How to Use the Together AI Alternative MCP in OpenAI Agents SDK

Q: Can I run embeddings generation with Together AI Alternative using the OpenAI Agents SDK?

Yeah, sure thing. You simply call createembeddings within your agent's workflow. The engine takes raw text and turns it into vector embeddings that can be used for advanced search or retrieval tasks.

Q: What happens if I need to delete old files with Together AI Alternative and the OpenAI Agents SDK?

You can use deletefile to remove uploaded assets or cached data from the system. This gives you granular control over your storage resources, which is critical for compliance.

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Connect Together AI Alternative MCP to OpenAI Agents SDK

Create your Vinkius account to connect Together AI Alternative to OpenAI Agents SDK 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 Together AI Alternative with OpenAI Agents SDK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Handle multi-modal content creation.

Your agent can generate images or videos based on a prompt using tools like `create_image_generation` or `create_video_generation`. This means if the user asks for an asset, the agent doesn't just talk about it—it builds it. It also manages audio by transcribing files with `create_audio_transcription` and turning text back into speech using `create_audio_speech`. These functions let your deployed product handle everything from visual output to voice interactions.

Manage complex, asynchronous tasks.

When you need an operation that takes minutes—like a massive data processing job or batch inference—you use `create_batch` and then monitor it with `get_batch`. The agent sends the request to create a background task, allowing the conversation flow to continue immediately. It keeps track of all running jobs. You can check the status of every queued item by calling `list_batches`, which is crucial for maintaining system visibility in a production environment.

Maintain reliable access endpoints.

For mission-critical functions, you don't want random latency. The agent sets up predictable connections using `create_endpoint` and manages them with `update_endpoint`. This dedicated endpoint guarantees performance when the core chat model runs. When finished or if scaling is needed, the system cleans up by calling `delete_endpoint`. It also lets you check current connection details via `get_endpoint`, keeping your overall MCP Server configuration clean.

Setup guide

Set up Together AI Alternative MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Together AI Alternative tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Together AI Alternative tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Together AI Alternative tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Together AI Alternative Agent",
            instructions="You have access to Together AI Alternative tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Together AI Alternative MCP in OpenAI Agents SDK

This MCP Server touches file metadata and raw text content. Remember that while the agent handles the tools, you are responsible for how the underlying data is stored or passed to external services.

Yeah, sure thing. You simply call `create_embeddings` within your agent's workflow. The engine takes raw text and turns it into vector embeddings that can be used for advanced search or retrieval tasks.

Absolutely. Since you're building a product, the agent uses built-in guardrails to validate every action before it executes against this MCP Server. It’s designed specifically for safety-critical deployments.

You can use `delete_file` to remove uploaded assets or cached data from the system. This gives you granular control over your storage resources, which is critical for compliance.

Yes, it does. The agent handles the full lifecycle: uploading data via `upload_file`, starting the job with `create_fine_tune`, and checking progress using `get_fine_tune`.

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