Bring Computer Vision
to CrewAI
Learn how to connect Roboflow to CrewAI and start using 29 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Roboflow MCP Server?
Connect Roboflow to your AI agent to streamline your computer vision pipeline. From dataset management to model training and inference, handle your entire CV lifecycle through natural language.
What you can do
- Workspace & Project Management — List projects, create new ones, or fork from Roboflow Universe to jumpstart your development.
- Dataset Operations — Upload images (via URL or Base64), manage versions, and download datasets in various formats like COCO or YOLO.
- Model Training — Start training runs, monitor results, and retrieve precise performance metrics (mAP, precision, recall) for any version.
- Image Search — Search and filter images within your workspace to audit your data and improve model accuracy.
- Inference & Results — Run inference on images and retrieve results to verify model behavior in real-time.
How it works
- Subscribe to this server
- Enter your Roboflow Private API Key
- Start building and managing vision models from Claude, Cursor, or any MCP-compatible client
Who is this for?
- ML Engineers — monitor training progress and dataset health without leaving the terminal or IDE.
- Data Scientists — quickly query dataset versions and export data for custom training scripts.
- Product Teams — audit model performance and visualize inference results through simple conversation.
Built-in capabilities (29)
Add projects to a folder (Enterprise)
Start an auto-labeling job using foundation models
Cancel an active training job
Assign a batch of images to a labeler and reviewer
Create a project folder (Enterprise)
Create a new project in a workspace
Delete multiple images from a project
Delete a project or version (moves to Trash)
Retrieve a download link for a zipped dataset in a specific format
Fork a public project from Roboflow Universe
Track long-running operations like forking or large exports
Check dataset health (class distribution, missing annotations, etc)
Get details for a specific image
Get project details, metadata, and versions
Verify authentication and retrieve default workspace
Retrieve metrics and status for a version training run
Retrieve metadata for a specific dataset version
List project folders in a workspace (Enterprise)
List items in the workspace trash
List information about a workspace and its projects
Add, remove, or set tags on an image
Restore an item from the trash
Run inference on an image using hosted models
Search and filter images within a specific project
Search and filter images within a workspace
Start training a model on a dataset version
Early stop an active training job
Attach an annotation file to an existing image
Upload an image to a project
Why CrewAI?
When paired with CrewAI, Roboflow becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Roboflow tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
- —
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
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CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the
mcpsparameter 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
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Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports
Roboflow in CrewAI
Roboflow and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Roboflow to CrewAI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Roboflow in CrewAI
The Roboflow 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. All 29 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in CrewAI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
Roboflow for CrewAI
Every tool call from CrewAI to the Roboflow MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How can I verify if my Roboflow API key is correctly configured?
You can use the get_root tool. It will attempt to authenticate with your key and return the default workspace details if successful.
Can I get the training performance metrics for a specific model version?
Yes! Use the get_training_results tool by providing the workspace, project, and version ID. It returns mAP, precision, recall, and other training metrics.
Is it possible to export my dataset to a specific format like YOLOv5?
Absolutely. Use the download_dataset tool and specify the format parameter (e.g., 'yolov5pytorch') to receive a download link for your zipped dataset.
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.
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.
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