How to Use the Frame.io MCP in LangChain

Q: Can LangChain agents read Frame.io video comments?

Yes. The agent calls listassetcomments to read feedback. It can then pass that text into other tools in your chain.

Connect the Frame.io MCP Server to LangChain to build multi-step video review pipelines.

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Connect Frame.io MCP to LangChain

Create your Vinkius account to connect Frame.io to LangChain 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 LangChain

Ask AI about this MCP

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Claude

Perplexity

LangChain MCP Server for Video Chains

The `list_review_links` and `list_asset_comments` tools let LangChain agents pull active review sessions and immediately fetch attached feedback. You build chains where the output of a review link query dictates which comment thread the agent reads next. LangSmith tracing shows exactly how long your agent takes to parse feedback. If a client drops fifty notes on a cut, the ReAct agent reads them and decides which editor gets the revision assignment based on the text.

Automate Technical QA

The `add_comment` tool in this MCP integration lets your agent drop timestamped notes directly onto video files. Your LangChain setup evaluates audio levels in the background and writes a note right at the 01:14 mark if the track clips. You don't have to log these changes by hand. The ReAct agent checks `get_asset_details` to confirm the video resolution, then formats the necessary technical corrections back into the timeline.

Map Production Folders

Use `list_teams` and `list_projects` to give your agent a map of your production environment. LangChain traverses the entire directory tree by feeding project IDs directly into `list_folders`. This turns a messy post-production drive into structured data. The agent finds the exact rough cut you need without clicking through endless nested folders.

Setup guide

Set up Frame.io MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Frame.io tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "frameio-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Frame.io transactions"
    })
    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 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 LangChain

Use pip install langchain-mcp-adapters and initialize a MultiServerMCPClient. Pass the Vinkius endpoint URL to connect.

Yes. The agent calls `list_asset_comments` to read feedback. It can then pass that text into other tools in your chain.

Yes. The agent looks at the available tools and decides whether to pull project details or add a comment based on your prompt.

LangSmith lets you trace the execution steps. You can set a maximum iteration limit on your ReAct agent to stop endless API calls.

The server feeds asset metadata and raw comment text directly to your client. LangChain handles the memory state locally or in your configured backend. Vinkius runs the connection in an ephemeral V8 sandbox, destroying the environment after the session closes.

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