open-prose

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What this is

OpenProse is a declarative language for standing AI work. Instead of scripting a sequence of instructions and hoping the run lands where you wanted, you declare the world as it should be: an ideal world state, written as familiar structured Markdown contracts (*.prose.md). You say what must stay true, and the system works out how much model work it takes to keep it that way. When order, loops, or exact choreography genuinely matter, optional imperative ProseScript plans drop in. Declarative by default, imperative where you want the control.

This is the oldest good idea in software, pointed at agents. SQL, Terraform, Kubernetes, React: you declare the desired state, and a reconciler is responsible for making reality match it. A thermostat is the one-sentence version. You set the temperature you want and it holds the room there; you never tell it when to fire.

OpenProse is a language, not a platform. The contracts are plain files that run on any Prose-Complete agent harness (any agent host that can spawn sessions, read and write files, and call tools): the language ships as a Skill your coding agent runs directly, and Reactor, built in this repo, is the deterministic runtime we recommend as the fast path.

Run it where your agent lives

Two on-ramps, one language: install the skill to author and run contracts inside your agent session (this section), or jump to Reactor for the deterministic runtime and its keyless proof. Same contracts either way; you can do both.

The fastest way to touch the language is the skill:

npx skills add openprose/prose

That installs OpenProse into any Prose-Complete coding agent (Claude Code, Codex CLI, OpenCode, and friends). From there, point your agent at a contract and say prose run <file>: the session itself embodies the VM; there is no separate binary. The examples/ directory is the tour; start small and read the contract before you run it.

Your first contract is a Markdown file away: kind: responsibility frontmatter, a ### Goal that states what should stay true, and the sections below. The skill teaches your agent the rest.

The language: Responsibilities

You author Responsibilities, standing goals written as Markdown contracts. This is where the declaration becomes concrete:

• ### Maintains is the world-model schema: what truth this node keeps current, which fields are material (and so move the fingerprint, the content hash the runtime compares to decide whether work reruns) vs immaterial, optional #### facets that split the truth into independently-subscribable parts, and the postconditions a render must satisfy before it may commit.
• ### Requires names the upstream facets this node subscribes to. Forme (the wiring layer) matches Requires.<facet> ↔ Maintains.<facet> and draws the subscription edge. Structure is subscription; the graph wires itself from the contracts.
• ### Continuity declares the wake source: input-driven by default, self-driven on a freshness cadence, or external-driven (a gateway turning an ingress event into an edge).

Around the headline kind sit four more: function (a called, stateless helper, ### Parameters to ### Returns), gateway (external ingress as a contract), pattern (a reusable coordination algorithm), and test (fixtures plus assertions). Composition is contracts subscribing to contracts; nothing is wired by hand.

The deep truth lives in the skill (skills/open-prose/) and the spec (spec/): the vocabulary, the section grammar, the execution semantics. This page is the door, not the manual.

Coming from OpenProse (v0.14 or earlier)? Read this first.

The Intelligent-React overhaul (runtime_contract 1 → 2) is a breaking vocabulary change. The headlines:

• The judge loop is retired wholesale. The old judge → verdict → pressure → fulfillment loop is gone, replaced by a deterministic reconciler: a render runs only when a node's subscribed input fingerprints or its own contract fingerprint move. There is no LLM in the wake/commit decision.
• Kinds renamed/deleted. kind: service is renamed to kind: function (### Parameters → ### Returns); kind: system is deleted (composition is now intra-node ProseScript call or cross-node subscription, wired by Forme); kind: responsibility is reshaped into a mounted DAG node that gains ### Requires + ### Maintains. ### Ensures is renamed to ### Maintains (now the world-model schema, not just an output list); ### Criteria/### Memory/### Fulfillment fold in.
• Old ledgers are abandoned, not migrated. Existing runtime data (old ReceiptV0 ledgers, the policy registry, bundled runs//state//dist/) is greenfield: there is no data migrator. Only your source text upgrades. Re-run from a clean state-dir.
• Upgrade your source with a dry run first. prose upgrade --dry-run (a prose skill command, run inside an OpenProse session, not the reactor CLI) inspects your files and reports the concrete migration plan without editing: mechanical rewrites where safe, surfaced as manual-review diagnostics where judgment is needed (e.g. a system/### Wiring flatten-or-split). Run it before prose upgrade.

