How to Use the Fireworks AI MCP in OpenAI Agents SDK

Q: Can I use Fireworks AI to handle transcription in OpenAI Agents SDK?

Yes, the transcribe tool is built into the server. You send your audio files through the tool call, and the agent receives the text output automatically.

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Connect Fireworks AI MCP to OpenAI Agents SDK

Create your Vinkius account to connect Fireworks AI 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 Fireworks AI with OpenAI Agents SDK

Ask AI about this MCP

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Claude

Perplexity

Direct access to high-speed inference

Your agent connects to the Fireworks AI API to run `chat` and `completion` tasks. You skip the latency overhead of heavy middleware and pipe tokens straight into your agent's reasoning loop. This setup keeps your production agent responsive. By using the `list_models` tool, you keep your client aware of available model versions without manual updates.

Multimodal agent capabilities

Expand your agent's reach using the `image` and `transcribe` tools. You can generate assets or process audio input within the same execution flow as your primary text logic. These operations happen as standard tool calls. Your OpenAI Agents SDK handles the orchestration while the MCP Server manages the heavy lifting of the underlying model endpoints.

Vector-ready agent state

Use the `embed` tool to turn raw text into vectors for your RAG pipelines. It plugs directly into your existing agent memory management. This creates a clean path for data retrieval. You generate embeddings on the fly, ensuring your agent always has fresh context for its current task.

Setup guide

Set up Fireworks AI 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 Fireworks AI tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Fireworks AI 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="Fireworks AI Agent",
            instructions="You have access to Fireworks AI tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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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 Fireworks AI. 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 Fireworks AI MCP in OpenAI Agents SDK

You install the server via your package manager and pass it to the Agent constructor using `mcp_servers`. The tools auto-discover immediately, letting you call them without extra boilerplate.

It does. You get consistent token output speeds that beat standard API providers. The server is designed for high-concurrency environments where every millisecond counts.

Yes, the `transcribe` tool is built into the server. You send your audio files through the tool call, and the agent receives the text output automatically.

Data flows directly between your agent and the API endpoints. No intermediary logs or storage are kept by the server itself, ensuring your inputs remain private during the request lifecycle.

The server returns standard error codes that your agent catches. You can wrap these calls in your own retry logic to keep the agent running smoothly.

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