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Provable, declarative multi-agent workflows for Claude Code (typed .slang, non-LLM executor).
Provable, declarative multi-agent workflows for Claude Code (typed .slang, non-LLM executor).
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-shofer-dev-slang-workflows": {
"args": [
"-y",
"slang-workflows"
],
"command": "npx"
}
}
}From the project's GitHub README.
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
.slangWorkflow 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.
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.
Concrete workflows (each is a .slang file you run with run_workflow):
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.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.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.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..slang file and driven by a
deterministic (non-LLM) executor, so a run unfolds the same way every time — no improvised, unrepeatable
subagent coordination.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.validate_workflow detects deadlocks, unknown references, and
orphaned outputs at parse time, before any tokens are spent.output: {…} where <expr>); invalid results retry instead of silently propagating downstream.budget: rounds(N)) + per-stake timeouts guarantee every run finishes.get_topology) and a sequence-diagram
trace (get_trace), for live or post-mortem inspection.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:
| Native dynamic workflows | slang-workflows | |
|---|---|---|
| The orchestration is… | an LLM-authored JavaScript script | a typed, declarative .slang file run by a fixed non-LLM executor |
| Who wrote the coordination logic | Claude, per task, in a general-purpose language | you (or an LLM, once) in a domain-specific language the runtime understands |
| Output contracts between stages | whatever the script happens to check | enforced by the runtime — structural + semantic (output: {…} where <expr>); invalid → retry |
| Per-agent tool scoping | up to the script | enforced — write_paths / deny via the SDK's canUseTool |
| Correctness of the structure | nothing checks the JS | static analysis before running — deadlock / unknown-ref / orphan-output |
| Termination | up to the script | provable — budget: rounds(N) + per-stake timeouts |
| Observability | instrument it yourself | auto-generated Mermaid topology + sequence-diagram trace |
| The reusable artifact | a script the model regenerates each time | a versioned .slang file + a fixed interpreter |
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.
.slang workflows — authored or LLM-generated inline (MCP tools below).escalate @Human.output: {...}, via SDK outputFormat) + semantic (where <expr>).write_paths (Write/Edit restricted to path globs, via a PreToolUse
command hook) and deny (remove native or MCP tools).validate_workflow): deadlock / unknown-ref / orphan-output detection before running.budget: rounds(N) + per-stake timeouts — the run always finishes.get_topology (Mermaid flowchart) + get_trace (Mermaid sequence + event log).background:true) with live polling of state/topology/trace.See DESIGN.md § Implementation Status for the full matrix.
claude CLI on your PATH) — the Agent SDK
spawns it to run agents.cd server
pnpm install
Agent SDK note.
@anthropic-ai/claude-agent-sdkis declared as an optional dependency because some internal registries don't mirror it. Ifpnpm installskips it, add it from the public registry: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 server
pnpm test # runs the unit suite (mock-based, no model calls)
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/workflows
cp /PATH/TO/slang-orchestrator/server/test/fixtures/*.slang .claude/workflows/
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".
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.Once the plugin is loaded, these commands drive it directly (Claude Code namespaces them by the
plugin, so your / menu shows slang-workflows:…):
| Command | Does |
|---|---|
/slang-workflows:slang-run <name or path> [k=v …] | Run a workflow to completion — synchronous, so @Human gates prompt you. |
/slang-workflows:slang-list | List the .slang workflows it can see. |
/slang-workflows:slang-new <description> | Generate a workflow from a description, validate it, and run it. |
/slang-workflows:slang-trace [workflow_id] | Render the topology + sequence-diagram trace of a run. |
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").
| Tool | Purpose |
|---|---|
list_workflows | Discover .slang files (name, title, params, agent count). |
get_slang_grammar | Concise grammar cheatsheet + example, so an LLM can generate a workflow to run inline. |
validate_workflow | Parse + static analysis (deadlocks, unknown refs, orphan outputs) without running. Accepts a name/path or inline source. |
run_workflow | Run a workflow by name/path or inline source (rejects parse/static-analysis errors first). Synchronous by default; background:true returns a workflow_id immediately to poll live. |
get_workflow_state | Serialized FlowState (per-agent status, round, budget) by workflow_id — live during a background run. |
get_topology | Run topology as a Mermaid flowchart (status-colored snapshot) by workflow_id. |
get_trace | Execution trace as a Mermaid sequenceDiagram + raw event log (who staked/routed to whom, commits, escalations, terminal) by workflow_id. |
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).
pnpm dev # run the server over stdio (logs to stderr)
pnpm typecheck # tsc --noEmit
pnpm 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/).
.claude-plugin/plugin.json plugin manifest
.mcp.json stdio MCP server declaration
server/
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)
MIT.
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