Where do I obtain my Together AI API Key?

Log in to the developer portal via `api.together.xyz/settings/api-keys`. If you do not have an existing key, click **Create API Key**. This token enables the execution of remote inferences spanning their hosted clusters securely.

Do I have to pay to use Together models through the agent?

Yes. This connector simply routes your instructions to Together AI. Any tokens consumed during chat completion, embeddings, images generation, or fine-tuning workloads are billed directly to your registered Together AI account balance according to their official compute pricing models.

Can I access free models on Together AI?

Yes! Together AI frequently offers free tiers for certain open-source models intended for experimentation and research. You can query these directly from your agent without depleting your account balance, though specific free-tier rate limits will apply.

Together AI MCP Server for CrewAI 7 tools — connect in under 2 minutes

Name: Together AI
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 7 Tools Framework

Connect your CrewAI agents to Together AI through Vinkius, pass the Edge URL in the `mcps` parameter and every Together AI tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Together AI Specialist",
    goal="Help users interact with Together AI effectively",
    backstory=(
        "You are an expert at leveraging Together AI tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Together AI "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 7 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Together AI MCP Server

Connect your Together AI account to any AI agent and integrate bleeding-edge open-source models seamlessly into your workflow. Harness world-class inference speeds to query Llama, Mixtral, and more, or orchestrate specialized model fine-tuning jobs straight from your chat environment.

When paired with CrewAI, Together AI becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Together AI tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Model Discovery — Explore and list all currently supported models on the Together network, identifying the best engine for any NLP or vision task
Conversational AI — Run chat completion cycles on advanced models simply by supplying a model ID directly from the chat prompt
Vector Storage Preparation — Generate instant rich embeddings for input texts, ready to populate your analytical databases
Creative Media — Instruct external diffusion models to generate images using detailed physical descriptions
Custom Fine-Tuning — Provision custom training runs by indicating a base framework and dataset file, alongside tracking existing job statuses

The Together AI MCP Server exposes 7 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Together AI to CrewAI via MCP

Follow these steps to integrate the Together AI MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 7 tools from Together AI

Why Use CrewAI with the Together AI MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Together AI through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Together AI + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Together AI MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Together AI for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Together AI, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Together AI tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Together AI against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Together AI MCP Tools for CrewAI (7)

These 7 tools become available when you connect Together AI to CrewAI via MCP:

chat_completion

Provide a model ID and a JSON array of messages. Executes a chat completion using Together AI models

create_finetune_job

Provide a base model ID and a training file ID. Creates a new fine-tuning job

generate_embeddings

Provide a model ID and a JSON array of strings. Generates vector embeddings for input texts

generate_image

Provide a model ID and descriptive prompt. Generates an image from a text prompt

list_available_models

Lists all AI models available on Together AI

list_finetune_jobs

Lists all fine-tuning jobs

text_completion

Provide a model ID and a prompt. Executes a base text completion

Example Prompts for Together AI in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Together AI immediately.

"List all the models currently available on Together AI."

"Generate an embedding array using model `togethercomputer/m2-bert-80M-8k-retrieval` for the sentence 'The cat sat on the mat'."

Troubleshooting Together AI MCP Server with CrewAI

Common issues when connecting Together AI to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Together AI + CrewAI FAQ

Common questions about integrating Together AI MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.