GEML — General Expressive Markup Language

English | 中文

GEML is an Agent-Native fundamental markup format and protocol, designed for people and AI agents to read and write the same document. One format, two readers. In agent-driven development and knowledge work, plain text and Markdown have no deterministic block boundaries: a program and a model trade the whole file in and the whole file back out — at best probing for it with line windows, and restating the original verbatim to rewrite it. Token cost grows with the length of the document, and the operation turns bloated. After a few rounds of rewriting, the copies excerpted elsewhere start to drift.

You can start without changing a thing. geml list, geml find and geml get address the Markdown you already have — nothing is converted, no new files, your .md stays .md:

geml list    README.md                          # every section, as an address
geml get     README.md '#key-features'          # read ONE section, not the file
geml set     README.md '#key-features' --body   # write one section back
geml replace README.md 'old text' 'new text'    # swap a string, told which block held it

Only that section enters the agent’s context — a couple of KB, not the whole ~48 KB file.

Need finer than a section — one block, one chart, one table? Let .geml stand in the middle ground: edit at that grain, and the --to md you ship never drifts from it.

A block has a name; the things inside it have a coordinate. A table's cell, a data block's leaf, a key in meta — each has a coordinate the structure already gives it, and get and set land on exactly that value.

geml get doc.geml '#fy[2]["Q1"]'                     # one cell
geml set doc.geml '#intake["fields"][1]["name"]'     # one leaf in the JSON

For people, it is plain text that reads clean; for agents, it is an addressable, verifiable, traceable, revertible "Doc-as-a-Base".

GEML is minimal. It is plain text — still clean with no renderer in sight; one block syntax for the whole language; addressable, verifiable, referenceable structure, natively.

Instead of a separate mini-syntax for each kind of content, GEML carries every kind in one container: the typed block. Code is a block. So are tables, diagrams, math, callouts, even metadata — and a run of prose can be one too (=== text), whenever you want it addressable. Extending it later is just as plain. The shape is the same every time, which makes the language easy enough to learn that it's hard to get wrong.

=== code {#hello lang=python}
print("hi")
===

geml get doc.geml '#hello'   # by name, just this block

Blocks have names so the verbs have somewhere to land — the full syntax is in the format in 1 minute.

Contents: What it solves · Why now · What's different · The format in 1 minute · Profiles · A gift for programmers · Get hands-on · With an LLM · Maturity & versions · The design · Roadmap · Take part · License

What it solves

Problems solved

1. Context load and token bloat

   • Status quo: data formats like JSON/XML carry heavy wrapper tags and syntax symbols; Markdown lacks strict structural metadata and a reference mechanism.

   • Approach: tuned markup density and syntax overhead, reading and writing only the target block — context cost no longer grows with document length, keeping agent reads and writes lightweight.

2. AST-level precision and parsing determinism

   • Status quo: unstructured text degrades over multiple rounds of LLM reads and writes — broken formatting, semantic drift, parsing hallucinations.

   • Approach: a deterministic grammar that maps directly to an abstract syntax tree (AST), so programs and LLMs perform atomic block-level create/read/update/delete.

3. Document copy fragmentation

   • Status quo: multi-agent collaboration and shared pipelines pass content around by copy-paste, leaving multiple disconnected copies.

   • Approach: Single Source of Truth by design — standardized module references and data binding eliminate redundant copies and version divergence.

Key features

1. AST-level structured operations

• Uniform node definitions; a document parses directly into a typed document tree (AST).

• Agents pinpoint the target section, attribute or component; partial patches and idempotent updates replace whole-file rewrites. Writes land as byte splices with whole-document re-validation — the tree serves reading and validation, and every untouched byte is guaranteed unchanged.

2. Low-token reads and writes

• What is saved is not markup characters — it is the part never read: #id hits one semantically complete block, and the rest never enters the context.

• For the same semantics, markedly lower prompt-token cost: better model throughput, lower inference cost.

3. Single source of truth, modular references

• Native cross-document, cross-fragment component references.

