How to Use the Gladia (Speech AI) MCP in AutoGen

Q: How do AutoGen agents coordinate Gladia (Speech AI) transcription tasks?

One agent uploads the file with uploadaudiofile, while another triggers inittranscription. A third agent uses gettranscription to fetch and analyze the final text.

Q: Can AutoGen handle live audio streams using Gladia (Speech AI)?

Yes. Your agents call initlivesession to establish a WebSocket stream. This MCP Server tool provides the connection parameters for live multi-agent processing.

Q: How do I list past speech-to-text tasks in AutoGen?

Use listtranscriptions to retrieve the history. Your coordinator agent can parse this list to delegate review tasks to other agents.

Q: How do agents clean up files after processing?

Your cleanup agent calls deletetranscription using the job ID. This deletes the file from the remote server immediately after your agents finish their analysis.

Let your AutoGen agents debate, transcribe, and analyze live audio sessions using this Gladia Speech AI MCP Server.

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Connect Gladia (Speech AI) MCP to AutoGen

Create your Vinkius account to connect Gladia (Speech AI) to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Gladia (Speech AI) with AutoGen

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`get_transcription` pulls down the completed text payload for your agents to review. One AutoGen agent can analyze the raw text for accuracy, while a second agent formats the output. This collaborative AutoGen review ensures your Gladia transcription summaries are accurate. The AutoGen agents debate speech discrepancies before finalizing the text, reducing errors from noisy audio files.

Live session negotiation via this MCP Server

`init_live_session` opens a real-time WebSocket connection for live speech processing. Your AutoGen coordinator agent manages this connection, directing live text to specialized worker agents. While one AutoGen agent listens to Gladia's live feed, another agent evaluates system performance. This multi-agent setup ensures your real-time AutoGen pipelines remain stable under heavy audio loads.

Manage the audio lifecycle cooperatively

`upload_audio_file` sends your raw recordings to the server to begin the processing cycle. Your AutoGen storage agent executes this upload, then hands the reference to the transcription agent. Once the transcription agent finishes running `init_transcription` and extracts the text, a cleanup agent calls `delete_transcription`. This automated AutoGen loop keeps your speech workspace organized.

Setup guide

Set up Gladia (Speech AI) MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Gladia (Speech AI) tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Gladia (Speech AI)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Gladia (Speech AI) data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Gladia (Speech AI)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Gladia (Speech AI) data")
    print(result.messages[-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Gladia. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Gladia (Speech AI) MCP in AutoGen

One agent uploads the file with `upload_audio_file`, while another triggers `init_transcription`. A third agent uses `get_transcription` to fetch and analyze the final text.

Yes. Your agents call `init_live_session` to establish a WebSocket stream. This MCP Server tool provides the connection parameters for live multi-agent processing.

Use `list_transcriptions` to retrieve the history. Your coordinator agent can parse this list to delegate review tasks to other agents.

Your cleanup agent calls `delete_transcription` using the job ID. This deletes the file from the remote server immediately after your agents finish their analysis.

Your raw audio recordings and final text outputs are isolated inside Vinkius's V8 sandbox via the MCP pipeline. AutoGen interacts with the server through a single secure endpoint token, preventing credential exposure and keeping your data strictly private.

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