Bring Llm Inference
to CrewAI
Learn how to connect Cerebras Inference to CrewAI and start using 15 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 Cerebras Inference MCP Server?
Connect to the Cerebras Inference platform to leverage the world's fastest AI inference. This MCP server allows your AI agent to interact with state-of-the-art models like Llama 3.1 and others using the Cerebras Wafer-Scale Engine (WSE) for unprecedented performance.
What you can do
- Chat & Text Completions — Generate high-speed responses using
create_chat_completionandcreate_completionwith support for streaming and tool calling. - Model Discovery — Explore available models and their specific details using
list_modelsandget_modelto choose the best fit for your task. - Batch Processing — Handle large-scale workloads asynchronously with
create_batch,list_batches, andcancel_batchfor efficient data processing. - File Management — Upload and manage JSONL files for batch jobs using
upload_fileandlist_filesdirectly from your agent. - Performance Metrics — Monitor your usage and performance metrics to optimize your inference workflows.
How it works
- Subscribe to this server
- Enter your Cerebras API Key
- Start generating tokens at speeds you've never seen before in Claude, Cursor, or any MCP-compatible client.
Who is this for?
- AI Developers — build and test applications with near-instant model responses to maintain development momentum.
- Data Scientists — run large-scale batch inference on massive datasets using the asynchronous batch API.
- Product Teams — integrate high-performance LLMs into production environments where latency is a critical factor.
Built-in capabilities (15)
Cancel a batch job
Create a batch job for asynchronous processing
Generate conversational responses using a structured message format
Generate text continuations from a single prompt string
Delete a file
Retrieve status of a batch job
Retrieve metadata for a specific file
Download raw content of a file
Retrieve Prometheus-formatted operational metrics
Fetches details for a specific model
List all batch jobs
List uploaded files
Lists all currently available models
Retrieve model details without an API key
Upload a JSONL file for Batch processing
Why CrewAI?
When paired with CrewAI, Cerebras Inference becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Cerebras Inference 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
- —
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
- —
Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports
Cerebras Inference in CrewAI
Cerebras Inference and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Cerebras Inference 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 Cerebras Inference in CrewAI
The Cerebras Inference 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 15 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
Cerebras Inference for CrewAI
Every tool call from CrewAI to the Cerebras Inference MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How do I check which models are available for inference?
Use the list_models tool. It will return a list of all supported models, including high-performance options like Llama 3.1, which you can then use in create_chat_completion.
Can I process thousands of requests at once?
Yes. Use upload_file to provide your JSONL data and then create_batch to start an asynchronous processing job. You can monitor progress with get_batch.
Does this server support tool calling and structured outputs?
Yes. The create_chat_completion tool supports tools, tool_choice, and response_format parameters, allowing the model to interact with other functions or return valid JSON.
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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