• Change the source node once and every reference follows — no version skew.

4. Robust two-way reads and writes

• One block shape for the whole language — easy to generate and hard to get wrong, a good match for mainstream LLM output distributions.

• A strict validator with precise error locations and actionable repair feedback.

5. Profile-based domain extensibility

• Zero dialect chaos: extend domain vocabularies (e.g. design styles, interactive forms, code graphs, media timelines) through declarative profile metadata without inventing new syntax or breaking parsers.

• Static type and constraint checking with safe fallback to standard blocks in unknown environments.

Comparison

Related MCP server: RBT Document Editor

Why the LLM era needs a brand-new text format

Because both the producer and the consumer of a document have changed.

In traditional software engineering, a document was either a static explanation for people to read, or a serialized data file for programs.

Today, people and AI agents collaborate on the same document at high frequency. When the agent becomes the document's "second reader and co-author", the old balance breaks for good:

1. Context is scarce compute: every whole-document read or write burns an agent's limited attention window and reasoning budget;

2. Human–machine collaboration needs an isomorphic carrier: people need to read it at a glance, agents need to read and write it precisely, block by block;

3. Knowledge must have a single source of truth: scattered prompts and copy-pasted Markdown are destined to decay with every iteration.

Yet none of our existing text infrastructure was designed for this scene:

• Markdown (typeset for people): no stable structural blocks, no machine keys. To change one parameter, an agent must read and write the whole text — wasting context budget across multi-turn loops, and inviting drift in both format and meaning.

• JSON / XML (serialized for machines): full of wrapper syntax and structural noise — blocking natural human reading, while quietly eating expensive tokens in long contexts.

• Scratch memory and scattered files (no single source of truth): context is torn across chat history and Markdown copies everywhere; a copy is drift from the moment it is made, and version skew and hallucinated distortion follow.

The root of all three failures is each tool's own virtue: Markdown's "never error, write anything" is what gives people their freedom to write — and exactly why a machine cannot trust the structure it reads back; JSON/XML's strict schema is what gives machines their certainty — and exactly why nobody writes prose in it. The virtue is the defect, which is why patches cannot fix this: bolting "a broken reference must fail the build" onto Markdown betrays its contract, and stripping the wrapper syntax from JSON denies its nature. When people and agents start co-writing the same text at high frequency, what is needed is not a compromise between the two poles, but a format that treats "readable by people" and "operable by machines" as one design constraint from day one.

The answer: "Doc-as-a-Base"

GEML invents no heavy new runtime. Borrowing from the REST architectural style of Dr. Roy Fielding's dissertation, it gives plain-text documents one standard set of operational semantics:

A document no longer needs just a format — it needs a set of verbs. GEML keeps plain-text readability and adds deterministic block-level operations.

💡 Deep Dive: If you are interested in the dilemma of engineering documents in the LLM era and why we need to redesign a plain-text format from the ground up, read our full article on the blog: "Why Do We Need a New Text Format in the Era of LLMs?"

What's different about GEML

GEML stays small on purpose — the thinking, what it refuses, and what is still open are in how we thought about the design.

The four capabilities were established a chapter ago — addressing, projection, validation, rollback. This chapter is where each format lands against them, and where GEML draws its boundaries.

How other formats compare

Each of the four has mature solutions in its own field; what's unusual is meeting all four in one plain-text format:

Item by item: vs. CommonMark · vs. XML and JSON · a 7-format capability matrix.

Coexisting with Markdown: GEML is the editing source of truth, Markdown is the delivered artifact. Project one way with geml <file> --to md|html and ship .md or .html as before. Collaboration, not lock-in. (Projection is lossy: block ids and table-bound charts don't survive it.)

Don't take the table's word for it — re-run it. This is what I asked the model:

Based on your own experience editing the READMEs just now, describe the command steps you go through on a document (I saw you using grep and such), and whether you cache documents to save tokens — let's compare, and from that see which parts of GEML would actually earn their place.

