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LLM connections

An LLM connection lets your Python services call Claude, OpenAI, Gemini or any self-hosted model behind one API. The provider's credentials stay in the platform, every call leaves an audit trail, and chat history, skills and GitOps come built in.

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AI gateway overview

The architecture-level view of LLM and MCP traffic in one platform

Creating connections

Define a connection in the Dashboard - provider, model, address, API key and limits

Invoking models

The one-shot invoke call from a Python service, and what the response contains

Calling models

Invoke - one-shot calls

Call a model from a service with self.llm, read the reply and the per-call token usage

Chat - multi-turn conversations

Conversations with managed history - the platform keeps each chat's turns and trims what goes to the provider

Skills

Reusable instructions sent as the system context of a call, shared with MCP gateways

Configuration and operations

Creating connections

Providers and models, addresses, API keys, pool size, timeout, token and history limits

GitOps with enmasse

Export and import the whole AI configuration as YAML across dev, test and production

Audit log

Every call is recorded with its outcome and duration

Agents calling your services

MCP gateways

AI agents calling your Python services as tools over MCP

AI gateway

LLM connections and MCP gateways as the platform's AI gateway

Schedule a demo

A one-on-one session covers LLM connections and MCP gateways end to end.

Schedule a demo