How to Use the Tavus Video Replicas MCP in AutoGen

Q: How do I use Tavus Video Replicas for collaborative content planning in AutoGen?

You assign one agent to pull initial data using listreplicas and another to draft the generation plan. They debate the optimal replica choice until a consensus is reached, which then triggers generatereplicavideo.

Q: What is the best way to audit usage of Tavus Video Replicas with AutoGen?

Have an auditor agent call both listgeneratedvideos and then systematically check details using getvideodetails on every item. The final output is a debate-verified report.

Q: If I need to know the status of generation, how does Tavus Video Replicas work with AutoGen?

One agent polls using getvideodetails, and another debates whether the current reported status is actually accurate based on historical records or known rate limits. This adds a layer of skepticism.

Facilitate multi-agent debate on Tavus Video Replicas using AutoGen's framework.

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Setup Tavus Video Replicas with AutoGen

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Debate Content Requirements

Set up a group of agents: one tasked with reviewing available replicas and another calling `list_replicas`. The debate can converge on the best replica to use for a new project. The output is a reasoned consensus, not just a list. This structured negotiation forces deliberation before you even call ``generate_replica_video``, ensuring all competing perspectives are considered.

Validate Video Integrity

You can build an agent pair: one calls `get_video_details` and reports the status. The second agent challenges that data point against a historical record retrieved via `list_generated_videos`. They debate if the video was actually completed or just marked as such. This consensus-driven process validates the state of your generated content before deployment.

Manage and Clean Assets

Assign agents roles for asset management. One agent might call `delete_replica` on a problematic voice, while another calls `get_replica_details` to confirm the deletion is irreversible. The final decision requires consensus before executing destructive actions. This deliberation layer adds necessary safety checks when dealing with critical resources like your video replicas.

Setup guide

Set up Tavus Video Replicas 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 Tavus Video Replicas 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="Tavus Video Replicas_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Tavus Video Replicas 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="Tavus Video Replicas_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Tavus Video Replicas 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 Tavus. 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 Tavus Video Replicas MCP in AutoGen

You assign one agent to pull initial data using `list_replicas` and another to draft the generation plan. They debate the optimal replica choice until a consensus is reached, which then triggers ``generate_replica_video``.

Yes. You can build a multi-agent flow where one agent flags an old video job using `get_video_details`, and another recommends deleting it via `delete_video` based on that flagged information.

Have an auditor agent call both `list_generated_videos` and then systematically check details using `get_video_details` on every item. The final output is a debate-verified report.

One agent polls using `get_video_details`, and another debates whether the current reported status is actually accurate based on historical records or known rate limits. This adds a layer of skepticism.

The system touches video job metadata, including replica IDs and generation status. Ensure your agents understand that this specific generation metadata must be handled with care.

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