What came back: what one edit costs and a real day replayed. Paste the question to your own model and see what it tells you. PS: I am still trying to work out whether the upstream chain (who calls this) and the downstream chain (what it calls) that codemap produces can pin down functions and call sites — and change project code — the same way. I will post a report when I have one.

The format in 1 minute

Typed blocks

One shape, every type. A block's basic syntax is === type [attributes] … === (where attributes like {#id .class key=val} are optional) — only the type (and how its body is read) changes:

=== code {lang=python}
print("hi")
===

=== note {.intro}
Parsed prose with *emphasis* and a [[#budget]] reference.
===

=== meta
title = "Budget plan"
===

A run of = (three or more) opens a block; an equal-length run closes it; longer fences nest inside shorter ones. A block that carries an #id can also close with the labeled fence === #id — no fence-length counting, which makes long blocks much harder to get wrong (nesting still requires a longer outer fence: a same-length bare === in the body closes the block early, labeled or not). The type decides how the body is read — raw (verbatim: code, diagram, math, table), flow (parsed prose with inline markup: note, text), or data (one key=val per line: meta); embed carries no body at all — its src= names the block it stands for — and every block may carry an attribute object {#id .class key=val}, where a .class is a semantic label, never a styling hook. The full inline grammar (emphasis, links, [[#id]] auto-references, media, footnotes, inline $math$) is in the spec.

Tables — two bodies, one model

Write a table visually:

=== table {#budget caption="Annual cost"}
| Plan  | Months | Rate |
|-------|-------:|-----:|
| Basic |      1 |   30 |
| Pro   |      2 |   30 |
===

…or as data. A table holds the facts; a view over it derives the computed columns and the summary row:

=== table {#fy25 format=csv header=1}
Segment,  Q1, Q2, Q3, Q4
Cloud,     8, 10, 12, 14
Platform,  5,  6,  7,  9
Services,  3,  4,  4,  5
===

=== view {#fy25-report src=#fy25 compute="FY [%.1f] = Q1 + Q2 + Q3 + Q4; n = 1" summary="Segment = 'Total'; FY [%.1f] = sum(FY); n = sum(n)"}
===

Both table forms describe the same model. The FY column and Total row are computed at build time, by the view:

compute runs + - * / ( ) per row over columns; summary adds a foot row from the aggregates sum / avg / min / max / count (with arithmetic over them, e.g. weighted ratios); a trailing [printf] sets numeric display. n above is the row-count idiom — count tallies non-empty cells in one column, so a constant column summed is what counts rows.

Tables can also pull their data from an external CSV via src="regions.csv".

❓ Up for discussion: should computed columns and the summary row stay? Keep, freeze, or drop — say which.

Math

=== math {#gauss caption="Gaussian integral"}
\int_{-\infty}^{\infty} e^{-x^2} dx = \sqrt{\pi}
===

$$\int_{-\infty}^{\infty} e^{-x^2} dx = \sqrt{\pi}$$

Diagrams & charts — host a DSL, or chart a table

GEML never interprets a diagram body; it routes it to a pluggable renderer (an unknown format is a warning, body preserved):

=== diagram {#flow format=mermaid caption="Review flow"}
graph LR
  A[Draft] --> B{Review} -->|ok| C[Publish]
===

graph LR
  A[Draft] --> B{Review} -->|ok| C[Publish]

A diagram can also chart a table — single source of truth, with the column references checked at build time and no data copied:

=== diagram {format=geml-chart data=#fy25-report type=bar x=Segment y=FY}
===

Drawn from the #fy25-report view above — FY is a computed column, so the chart binds to the view that derives it, not to the base table:

xychart-beta
  title "FY by segment"
  x-axis [Cloud, Platform, Services]
  y-axis "FY"
  bar [44, 27, 16]

Data — a value, not just text

Every block type names what it holds: code a region of code, table a grid, math a formula. data holds a data value, and it is where the data formats live — json (the default), jsonl, and yaml for a declared subset; toml reserved. Being typed means the body is read, not just displayed: a missing comma fails the build, geml get --json returns the value itself, and a chart can read it directly.

