Slang Workflows

███████╗██╗      █████╗ ███╗   ██╗ ██████╗██╔════╝██║     ██╔══██╗████╗  ██║██╔════╝███████╗██║     ███████║██╔██╗ ██║██║  ███╗╚════██║██║     ██╔══██║██║╚██╗██║██║   ██║███████║███████╗██║  ██║██║ ╚████║╚██████╔╝╚══════╝╚══════╝╚═╝  ╚═╝╚═╝  ╚═══╝ ╚═════╝     provable, declarative multi-agent workflows for Claude Code     a non-LLM executor runs your typed .slang files

slang-workflows

Run provable, .slang-driven multi-agent workflows inside Claude Code.

A non-LLM state machine (the slang executor) runs inside an MCP server and coordinates agents; each agent is a Claude Agent SDK session. You declare the collaboration in a typed .slang file and the executor enforces it — typed output contracts, static analysis, tool-scoping, and provable termination — instead of leaving coordination to the model. The top-level Claude Code session only triggers and observes; it never makes a coordination decision.

Claude Code's native dynamic workflows also codify orchestration (Claude writes a JS script); slang's difference is that the structure is enforced and statically analyzable, and runs render as Mermaid topology + trace diagrams. See the benchmark for the A/B/C head-to-head.

Origin. The .slang Workflow engine was born in Shofer — the AI-agent VS Code extension — where the deterministic, non-LLM executor was first built. This plugin brings it to Claude Code.

Design and rationale: DESIGN.md. Language reference: slang_specs.md. Privacy: PRIVACY.md.

[slang-workflows running implement-feature.slang deterministically — the guaranteed review loop, 0 coordination LLM tokens, converged]

How it works

flowchart TB    You(["You"])    CC["Claude Code<br/>top-level session"]
    subgraph server["slang-workflows · MCP server"]        direction TB        SL["your .slang workflow<br/>agents · stakes · contracts · converge"]        EX["slang executor<br/>deterministic · non-LLM state machine"]        MB[("FlowState + mailbox")]        SL -->|"parsed once"| EX        EX <--> MB    end
    subgraph Agents["Agent SDK sessions — the agents your .slang declares"]        direction LR        A1["Agent A"]        A2["Agent B"]        A3["Agent …"]    end
    You <-->|"ask · approve escalations"| CC    CC -->|"run_workflow"| EX    EX -->|"topology + trace diagrams"| CC    EX ==>|"dispatch stake — one session per agent, resumed"| Agents    Agents ==>|"typed result → output-contract check + retry"| EX    EX -.->|"escalate @Human"| CC
    classDef brain fill:#eaeaff,stroke:#5b5bd6,stroke-width:2px;    class EX brain;

The top-level Claude Code session only triggers and observes — it never makes a coordination decision. The non-LLM executor reads your .slang file, dispatches each stake to an Agent SDK session (one long-lived session per agent, resumed across rounds), checks every result against its output contract (retrying on failure), routes it through the mailbox, and repeats each round until the workflow's converge condition or round budget is met. Runs render as Mermaid topology and trace diagrams; escalate @Human surfaces back to you in the normal chat.

Use cases

Concrete workflows (each is a .slang file you run with run_workflow):

• Implement a feature with a human design-approval gate (implement-feature.slang) — an Architect decomposes your request and writes the design doc, but is scoped so it physically cannot write code (write_paths: ["**/*.md"], deny: [Bash]) and must delegate. You approve the design (escalate @Human), then a Developer implements it slice-by-slice while a Reviewer signs off each round, with a final review gate before it commits.
• Ship a feature as separate, verified deliverables (implement-feature-complex.slang) — a linear Design → Implement → Test → Review → Document pipeline where five specialists each produce one artifact (the implementation, a passing vitest spec, usage docs) and can't do another's job — so code, tests, and docs actually match the design. Converges only when every stage has committed.
• Troubleshoot a bug with two independent investigators (debug.slang) — paste the symptom/repro; two developers root-cause it in parallel, strictly read-only (no accidental edits), an Orchestrator consolidates their independent findings into one fix plan, one developer implements it, and the other peer-reviews the fix in a loop until satisfied.
• A reviewer that can inspect but never edit — give the review agent read/execute and no write scope, so a "check my work" run can run tests and read code but cannot alter it — enforced, not just prompted.

Features

• Reproducible multi-agent pipelines — the collaboration is codified in a .slang file and driven by a deterministic (non-LLM) executor, so a run unfolds the same way every time — no improvised, unrepeatable subagent coordination.
• Enforced per-agent tool-scoping — write_paths restricts each agent's Write/Edit to path globs and deny removes tools (e.g. Bash), enforced via the SDK's canUseTool — not merely requested in a prompt.
• Static analysis before running — validate_workflow detects deadlocks, unknown references, and orphaned outputs at parse time, before any tokens are spent.
• Typed output contracts — each stake must return a structurally and semantically valid result (output: {…} where <expr>); invalid results retry instead of silently propagating downstream.
• Provable termination — round budgets (budget: rounds(N)) + per-stake timeouts guarantee every run finishes.
• Auto-generated diagrams — every run renders a Mermaid topology (get_topology) and a sequence-diagram trace (get_trace), for live or post-mortem inspection.
• Convergence-driven collaboration — agents route via mailboxes and iterate until a declared convergence condition or budget is reached, with session resume so an agent keeps its context across rounds.

