How to Use the Frame.io MCP in AutoGen

Q: How do I load Frame.io tools into AutoGen?

Install the MCP extension using autogen-extmcp and run mcpservertools. Pass the resulting list to your AssistantAgent.

Q: Can AutoGen agents debate Frame.io video feedback?

Yes. One agent can pull notes via listassetcomments while another argues about which revisions take priority.

Use the Frame.io MCP Server with AutoGen agents to debate and manage video production workflows.

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Connect Frame.io MCP to AutoGen

Create your Vinkius account to connect Frame.io 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 Frame.io with AutoGen

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Frame.io MCP Server for AutoGen Debates

The `list_asset_comments` tool feeds directly into AutoGen conversation loops. A technical QA agent reads the notes, while a producer agent argues about the project timeline. They do not just execute commands. The agents negotiate whether to flag an asset for revision or approve it, using actual feedback data to back up their claims.

Automate Feedback Consensus

Give your agents the `add_comment` tool to log their final decisions. After debating the merits of a cut, the winning agent drops a timestamped note onto the video. This creates a hard audit trail. You get one agreed-upon revision list instead of fifty conflicting notes from scattered automated checks.

Audit Project Access

Security agents run `list_collaborators` and `list_teams` to verify who has access to unreleased footage. If a contractor is still on the project, the agent flags it. Another agent then checks `get_my_profile` to verify permissions. The AutoGen framework resolves the access dispute before any sensitive review links go out.

Setup guide

Set up Frame.io 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 Frame.io 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="Frame.io_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Frame.io 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="Frame.io_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Frame.io data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Frame.io. 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 Frame.io MCP in AutoGen

Install the MCP extension using autogen-ext[mcp] and run mcp_server_tools. Pass the resulting list to your AssistantAgent.

Yes. One agent can pull notes via `list_asset_comments` while another argues about which revisions take priority.

The framework shares the tool execution results in the conversation history. All agents in the chat see the returned project metadata.

Only if the agents reach a consensus to do so. They must explicitly call `add_comment` after their deliberation.

The MCP server exposes folder names, raw video metadata, and team directories. AutoGen manages the conversation history locally. Vinkius authenticates the endpoint with a single token and drops the session entirely once the script finishes.

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