=== data {#log format=jsonl}
{"ts":"09:00","p95":41}
{"ts":"09:10","p95":58}
===

A jsonl body holds one record per line, which a program can blind-append at end-of-file. Records can also stay in their own file: src=ops/latency.jsonl#L900-999 names the file and, optionally, a line window — so the log keeps being appended and tailed as before, while the document is its verified, addressable, chartable view of it.

Embeds — a dynamic reference, not a copy

One block can stand for another: in the same document by src=#id, across documents by src=other.geml#id. An embed is a dynamic lookup of the source at render time — change the source once and every embed follows; delete it and geml check fails the build on the spot.

=== embed {src=#fy25}
===

The body stays empty; the target lives in src=.

Markdown can't show you the projection. To see it live: install the browser extension, open the raw link to sample.geml, and scroll to the Transclusion section — a same-document projection (src=#roadmap), cross-document projections, and even chained resolution (an embed pulls a chart, which itself binds to a table in another file) all render in place: nothing is written there, yet edit the source once and the projection follows.

Profiles — domain vocabularies, assembled like Lego bricks

Want to author interactive forms, define a design token system, or map an entire codebase's call graph inside your documents? In traditional Markdown, this requires proprietary plugins (:::note, custom JSX tags), inevitably fracturing into incompatible dialect silos.

GEML solves this with Profiles (Application-layer vocabularies, spec §8.6): A single-line declaration that unlocks domain-specific structured superpowers on demand.

=== meta
profile = "geml-style/v1 geml-form/v1"
===

=== form-field {#email label="Work email" type=email required pattern="[^@]+@acme\\.com"}
===

=== style-rule {#cta match="button.cta" bg="{{brand}}" radius="6px"}
===

Extension without fragmentation

• 🧩 Mix & match like Lego bricks The core syntax stays minimal and frozen, while domain capabilities expand infinitely. Call graphs, design tokens, form validation, version history... compose multiple domain vocabularies with one profile = "..." line.

• ⚡ Zero-plugin overhead with instant tooling support Adding a new domain block requires zero parser forks or custom plugins. Custom blocks instantly inherit the entire infrastructure: deterministic #id addressing, geml get/set blockwise mutation, CLI verbs, MCP protocol, and autonomous AI Agent control.

• 🛡️ Naturally portable, never locked in Extend capabilities without breaking interoperability. In any third-party or unfamiliar processor, documents maintain 100% structural integrity and block-level addressability, ending the nightmare of broken formatting when switching tools.

Standard Published Profiles

💡 Want to see Profiles in action? • geml-media live demo: One cut document and one command (geml media build ep01-cut.geml --out ep01.mp4 --burn-subs) orchestrates ffmpeg to align audio/video, mix tracks, and burn subtitles into a finished video (details in playground/geml-media-demo). • geml-style live demo: Content stays pure text in page.geml, while styles and layout live in github.style.geml — rendering a 1:1 pixel-accurate replica of GitHub's blob page without CSS lock-in (details in playground/style-demo). • The code graph in the next section is itself a Profile: every document in .geml-code-graph/ declares profile = "geml-codemap/v1". You can also easily create your own custom domain profile.

A gift for programmers — geml-code-graph

To test GEML's expressive power and flexibility — and above all to see whether block-level bidirectional linking holds up — let's try it on a code graph, a familiar but demanding case for programmers: your whole codebase's call graph, written as GEML. geml codemap build lays the call graph out as a tree of GEML documents — every method an #id block, with #calls / #called-by edges both ways. The downstream chain (what a method calls) for troubleshooting, the upstream chain (who calls it) for the blast radius — all visible in a second;

npm i -g @geml/geml
geml codemap build              # --root defaults to . : detect languages -> index -> one merged graph in ./.geml-code-graph/
geml codemap serve              # opens your browser on the graph

geml-code-graph is itself a diagram format — one line embeds it in any GEML document (=== diagram {format=geml-code-graph src=.geml-code-graph/index.geml} ===), and an optional per-commit hook (bundled with the Claude skill) rebuilds it as the code moves, so the graph doesn't drift.

