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Frags runtime · open source, AGPL

The open-source runtime for AI backend services.

Frags executes FML Blueprints as typed, multi-session workflows — coordinating tools, scoped context, deterministic prework, and schema validation, so a prompt becomes a service you can call.

View on GitHubBrowse Blueprints
brew install fragshq/tools/frags
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frags · runtime
$ frags run revenue-brief.fml --llm claude ▸ loading Blueprint................. ok▸ required tools .............. apicp:salesforce, mcp:slack▸ session collect_accounts    queryAccounts → 38 records    accounts[] schema validated▸ session identify_risk    scoped context → accounts only    riskFindings[] schema validated▸ session notify_team    chat_postMessage → #revenue ✓ complete · typed output ready for the caller

Runtime engine

Built for workflows that need more than one prompt.

An agent framework hands you an autonomous black box and asks you to trust it. Frags hands you a typed, inspectable Blueprint you can read, diff, and version — the difference between hoping it worked and knowing it did.

Multi-session orchestration

Break a complex output into focused sessions. Each session owns a slice of the schema, can depend on earlier work, and can run only when its gate passes.

Scoped context by design

Frags controls what each model sees, so downstream sessions get the useful prior output without dragging the whole workflow into every prompt.

Schema-first output

Blueprints produce machine-consumable JSON that is validated against the declared schema. The runtime is built for services, not loose chat transcripts.

Bring the tools you need

Declare MCP servers, APICP-backed APIs, collections, or custom functions. Tool use stays explicit and local to the sessions that need it.

Deterministic work outside the model

Pre-calls, lightweight transforms, and output filters handle mechanical work before and after LLM calls to reduce cost and improve reliability.

CLI and Go library

Use Frags directly from the command line while prototyping, then embed the runtime into your own services when the workflow becomes production code.

Plot twist

Does building reliable AI automations make you want to say FML?

Good news — that’s just the name of the language. FML is a programming language for instructing LLMs, running on the Frags runtime. We own the whole vertical stack — language, runtime, and tooling — so the web editor and your IDE have no limits.

FML contract

Blueprints are executable specifications.

FML is the Blueprint language Frags interprets. It describes the workflow's shape, not just a prompt: caller parameters, declared tools, deterministic preparation, session order, context boundaries, and the final JSON Schema.

  • system(...)The shared system prompt for the Blueprint
  • parameter(...)Typed inputs supplied by the caller
  • require mcpThe MCP servers, APICP APIs, and functions it may use
  • session(...)A scoped LLM work unit with its own context and dependencies
  • use / call(...)Tool use and deterministic pre-calls inside a session
  • schema { }The typed output each session assembles
  • {{ ... }}Templating across parameters and prior sessions

Open source

Install Frags locally.

Frags is AGPL-licensed and fair-code distributed — the source is always visible, you can self-host it anywhere, and you can extend it with your own tools. No account required to get started.

Read the local setup guide
  • Source available

    The full runtime source is always visible on GitHub — read it, audit it, fork it.

  • Self-hostable

    Run the CLI or embed the Go library on your own infrastructure — no hosted dependency required.

  • Extensible

    Add your own nodes, tools, and MCP servers to fit blueprints to the systems you already run.

macOS · Homebrew
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brew tap fragshq/toolsbrew install fragshq/tools/frags
Windows · Scoop
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scoop bucket add fragshq https://github.com/fragshq/scoop-bucket.gitscoop install frags

Prefer a raw binary? Grab the latest CLI release from GitHub. Need role-based access, audit trails, or support at scale? See Enterprise.

Frags and APICP work together.

APICP narrows external APIs into clean MCP tools. Frags runs the Blueprint that decides when those tools are used, how their results are transformed, and which session owns each output field.

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Questions or bugs? Open a GitHub issue.