Nothing is held hostage

The contracts in this repo are harness-agnostic: OpenProse Markdown runs on any Prose-Complete agent host (a fresh git clone is a first-class experience). The contract is the public artifact; the deployment's secrets and data stay private. A contract and its trail can leave for any compliant host with no lost meaning. OpenProse stays free, MIT, and portable, forever.

Reactor, below, is the harness built to run that contract well. We strongly encourage authoring your OpenProse with Reactor; the language never requires it.

Reactor: the recommended harness

Declare the world as it should be; Reactor keeps it true.

Reactor (@openprose/reactor) is the deterministic runtime for OpenProse. It holds a composed world-model (your declared truth, on disk), watches the world for change, and re-renders only the declared facets whose inputs actually moved (memoized agent sessions wired into a DAG). Every decision leaves a content-addressed receipt behind. It lives in this repo at packages/reactor/, with the full API reference in the SDK README.

Reactor compiles a contract set once (intelligently: Forme topology, a per-node canonicalizer, postcondition validators, all frozen), then runs it forever (dumbly: compare fingerprints, skip / render / propagate). The reconciler that decides whether to wake is deliberately deterministic: there is no judge step. The memo key has no clock in it. A failed render leaves the prior truth standing, and a failed receipt records why; only a rendered, moved fingerprint propagates. And because the reconciler acts only when reality drifts from what you declared, the bill follows real change, not the clock:

Inference cost that scales with surprise, not wall-clock time.

In plain terms: you declare what should stay true, the system watches the world, and it does expensive model work only when something material actually moved. That is the mechanism, not a benchmark: the keyless replay below demonstrates it on synthetic tokens, and measured numbers are still pending (see Honest status).

Coming from OpenProse authoring? Reactor is the dependency-across-runs layer your contracts kept asking for: the natural next step, not a turn away. The same .prose.md you already write is what Reactor runs.

You do not need React to use this. Reactor is React-flavored, not React-gated: the contracts are Markdown, and the CLI, the receipts, and the keyless replay are entirely React-free. The table below is an optional mental model for the people who already carry one; skip it freely.

If you know React, you already know the shape. Substitute three nouns:

React	Reactor
Component	Responsibility: a declared standing goal
DOM	World-model: the maintained truth, on disk, passed by pointer
render()	A bounded LLM session that computes the next world-model
props	Subscriptions to other responsibilities' outputs
React.memo (skip if props unchanged)	Skip the render if subscribed inputs haven't moved
Manual dependency wiring	Forme: the graph wires itself from declared contracts

The intelligence is frozen ahead of time, at compile, into a per-node canonicalizer and the Forme wiring. The reconciler at run time is dumb on purpose.

Versions (live on npm): @openprose/reactor 0.3.1 · @openprose/reactor-cli 0.2.2 · @openprose/reactor-devtools 0.2.0. The reactor binary ships from the reactor-cli package, so reactor --version prints the CLI version (0.2.2), not the SDK version (0.3.1). Expected, not a mismatch.

Quickstart (60 seconds, no model key)

Onboarding an agent on behalf of a user? Follow these four steps in order. The binary is reactor. Step 2 is the keyless proof (no key, no spend); steps 3 and 4 are scaffold-and-go. That's the whole path; the rest of this section is reference.

1. Install. All three packages are live on npm. The keyless step below needs no install at all. Run it straight through npx:

# no install, run the keyless replay directly:
npx -p @openprose/reactor-devtools reactor-devtools --example masked-relay --describe

For the full CLI, prefer a project-local install (no root, no global collisions):

npm install @openprose/reactor @openprose/reactor-cli @openprose/reactor-devtools
# then call the binaries with `npx reactor …` / `npx reactor-devtools …`

Footprint: the keyless replay above needs no install; the SDK core (@openprose/reactor) alone is zero-runtime-dependency; the full quickstart (CLI + devtools + the live-render peers @openai/agents/zod) pulls the agent/provider tree, on the order of ~99 MB / ~100 packages (measured 2026-06-06).