Scale is measured, not promised: on Apache Flink's codebase — 13,585 Java source files, ~81,000 methods, 266,821 call edges — the plain-text data tables still open and query instantly, and you can grep any method name to trace its call chain. Reproduce it yourself: clone apache/flink and run geml codemap build --joern … at its root.

Next — get hands-on now

▶ Try writing GEML in the Playground — edit on the left, rendered live on the right, and the build verdict flips red the moment a reference breaks. No install, nothing to read first.

Then, in the order that suits you:

1. See it render in your browser. Install the extension and open a raw .geml link (the raw file, not the GitHub blob page — that one is HTML): the GEML spec itself (dogfood — the spec is a GEML document, rendered at scale), the showcase (a computed table, four charts, a Mermaid flow, and math), or playground/sample.geml for the interactive code-graph.

2. See a whole page laid out from a document. playground/style-demo/ is a 1:1 replica of a GitHub blob page — top bar, file tree, breadcrumb, Preview/Code/Blame, dropdown menus — where page.geml holds every string and github.style.geml holds every colour and length, and the viewer knows about neither. It needs the extension and a local server (why, and the two commands): the page fetches its stylesheet and icons, which raw.githubusercontent.com forbids.

3. Run it locally. npm i -g @geml/geml (Node 22+), then geml check a document, or point it at your own repo with geml codemap build.

4. Set up Claude Code — one command. npx -y @geml/geml skill install puts the authoring skill, the CLI and the MCP server in place, user-global, for every project. It edits no settings and installs no hooks. Details.

5. Read the grammar. The full spec (EN / 中文) is normative and short enough to read in a sitting.

6. Or see it worked through, rule by rule. GEML, illustrated (EN / 中文) — eleven self-contained pages, one per block type, per profile, and for the CLI: GEML on the left, what the processor actually does on the right (geml check diagnostics, geml list addresses, --to html markup), each rule tagged with its source and status.

Using GEML with an LLM

The goal is one thing: your model edits a block at a time, and verifies — never re-reads and re-emits a whole file to change one paragraph. Getting there takes one step, and which step depends on what you use.

Using Claude Code — run this

npx -y @geml/geml skill install

It installs the authoring skill, the geml CLI and the MCP server, user-global, for every project. No settings.json edits, no hooks; re-run after an upgrade. (Prefer plugins? claude plugin marketplace add geml-spec/geml, then /plugin install geml@geml — same skill, MCP server bundled.)

Using DeepSeek Harness — add this bundle

The same setup, packaged as a dsh bundle — the geml MCP server plus the authoring and code-graph skills:

dsh plugin --profile web add @geml/dsh-plugin   # web = the profile dsh boots by default; use your own profile name if you run another

Listed on dshmarket and awesome-dsh-plugin; source in integrations/dsh-plugin/.

Using Codex — install the plugin

The same payload once more, packaged for Codex: both skills, the MCP server, and a SessionStart hook. Start Codex in a checkout of this repo and it shows up in /plugins (the marketplace source is committed at .agents/plugins/marketplace.json); to add it without cloning, the git-subdir entry is in integrations/codex-plugin/.

Then say it once in a session, and the project has switched:

This project uses GEML as its base document format; generate other formats from it as needed.

Using anything else — paste this, then check the output

A model with no skill to read needs the rules once. Paste the prompt below, and keep geml check as the gate on whatever it writes back — the CLI is npm i -g @geml/geml (Node 22+).

Write the document as GEML: every block is === type [attributes] … === (the format in 1 minute lists the types). Four rules are the ones models get wrong: the closing fence is a = run of the exact opening length, and a body containing === needs a longer outer fence; headings are ATX # only, with no --- frontmatter (metadata is === meta); every #id is unique and every reference ([[#id]], [text](#id), [^id], data=#id) must resolve; there is no raw HTML. The normative spec is GEML-spec.md.

