Introduction

aiSlang is a declarative language (.ais) plus a single CLI (aislang) for building multi-agent AI systems. One file describes the whole system, and one CLI validates it, compiles it, and runs it — locally on your own machine or as a stack you deploy to your own cluster. The full .ais grammar is defined in the language reference (LANGUAGE-SPEC-v0.md).

Agent frameworks make it easy to demo one agent. Real systems compose many — a router picks a specialist, an orchestrator delegates, a reflector critiques and retries — and need model fallback, spend caps, tracing, secret scoping, and a real way to serve them. aiSlang makes the system the unit: you declare the agents and how they combine, then aislang apply ships it.

One file describes the whole system

A .ais project captures everything about a multi-agent system in one place:

  • Agents and how they compose — routing, orchestration, reflection, and other patterns.
  • Models by capability, not vendor lock-in: declare what a model must do (requires = [chat, tool_use], context_min, cost_max, latency, residency) and let the resolver pick a concrete model from a bundled catalog, with fallback chains and per-run / per-day budgets.
  • Prompts, versioned and hashed, with template variables.
  • MCP tools the agents can call.
  • RAG knowledge — vector stores and embeddings attached to an agent.
  • Evals — datasets, metrics, and pass thresholds.
  • Deploy targets — Docker Compose or Kubernetes.

What the CLI does

The aislang CLI is a compile-then-deploy tool, not an interpreter:

  • Validates and type-checks the .ais source, with source-aware diagnostics.
  • Resolves models against a bundled catalog and pins the choices in a lockfile for reproducibility.
  • Emits a stack — a Docker Compose or Kubernetes deployment made of a LiteLLM router, Jaeger tracing, budget enforcement, and your agents.
  • Runs chat and evals locally against the deployed stack.
  • Exposes its whole surface as an MCP server, so MCP-aware hosts like Claude Desktop or Cursor can author, deploy, and chat with your agents.

aislang remote --target can also talk to aiSlang Cloud, a managed control plane; these docs cover the local, self-hosted CLI only.

What these docs cover

  • Why aiSlang — the problem it solves and where it sits relative to existing tools.
  • Install — get the aislang binary and its prerequisites.
  • Quickstart — a running multi-agent stack in five minutes.
  • Language — the .ais language reference.
  • CLI reference — every subcommand and flag.
  • Deploy — Docker Compose and Kubernetes targets.
  • MCP — drive aiSlang from Claude Desktop, Cursor, and other MCP hosts.
  • Observability — OpenTelemetry traces, Jaeger, and external collectors.