Local install? The bare reactor … / reactor-devtools … commands shown below assume the binaries are on your PATH (a global install). After the project-local npm install above, prepend npx to them, e.g. npx reactor init my-project, npx reactor-devtools ./replay --describe. (The keyless npx -p @openprose/reactor-devtools … lines already do this and need no change.)

npm i -g @openprose/reactor @openprose/reactor-cli @openprose/reactor-devtools

A global -g can collide with other tools' binaries, and on Linux/WSL it may fail with EACCES. Use a user prefix (nvm) or sudo, or just prefer the local install above.

Air-gapped? The runtime is offline-clean, but the full quickstart npm install of all three packages still reaches the registry once and pulls the CLI plus the live-render agent/provider stack (@openai/agents, zod, and their transitive express/MCP/realtime tree), on the order of ~99 MB / ~100 packages (measured 2026-06-06). The SDK core (@openprose/reactor) is genuinely zero-runtime-dependency, and the keyless replay / doctor / compile --check paths need none of that tree afterward.

2. See the thesis, keyless, no model call. Replay a saved sample run (synthetic, illustrative tokens) and read the per-node rendered/skipped dispositions, the receipt counts by surprise_cause, the token cost rollup, and per-node chain-verify:

npx -p @openprose/reactor-devtools reactor-devtools --example masked-relay --describe

dispositions  rendered=46 · skipped=31 · failed=0
surprise-cause  external=8 · input=69   (a.k.a. wake-cause)   ← receipt COUNTS, 77 total

COST ROLLUP  (tokens)
  total       fresh=27180 tokens · reused=12840 tokens · reuse=32%
    external  receipts=  8 fresh=   1080 tokens reused=840 tokens
    input     receipts= 69 fresh=  26100 tokens reused=12000 tokens
CHAIN-VERIFY ok

The surprise-cause line counts receipts by what woke them (8 external + 69 input = the 77 total receipts); the cost rollup below it is the actual token spend: fresh tokens are what each surprise cost, reused is what memoization saved (32% of the would-be tokens). That's "cost scales with surprise", checkable, with no key and no spend. Frames where a memo-skip happened show as skipped moved[—] fresh 0.

Prefer the browser? Drop --describe: reactor-devtools --example masked-relay boots an animated DAG viewer at a localhost URL: nodes flash on render, dim-pulse on memo-skip, with a live cost meter.

3. Scaffold and inspect, keyless. Everything here runs offline:

# local install? prepend: npx reactor … (see the "Local install?" note above)
reactor init my-project && cd my-project
reactor doctor                          # what's present + the exact fix for anything missing
reactor compile --check; echo "exit=$?" # offline; exits 1 if the contract set is STALE (CI-wireable)

Author your OpenProse contracts as *.prose.md files under the scaffold's src/, a kind: responsibility per standing goal (its ### Maintains / ### Requires / ### Continuity), optional gateways for ingress, optional functions for stateless helpers. reactor compile runs Forme over them.

4. Go live (needs a model key). These steps reach the model surface: set OPENROUTER_API_KEY and the two optional peers; a keyless reader can stop at step 3.

npm i -g @openai/agents zod          # the two optional live peers
reactor compile                      # Forme wires the DAG; freezes per-node canonicalizers
reactor serve --http 8080            # drive the scaffold's static gateway to a real receipt
reactor-devtools .reactor --describe # replay YOUR live run's ledger

Use reactor serve (not reactor run) to drive a scaffold's static gateway: serve ingests its seeded items; run is for graphs whose connectors emit on their own.

The SDK: one call, then go deeper

Reactor is a real SDK you plug into your own stack, not a closed product. The public API is a curated front door: import { reactor } from "@openprose/reactor". One call takes a directory of .prose.md contracts all the way to a booted, reconciling reactor and hands back one typed Reactor handle.

import { reactor } from "@openprose/reactor";

// Compile ./my-project, assemble a durable reactor over ./state, boot to a fixpoint
// (cold nodes render once; warm nodes memo-skip), hand back a live handle.
const { reactor: r } = await reactor("./my-project", { directory: "./state" });

console.log(r.ledger.all().length); // the receipt trail
await r.ingest("source", { wake: { source: "external", refs: [] } });

That's the front door. The deeper surface lives behind six reasoned subpaths: . (the facade + the vocabulary a driver needs), /agents (the full @openai/agents escape hatch, every render knob passes through), /adapters (the substrate + record/replay injection seam), /run and /run/types (the offline boundary), and /internals (the engine room). The full API reference is the SDK README and the docs site; this page just opens the door.

