meeting_transcript.txt"])
print(qa.start("What were the action items and who owns them?"))

Choosing an STT Provider

Pass the provider string to AudioAgent(llm=...). Start with Whisper, switch for speed or accuracy. See Audio Overview, Groq, and Deepgram for setup and exact model strings.

Common Patterns

Save notes, batch a folder, or persist memory across meetings.

Save to Markdown
Batch a directory
Persist with Memory

from praisonaiagents import Agent, AudioAgent

transcript = AudioAgent(llm="openai/whisper-1").transcribe("meeting.mp3")
notes = Agent(
    name="Meeting Notetaker",
    instructions="Summarise decisions and action items with owners.",
).start(f"Transcript:\n\n{transcript}")

with open("notes.md", "w") as f:
    f.write(notes)

import glob
from praisonaiagents import Agent, AudioAgent

audio = AudioAgent(llm="openai/whisper-1")

for path in glob.glob("recordings/*.mp3"):
    transcript = audio.transcribe(path)
    transcript_file = path.replace(".mp3", ".txt")
    with open(transcript_file, "w") as f:
        f.write(transcript)

    qa = Agent(name="Meeting Q&A", knowledge=[transcript_file])
    print(path, "->", qa.start("Summarise the action items."))

from praisonaiagents import Agent, AudioAgent

transcript = AudioAgent(llm="openai/whisper-1").transcribe("meeting.mp3")

with open("meeting_transcript.txt", "w") as f:
    f.write(transcript)

qa = Agent(
    name="Meeting Q&A",
    instructions="Answer from the transcript and remember past meetings.",
    knowledge=["meeting_transcript.txt"],
    memory=True,
)
print(qa.start("How does this meeting compare to last week's?"))

See Memory to persist context across meetings.

Best Practices

Keep the transcript on disk

knowledge=[...] indexes files by path, so write the transcript to disk before passing it.

Match the model to the task

Use a cheaper LLM for summarising long transcripts and a stronger one for Q&A accuracy.

Redact PII before persisting

Meeting audio often contains personal data — strip sensitive details before saving transcripts.

Instruct the notetaker clearly

Tell the notetaker to use only the transcript so it does not invent action items.

Knowledge

Index files for retrieval-augmented Q&A.

Audio Overview

Speech-to-text providers and setup.

Meeting Minutes Action Items

Text-transcript recipe variant.

Memory

Persist context across meetings.

Getting Started

Install

Core Concepts

Guides

Features

Models

Databases

Observability

Memory

Telemetry & Analytics

Knowledge

RAG

Persistence

Tools

Other Features

Developers

Configuration

Best Practices

Getting Started (No Code)

Meeting Notes Agent

Quick Start

How It Works

User Interaction Flow

Full Example

Choosing an STT Provider

Common Patterns

Best Practices

Knowledge

Audio Overview

Meeting Minutes Action Items

Memory

​Quick Start

​How It Works

​User Interaction Flow

​Full Example

​Choosing an STT Provider

​Common Patterns

​Best Practices

​Related

Knowledge

Audio Overview

Meeting Minutes Action Items

Memory

Quick Start

How It Works

User Interaction Flow

Full Example

Choosing an STT Provider

Common Patterns

Best Practices

Related