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• Author: dbrockmann
• Requires: Python >=3.12

joinly-client: Client for a conversational meeting agent used with joinly

Prerequisites

Set LLM API key

Export a valid API key for the LLM provider you want to use, e.g. OpenAI:

exportOPENAI_API_KEY="sk-..."

Or, create a .env file in the current directory with the following content:

OPENAI_API_KEY="sk-..."

For other providers, export the corresponding environment variable(s) and set provider and model with the command:

uvxjoinly-client--llm-provider<provider>--llm-model<model><MeetingUrl>

Start joinly server

Make sure you have a running joinly server. You can start it with:

dockerrun-p8000:8000ghcr.io/joinly-ai/joinly:latest

For more details on joinly, see the GitHub repository: joinly-ai/joinly.

Command line usage

We recommend using uv for running the client, you can install it using the command in their repository.

Connect to a running joinly server and join a meeting, here loading environment variables from a .env file:

uvxjoinly-client--joinly-urlhttp://localhost:8000/mcp/--env-file.env<MeetingUrl>

Add other MCP servers using a configuration file:

{
"mcpServers":{
"localServer":{
"command":"npx",
"args":["-y","package@0.1.0"]
},
"remoteServer":{
"url":"http://mcp.example.com",
"auth":"oauth"
}
}
}

uvxjoinly-client--mcp-configconfig.json<MeetingUrl>

You can also set other session-specific settings for the joinly server, e.g.:

uvxjoinly-client--ttselevenlabs--tts-argvoice_id=EXAVITQu4vr4xnSDxMa6--langde<MeetingUrl>

For a full list of command line options, run:

uvxjoinly-client--help

Code usage

Direct use of run function:

importasyncio

fromdotenvimport load_dotenv
fromjoinly_clientimport run

load_dotenv()


async defasync_run():
 await run(
 joinly_url="http://localhost:8000/mcp/",
 meeting_url="<MeetingUrl>",
 llm_provider="openai",
 llm_model="gpt-4o-mini",
 prompt="You are joinly, a...",
 name="joinly",
 name_trigger=False,
 mcp_config=None, # MCP servers configuration (dict)
 settings=None, # settings propagated to joinly server (dict)
 )


if __name__ == "__main__":
 asyncio.run(async_run())

Or only using the client and a custom agent:

importasyncio

fromjoinly_clientimport JoinlyClient
fromjoinly_client.typesimport TranscriptSegment


async defrun():
 client = JoinlyClient(
 url="http://localhost:8000/mcp/",
 name="joinly",
 name_trigger=False,
 settings=None,
 )

 async defon_utterance(segments: list[TranscriptSegment]) -> None:
 for segment in segments:
 print(f"Received utterance: {segment.text}")
 if "marco" in segment.text.lower():
 await client.speak_text("Polo!")

 client.add_utterance_callback(on_utterance)

 async with client:
 # optionally, load all tools from the server
 # can be used to give all tools to the llm
 # e.g., for langchain mcp adapter, use the client.session
 tool_list = await client.list_tools()

 await client.join_meeting("<MeetingUrl>")
 try:
 await asyncio.Event().wait() # wait until cancelled
 finally:
 print(await client.get_transcript()) # print the final transcript


if __name__ == "__main__":
 asyncio.run(run())

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