What it will do with it

geml list   doc.geml                                     # CALL FIRST: every block, its address, kind, lines
geml find   "words" doc.geml                             # search block content -> an address, not a line number
geml get    doc.geml '#hello'                            # read ONE block (a heading id = its whole section)
geml get    doc.geml '#hello' --intro                    # a section cuts three ways: --head | --intro | --body
geml set    doc.geml '#license' --in template.geml#mit   # replace that block, forking another
geml add    doc.geml --after '#intro' --in snippet.geml  # insert a fragment (keeps its own ids)
geml revert doc.geml '#plan' --rev -1                    # roll ONE block back
geml check  doc.geml                                     # validate only: diagnostics + exit code

Any section cuts three ways, on get and set alike: --head is the heading line, --intro what it says before its first subheading, --body everything under it — so --body always contains --intro, and equals it when there is no subheading. A section's opening can be edited without pulling its subsections into context.

Every mutation is re-parsed before it writes and refused if it would break the document — which is what makes editing unattended safe. The rest of the verbs (delete, rename, history, --to md|html|geml conversion, addressing a block by type or content hash) are in the parser README.

MCP Server

A standard Model Context Protocol server ships with the package, so your agent edits one block at a time instead of rewriting whole files — on Markdown and GEML alike. It runs locally on Windows, macOS, and Linux; --root is the directory the server is confined to (use . or ${workspaceFolder} to bind to the active project).

Claude Code — one-command setup (installs skill, CLI, and MCP server):

npx -y @geml/geml skill install

(Or register manually via CLI: claude mcp add --scope user geml -- npx -y @geml/geml mcp --root .)

Cursor — add .cursor/mcp.json to your project:

{
  "mcpServers": {
    "geml": {
      "command": "npx",
      "args": ["-y", "@geml/geml", "mcp", "--root", "${workspaceFolder}"]
    }
  }
}

(Or in Cursor Settings → Features → MCP: name geml, command npx -y @geml/geml mcp --root .)

Claude Desktop — add to claude_desktop_config.json:

{
  "mcpServers": {
    "geml": {
      "command": "npx",
      "args": [
        "-y",
        "@geml/geml",
        "mcp",
        "--root",
        "/absolute/path/to/your/docs"
      ]
    }
  }
}

Then just ask for the change you want — "fix the Q3 row in the FY26 table" — and the agent addresses that one block. You never learn a tool name: each mirrors a CLI verb (geml set → geml_set), so one vocabulary covers the terminal and the agent.

Two guarantees make this better than letting a model rewrite the file: a write is parsed before it reaches disk and refused with its diagnostics if it would break the document, and every write first records a .gemlhistory revision — so a bad edit is both prevented and undoable (geml_revert restores one block, the rest of the file byte-identical). Paths stay confined to --root, which a client cannot widen.

Point --root at a repository that has a code graph (geml codemap build) and the same server also answers "who calls this" — four read-only geml_codemap_* tools, one client entry instead of two. Every tool and option: docs/mcp-guide.md.

Ecosystem and maturity

GEML is a small, young spec — but a stable one: 1.0 is released and usable for real documents (this repo's own spec is one), with a strict conformance suite, a reference implementation that passes it (versioned independently of the spec), and an open proposal process.

There is one specification, and it is bilingual. The .gemlhistory sidecar is defined by the geml-history/v1 profile — an application layer on top of the spec rather than part of it, which is also why it is MIT and the spec is CC-BY (LICENSE-spec.md says why):

Every profile this project publishes: spec/profiles/.

Versions and compatibility

• Self-hosting — GEML-spec.geml is the specification written in GEML, required to parse clean on every test run.

• A conformance suite is what holds separate implementations compatible.

• A reference implementation of the parser. 1,700+ unit tests today, plus the conformance corpus, round-trip serialization and end-to-end CLI runs, with coverage CI-gated at ≥95% lines / statements / functions / branches.

