Bring Multi Agent Systems
to Pydantic AI
Learn how to connect Relevance AI to Pydantic AI and start using 11 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 Relevance AI MCP Server?
Connect your Relevance AI account to any AI agent and take full control of your autonomous AI workforce and tool orchestration through natural conversation. Relevance AI provides a world-class platform for building and scaling multi-agent systems, and this integration allows you to trigger autonomous agents, execute custom studios (tools), and monitor long-running task histories directly from your chat interface.
What you can do
- Agent & Workforce Orchestration — List all available autonomous agents and trigger them to perform specific goals with dynamic inputs programmatically.
- Studio & Tool Intelligence — Access and monitor your custom AI 'Studios' and execute them with complex parameters directly from the AI interface.
- Task Lifecycle Management — Retrieve real-time progress for background tasks and monitor final outputs to ensure your autonomous workflows are always synchronized.
- Knowledge & RAG Control — List and search through your agent's knowledge base items and datasets via natural language.
- Operational Monitoring — Track system activity and manage regional deployments using simple AI commands.
How it works
- Subscribe to this server
- Enter your Relevance AI Region Code (e.g., bcbe5a) and API Key
- Start managing your autonomous AI teams from Claude, Cursor, or any MCP-compatible client
No more manual dashboard refreshing for task results. Your AI acts as a dedicated orchestrator for your entire agentic infrastructure.
Who is this for?
- AI Operations Managers — quickly retrieve task summaries and monitor agent deployments without switching tabs.
- Automation Engineers — automate the triggering of complex multi-agent workflows via natural conversation.
- Developers — integrate real-time task results and studio execution into custom business architectures.
Built-in capabilities (11)
Permanently delete a task record
Get metadata for an agent
Get details for a knowledge base
Check status and results of a task
List recent agent tasks
List all AI agents
List all agent execution history
List knowledge base items
List all studios/tools
Start an agent task
Execute a specific tool (Studio)
Why Pydantic AI?
Pydantic AI validates every Relevance AI tool response against typed schemas, catching data inconsistencies at build time. Connect 11 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
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Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Relevance AI integration code
- —
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your Relevance AI connection logic from agent behavior for testable, maintainable code
Relevance AI in Pydantic AI
Relevance AI and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Relevance AI to Pydantic AI 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 Relevance AI in Pydantic AI
The Relevance AI 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 11 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Pydantic AI 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
Relevance AI for Pydantic AI
Every tool call from Pydantic AI to the Relevance AI MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can my AI automatically trigger another autonomous agent in Relevance AI?
Yes! Use the trigger_agent tool. Provide the agent_id and the user message/goal, and your agent will initiate the autonomous workflow in your Relevance account instantly.
How do I find my Region Code and API Key?
The Region Code is in your dashboard URL (e.g., bcbe5a). For the API Key, log in to Relevance AI, navigate to Settings > API Keys, and generate a new secret key.
How does Pydantic AI discover MCP tools?
Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.
Does Pydantic AI validate MCP tool responses?
Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
Can I switch LLM providers without changing MCP code?
Absolutely. Pydantic AI abstracts the model layer. your Relevance AI MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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