The example library

The examples in skills/open-prose/examples/ are the tour of the language: each carries its contract source and a README with its standing goal and DAG sketch. The thirteen below are replayable keyless, driving the real reconciler at zero model spend, through two paths: the six marked with * ship bundled inside reactor-devtools and replay by name from any directory (no clone needed), and the full thirteen are exercised by the repo's offline example corpus at tests/open-prose/examples/, six of which ship a committed, chain-verifiable replay/ state-dir you can open directly. (Two of the thirteen, masked-relay and tamper-forge, share a byte-identical ledger: tamper-forge is an audit lens over the masked-relay receipts, so the set is twelve distinct datasets plus one honest tamper-evidence lens, not thirteen unrelated ledgers.)

Example	What it shows	Domain
surprise-cost *	memoized skip → surprise-render when the memo key moves	the core thesis
renewal-risk	a standing responsibility re-checking only the accounts that moved	SaaS / finance
inbox-triage *	diamond fan-in + failure isolation	email / ops
monorepo-ci *	hub fan-out blast radius; a failing test blocks the merge gate	dev tooling / CI
research-tree *	recursive propagation up a tree, branch-memoized	research
masked-relay *	peer-blind fan-out with deterministic masked projections	competitive intel
agent-observatory *	many cheap watchers → batched synthesis	agent ops
tamper-forge	attack a real ledger; watch chain-verify catch it (and where it honestly can't)	audit / security
oblique-weave	hidden-context adversarial roles	product strategy
github-star-enricher	per-entity fan-out + shared receipts + a human gate	growth / GTM
implementation-pipeline	fixed wide fan-out with per-facet lane wake	software delivery
forme-fixpoint	the topology as a responsibility (the self-wiring bootstrap)	meta
basic-unit-suite	the 13 micro-mechanics, one by one	substrate

Run any starred example, keyless, from anywhere (no clone, no install):

npx -p @openprose/reactor-devtools reactor-devtools --example surprise-cost --describe

Or open a committed replay directly (from a clone of this repo):

reactor-devtools tests/open-prose/examples/renewal-risk/replay --describe   # the render/skip/cost trail
reactor --state-dir tests/open-prose/examples/renewal-risk/replay receipts  # the per-node ledger (list | verify | cost)

Installed from npm, not a repo clone? The skill and its example contracts ship inside the SDK tarball at node_modules/@openprose/reactor/skill/open-prose/examples/<name>/ (note: skill, singular, in the tarball; skills, plural, in the repo), and the starred examples replay by name from any directory with the --example command above.

Or run the offline example corpus (this is what CI runs, zero spend):

REACTOR_OFFLINE=1 pnpm test:examples

To take one live, cd into its dir and run reactor doctor → compile → topology → run → serve with a key set. Each example's README.md carries its standing goal, DAG sketch, and the full flow.

The technical report

The full architecture write-up (the React metaphor that is the design, the Forme wiring, the receipt model, an honest RLM accounting, and why nothing on the market is a drop-in replacement) is the Reactor technical report.

Honest status

In the spirit of the receipts:

• The language: the skill ships at version 0.15.0, runtime_contract 2; the spec (spec/) and the example corpus are migrated to the current vocabulary. The overhaul is recent: if you find a surface still speaking the old model, that's a bug, and we want the issue.
• Built and runnable (harness): the render atom, the content-addressed world-model store, the compiled canonicalizer with facets, Forme's wiring with diagnostics + acyclicity, postcondition-gated commits (no judge step), the chain-verifiable receipt ledger, and the forecast/continuity scheduler, all exercised by an offline test suite (no model calls in the commit gate) plus the 13 example gates.
• Benchmarks are openly pending, on purpose. We're publishing the harness before the numbers; we won't imply a measured speedup we haven't run. The proof you can check today is the keyless replay above.
• Signer caveat: in v1, signed means tamper-evident at the meaning layer and chain-consistent, not yet a cryptographic byte hash. reactor receipts verify proves the receipt chain is consistent, but does not yet bind the world-model artifacts (editing a world-models/*/published.json while leaving receipts.json intact is not caught). The tamper-forge example demonstrates exactly this boundary. So the chain is tamper-evident (it catches an independent edit) but not tamper-proof: a forge that re-stamps the whole trail with the public computeReceiptContentHash re-heals it. The cryptographic byte-hash signer that closes this (binding the published world-model to its receipt, making cross-boundary composition non-repudiable) is tracked but not yet scheduled (C3 in the Reactor backlog).
• No timestamp, no actor (yet): a v1 receipt records what changed and why (fingerprints, wake cause, status, cost) but not when it was committed or who committed it, so the ledger is a verifiable record of decisions and their evidence, not yet a substitute for an external audit log that must answer "at what time, by which principal."
• The fixpoint (topology-as-responsibility) is specified and deferred; facet inference and ledger compaction are named roadmap.