• Forward compatibility is in the grammar. A processor must degrade gracefully on constructs it does not recognize (spec §8.2), which is why adding a block type or a diagram format is not a breaking change. The type registry is open: an unregistered type name should contain a hyphen (acme-invoice), leaving hyphen-free names to future versions of the spec (§8.5).

• Claiming conformance. An implementation may call itself conformant to GEML 1.0 once it reproduces the conformance suite case for case (§8.5). No permission needed, and no sign-off from this repo.

• On the wire. Extension .geml (version sidecar .gemlhistory), media type text/geml, or text/vnd.geml where a registered type is required — text/geml is not registered with IANA yet.

• A fragment identifier on a .geml URL names the block bearing that id (§0.6) — which is not what #tag means on an HTML page.

How we thought about the design

What the design follows

1. Human–Agent Isomorphism, Not a Compromise Instead of splitting the difference between human-readable Markdown and machine-readable JSON, GEML treats human readability and machine determinism as a single, uncompromising constraint. Humans get clean, distraction-free prose; agents get a strongly typed AST—eliminating translation loss between two separate formats.

2. Doc-as-a-Base, Not a Stream of Characters Traditional documents are fragile streams of characters where editing one sentence often forces a full-file rewrite. GEML treats a document as an addressable database of structured records with stable primary keys (#id). Every block has an independent lifecycle, spatial coordinate, and atomic CRUD interface suited for O(1) agent reads and writes.

3. One Syntax Primitive, Infinite Domain Vocabularies Refuse to invent syntax patches for every new kind of content. GEML uses a single typed-block primitive (=== type) to carry code, data, tables, math, and layout. Domain capabilities expand infinitely through Profiles (profile = "..."): the grammar stays 100% frozen, while vocabularies remain open—ending dialect fragmentation at the root.

4. Transclusion over Duplication: Kill the Incentive to Copy Traditional hyperlinks are signposts pointing elsewhere, encouraging copy-pasting that inevitably causes copies to drift out of sync. GEML references are dynamic viewports (=== embed): define once at the source, and project live everywhere. Maintain a single source of truth by removing the motivation to copy.

5. Compiler-Grade Integrity: Treat Documentation Like Code Markdown's ethos is "never fail, render something"—the primary breeding ground for agent hallucinations and silent documentation decay. GEML enforces strict build-time static validation. Broken #ids, invalid attributes, and cyclic references fail the build with a non-zero exit code. Catch errors before they pollute downstream systems.

6. Local-First History, Not Cloud Lock-in or Git Overhead Data belongs on the local filesystem, and versioning belongs at block granularity. GEML refuses to lock version history behind proprietary cloud platforms (like Notion or Google Docs), while avoiding the heavy whole-repo commit overhead of Git for micro-edits. The companion .gemlhistory gives plain text local-first atomic snapshots and surgical rollback (geml revert #id), ensuring true data sovereignty and safety.

What it therefore refuses

Roadmap

• The GEML 1.0 specification, in English and Chinese, with a conformance suite — plus the geml-history/v1 profile that defines the .gemlhistory sidecar

• Reference implementation @geml/geml: parser, CLI, block-level .gemlhistory tracking

• Official MCP server (geml mcp) for Claude Code, Cursor, Codex and other MCP hosts

• codemap — a whole codebase's call graph, written as GEML

• The VS Code extension published on the Visual Studio Marketplace (publisher geml)

• Ecosystem integrations: VS Code highlighting and reference checking, tree-sitter, Obsidian, Logseq (two-way sync against a live DB graph), the browser viewer, a GitHub Action, LangChain / LlamaIndex, and the agent-harness plugins — Claude Code, Codex, Grok, DeepSeek Harness, plus root manifests for Gemini CLI and Kimi Code

• The Logseq plugin listed in the Logseq marketplace (PR #893) and the Grok plugin listed in xai-org/plugin-marketplace

• Parsers in other languages (Rust / Python) — the spec and the conformance suite are public, so community implementations are welcome; we are glad to help line them up

Take part

GEML is 1.0, but "stable" means the rules already there won't shift under you, not that the design is settled. There is exactly one implementation so far, and one set of opinions behind the spec. Your thinking can still change the spec itself. If you want a hand in it:

Come argue about these:

• Should a document format do table arithmetic?