How this differs from Claude Code's native dynamic workflows

Claude Code already has a built-in dynamic workflows feature: when a task needs orchestration, Claude writes a JavaScript script (via the Agent SDK / Workflow tool) that spawns and coordinates subagents. That's a real step up from improvised, one-off subagent calls — the script codifies the orchestration and, once written, runs deterministically and coordinates for ~0 extra LLM cost. slang shares those goals; our benchmark shows both approaches reach real, working implementations with near-zero coordination-LLM cost. The difference is what the orchestration is, and what's guaranteed about it:

In short: native dynamic workflows put the orchestration in LLM-written code you have to trust; slang puts it in a typed declaration the runtime validates and enforces — analyzable before it runs, scoped and contract-checked while it runs, and rendered as diagrams after. Reach for slang when you want guarantees and auditability (safety-scoped agents, provable termination, contract-valid hand-offs, a reusable versioned workflow), not just "the model coordinated some subagents this time." Both are far better than unstructured subagents — slang trades a bit of up-front declaration for enforcement and repeatability.

What works today

• Discover / validate / run .slang workflows — authored or LLM-generated inline (MCP tools below).
• Deterministic executor: stake → output-contract validation + retry → mailbox routing → convergence; multi-agent flows; session resume (one agent = one session across stakes); escalate @Human.
• Output contracts: structural (output: {...}, via SDK outputFormat) + semantic (where <expr>).
• Enforced tool scoping: write_paths (Write/Edit restricted to path globs, via a PreToolUse command hook) and deny (remove native or MCP tools).
• Static analysis (validate_workflow): deadlock / unknown-ref / orphan-output detection before running.
• Provable termination: budget: rounds(N) + per-stake timeouts — the run always finishes.
• Diagrams: get_topology (Mermaid flowchart) + get_trace (Mermaid sequence + event log).
• Synchronous or background runs (background:true) with live polling of state/topology/trace.

See DESIGN.md § Implementation Status for the full matrix.

Requirements

• Node.js 22+ and pnpm.
• Claude Code installed and authenticated (the claude CLI on your PATH) — the Agent SDK spawns it to run agents.

Install

cd serverpnpm install

Agent SDK note. @anthropic-ai/claude-agent-sdk is declared as an optional dependency because some internal registries don't mirror it. If pnpm install skips it, add it from the public registry:

bash

pnpm add @anthropic-ai/claude-agent-sdk --registry=https://registry.npmjs.org/

The server parses/validates workflows without it; running agents requires it.

Verify the install:

pnpm typecheck   # type-checks the whole serverpnpm test        # runs the unit suite (mock-based, no model calls)

Use it

At runtime the server discovers .slang files in your project's .claude/workflows/ (and ~/.claude/workflows/) — that's the user's space for their own workflows. The plugin ships showcase workflows in server/test/fixtures/; copy them in to try them:

mkdir -p .claude/workflowscp /PATH/TO/slang-orchestrator/server/test/fixtures/*.slang .claude/workflows/

Quickest: register the MCP server with Claude Code

From the project whose .claude/workflows/ you want to run:

claude mcp add slang-workflows -- npx tsx /ABSOLUTE/PATH/TO/slang-orchestrator/server/src/main.ts

Then in Claude Code, ask it to use the tools — e.g. "list the slang workflows", "run the where-clause workflow".

As a Claude Code plugin

This directory is a self-contained plugin: .claude-plugin/plugin.json

• .mcp.json (a stdio server launched as npx tsx ${CLAUDE_PLUGIN_ROOT}/server/src/main.ts). Install it through Claude Code's plugin mechanism to expose the tools automatically.

Slash commands

Once the plugin is loaded, these commands drive it directly (Claude Code namespaces them by the plugin, so your / menu shows slang-workflows:…):

They're thin wrappers over the MCP tools below — you can always just ask Claude in natural language instead (e.g. "run the where-clause workflow").

MCP tools

Generate-and-run loop: get_slang_grammar → author slang → validate_workflow{source} → run_workflow{source, background:true} → poll get_topology / get_trace while it runs. The workflow is authored by an LLM but executed deterministically (contracts enforced, always terminates). @Human escalation works in synchronous runs only (interactive elicitation needs the tool call to stay open).

Develop

pnpm dev        # run the server over stdio (logs to stderr)pnpm typecheck  # tsc --noEmitpnpm test       # node:test suite

The executor depends only on a Dispatcher interface; FakeDispatcher makes the whole VM testable without the Agent SDK or any model calls (see server/test/).

Layout

.claude-plugin/plugin.json   plugin manifest.mcp.json                    stdio MCP server declarationserver/  src/    main.ts                  MCP server + tool surface    constants.ts             tunable defaults in one place (round cap, retry budget, stake timeout, loop guard)    executor.ts              deterministic round loop + output contracts    dispatcher.ts            Dispatcher interface + FakeDispatcher (the agent-runtime seam)    agent-sdk-dispatcher.ts  production backend (Claude Agent SDK)    tool-group-map.ts        slang tool-groups → Claude Code tools    workflows.ts             .slang discovery + validation    slang/                   vendored, framework-agnostic slang VM (lexer/parser/interpreter/…)  test/                      node:test unit + conformance suite    fixtures/                showcase .slang workflows (also the conformance fixtures)

License

Apache-2.0. See LICENSE.