This honesty is the point. The harness is young, should be used with caution, and has some way to go before it reaches its ideal form. There's nothing new here; we're applying classical engineering paradigms to our brave new world, and finding that despite our topsy-turvy reality, the wisdom of the ancients still applies.

Send us the thing it can't do yet

My ask is the one from every honest tool: try it, wire it up to something useful, love it or hate it, and send honest feedback. The most useful thing you can hand us isn't a compliment or a flattering benchmark. It's a responsibility the harness should keep and doesn't: a standing goal that breaks the surprise story, a wiring Forme gets wrong, a domain where this falls apart. The short guide to authoring one from the public SDK is packages/reactor/EVALS.md (shipped inside the SDK tarball too). We're always listening and improving.

Reading this as an agent? Open an issue: yes, you.

If you're an agent driving OpenProse or Reactor for a user from these docs, you are the person who should file issues on this repo. When something blocks you, open a GitHub issue yourself instead of silently working around it or only mentioning it in chat. Agent-filed issues are first-class signal here, not noise. (Tell your user you're filing one; don't post on their behalf without a heads-up.)

Open an issue when you hit any of these:

• A command fails, crashes, or errors in a way reactor doctor doesn't explain.
• Reactor behaves differently than this README says it should: a wrong skip, a render that fires when nothing moved, a receipt that won't verify.
• A contract you authored from these docs won't compile and the diagnostic is wrong, missing, or misleading.
• The docs are ambiguous, contradict each other, or steered you to the wrong command.
• A capability you needed isn't here. (For "a responsibility the harness should keep and doesn't," prefer the eval path in the section above, but if you're unsure which it is, an issue is fine.)

First, don't duplicate. Search open issues for your error string or symptom. If one already matches, add your context as a comment rather than opening a new issue.

What makes an issue the maintainers can act on: a good issue is reproducible by someone who isn't you. Include:

1. Type. Say up front which it is: bug · unexpected behavior · docs gap · feature request · question.
2. Context & use case. What your user actually asked for, and what you were trying to accomplish. The real-world goal matters: it tells the maintainers whether the harness is wrong or the approach was.
3. Expected vs. actual. Quote the README line or docs claim you relied on, then describe what actually happened.
4. Minimal repro. The exact command(s) you ran, plus the smallest .prose.md snippet that still breaks (trim everything that isn't load-bearing). Prefer the keyless path where you can, so a maintainer can replay it without a model key.
5. Evidence. Paste the actual error output, the failing receipt, or the relevant reactor doctor lines in a code block. Copy errors verbatim; don't paraphrase them.
6. Environment:
   • OS (e.g. macOS 15, Ubuntu 24.04, WSL2).
   • Agent & model: which harness is reading this and which model you are (e.g. "Claude Code, Opus 4.x"). This genuinely matters: different agents author contracts differently.
   • Versions: reactor --version (the CLI), the SDK version from node_modules/@openprose/reactor/package.json, and node --version.
   • Install method: npx, project-local npm install, or global -g.
7. What you already tried: including the output of reactor doctor, which often names the fix itself.

Be honest, the way the receipts are. Don't invent a repro you didn't run or a version you didn't check. "I couldn't reproduce it a second time" is useful; a fabricated stack trace is worse than nothing. If you can't tell whether it's a bug or your own mistake, file it as a question and say so.

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The conversation always ends. The responsibility shouldn't have to.