Bring Usage Based Billing
to CrewAI
Learn how to connect Lago to CrewAI and start using 12 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 Lago MCP Server?
Connect Lago to your AI agent to automate your metering and billing infrastructure. Lago is the open-source alternative to Stripe Billing, designed for complex usage-based pricing models.
What you can do
- Customer Management — Create and update customer profiles with
upsert_customerand retrieve details withget_customer. - Subscription Lifecycle — Assign plans to customers using
create_subscriptionand monitor them withget_subscription. - Billing Infrastructure — Define billing plans with
create_planand set upcreate_billable_metricto track consumption. - Usage Tracking — Send real-time usage data with
send_eventorbatch_eventsto trigger accurate billing. - Financial Operations — Manage wallets, apply coupons, and list invoices to keep your revenue operations running smoothly.
How it works
- Subscribe to this server
- Enter your Lago API Key
- Start managing your billing logic from Claude, Cursor, or any MCP-compatible client
Who is this for?
- SaaS Founders — quickly check customer subscription status or update plans without leaving the chat.
- Growth Engineers — automate the creation of coupons and wallets for promotional campaigns.
- Finance Teams — list invoices and verify billable metrics through natural language queries.
Built-in capabilities (12)
Apply a coupon to a customer
Send a batch of usage events
Create a billable metric
Create a coupon
Create a new billing plan
Assign a plan to a customer (create subscription)
Create a wallet for prepaid credits
Retrieve a customer by external ID
Retrieve a subscription by external ID
List all invoices
Send a usage event
Requires an external_id. Create or update a customer in Lago
Why CrewAI?
When paired with CrewAI, Lago becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Lago 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
Lago in CrewAI
Lago and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Lago 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 Lago in CrewAI
The Lago 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 12 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
Lago for CrewAI
Every tool call from CrewAI to the Lago 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 track a new usage event for a customer?
Use the send_event tool. You'll need to provide the customer's external ID and the metric code to record the consumption in real-time.
Can I create a new billing plan with specific charges?
Yes, use the create_plan tool. You can define the name, interval, and base amount, as well as pass a JSON object for specific charges.
Is it possible to apply a discount to a customer?
Absolutely. First, create a coupon using create_coupon, then use the apply_coupon tool to link it to a specific customer's external ID.
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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