• Should GEML have a style layer at all?

• When is a .gemlhistory sidecar actually worth having?

• geml get with no selector lists blocks. Should that be geml list?

• --view reads through an embed. Flag, or its own verb?

Or claim a piece:

• Write a second implementation of the spec — a new GEML parser in whatever language you like (how to write a parser)

• Finding the places where the spec is ambiguous is itself the contribution, whether or not that parser ever ships.

Or propose something new:

• A GEP: the proposal, the spec edit and the conformance cases land together (process)

Or put it to use:

Three files to read first: GOVERNANCE.md for how decisions get made, CONTRIBUTING.md for how to send work, and CODE_OF_CONDUCT.md for the one rule about people — disagree with the design as sharply as you like, not with the person.

Repository layout

spec/                  The specification as .md (EN / 中文) and the CC-BY spec
                       license, with profiles/ (application layers — geml-history,
                       geml-codemap, geml-style, geml-form) and proposals/ (GEPs),
                       both MIT
spec/in_geml_format/   The dogfood: the specification written in GEML, with its
                       .gemlhistory sidecar
geml-parser/           Reference parser, renderer, CLI + codemap toolkit (TypeScript, Node 22)
integrations/          Everywhere GEML plugs in: geml-viewer (browser extension),
                       geml-check-action (CI), vscode, obsidian, logseq (two-way
                       vault sync + the watcher), tree-sitter (brief),
                       langchain+llamaindex (RAG loaders), windows-icon
                       (Explorer file icons), and the agent-harness plugins —
                       claude-plugin, codex-plugin, grok-plugin, dsh-plugin
.agents/, .claude-plugin/   Plugin marketplace manifests, so the plugins show up
                       from a checkout (Codex `/plugins`, Claude Code `/plugin`)
playground/            In-browser playground (+ a live geml-code-graph of this repo)
docs/                  Guides, design notes, comparisons/ (COMPARISON + vs-CommonMark +
                       vs-XML-and-JSON), assets (logos, used by the Pages site below),
                       and an example .geml to render
.claude/skills/        Claude skills: GEML authoring, and the code graph
.github/               CI + geml-check workflows, MCP registry publish, and issue
                       templates (bug, GEP, new implementation)
site/                  The geml-spec.github.io/geml Pages site: a project homepage
                       (index.md) plus a Jekyll blog (blog/, posts in _posts/) —
                       the long-form "why a new format" article (EN / 中文) lives
                       there as its first post. `cd site && bundle exec jekyll
                       serve` builds it locally; the pages jobs in
                       .github/workflows/ci.yml build and deploy it on push to
                       main, grafting in playground/ as static output — with
                       playground.js built there rather than committed.

License & governance

Code is MIT (LICENSE): everything in this repository — geml-parser/, all of integrations/, playground/, .claude/skills/, the GEPs in spec/proposals/ — except the specification documents.

The specification documents are CC-BY-4.0 (LICENSE-spec.md, which lists them exactly): spec/GEML-spec* and spec/in_geml_format/*. There is one specification; the profiles under spec/profiles/ are application layers and are MIT. A spec is not software, so anyone may build a conformant implementation without permission — and call it conformant to GEML 1.0 once it passes the conformance suite.

Using the name. You need no permission to implement GEML, to name an implementation after the format (geml-rs, pygeml, a geml package on your language's registry), or to state that your tool reads and writes GEML. Two requests, neither of them a legal restriction: call an implementation conformant to GEML 1.0 only once it passes the conformance suite, and don't imply that this project wrote, endorses, or maintains it. Attribution for the specification text itself is what CC-BY-4.0 already asks for.