The Problem

Agents need to show their work through plans, diagrams, dashboards, status cards, receipts, and reports. Markdown is portable but visually limited. HTML is expressive but not always durable across surfaces. Images are easy to share but lose their structure. Raw model output is flexible but inconsistent.

HyperWeave turns structured specs into deterministic visual artifacts. Each artifact is a self-contained SVG with layout, branding, data binding, and machine-readable metadata baked in. No JavaScript, no runtime, no dependencies. Readable by humans, recoverable by agents, and portable anywhere an <img> tag renders.

FORMAT	Renders identically across surfaces	Agent-Readable Metadata	Visual Fidelity	Token Efficiency	Zero Dependencies	SCORE
SVG	✓	✓	✓	~	✓	4.5
MARKDOWN	~	~	✗	✓	✓	3
HTML	✗	✓	✓	✗	~	2.5

Agentic Artifacts

HyperWeave receipts turn an AI coding session into a portable artifact that shows what it cost by model, tool usage, token spend, and context window history. Install the hook once and every session emits one:

pip install hyperweave
hyperweave install-hook

It reads your session's JSONL transcript from disk and detects the harness automatically (Claude Code or Codex). Theme it with any of the 8 primer variants, or the raw register tape:

hyperweave install-hook --genome cream   # any primer variant
hyperweave install-hook --genome raw     # the paper register tape

Want a different agent harness? Open an issue.

Matrices - Generative Tables

HyperWeave matrices are structured tables rendered as portable SVGs. A single JSON description can produce comparison grids, registries, tiers, benchmark tables, numeric heatmaps, chips, glyphs, bars, and status maps, while carrying a machine-readable payload for agents.

READER		PIXELS	MOTION	READS VIA
GitHub README	github	✓	Yes	camo, css animation
VS Code preview	vscode	✓	Yes	markdown preview
Slack unfurl	slack	~	-	image proxy
Gmail body	gmail	~	-	img tag
Agent	mcp	✗	-	hw:payload, hwz/1, markdown twin

hyperweave compose matrix --spec-file /dev/stdin -g primer --variant porcelain -o one-artifact.svg <<'JSON'
{
  "title": "One artifact. Many readers.",
  "subtitle": "how each consumer ingests the same SVG",
  "columns": [
    {
      "id": "reader",
      "label": "READER",
      "role": "label"
    },
    {
      "id": "mark",
      "label": "",
      "kind": "glyph",
      "glyph_tint": "full"
    },
    {
      "id": "pixels",
      "label": "PIXELS",
      "kind": "check"
    },
    {
      "id": "motion",
      "label": "MOTION",
      "kind": "pill"
    },
    {
      "id": "via",
      "label": "READS VIA",
      "kind": "chip"
    }
  ],
  "rows": [
    {
      "label": "GitHub README",
      "cells": [
        {
          "glyph": "github"
        },
        {
          "state": "full"
        },
        {
          "state": "on"
        },
        {
          "chips": [
            "camo",
            "css animation"
          ]
        }
      ]
    },
    {
      "label": "VS Code preview",
      "cells": [
        {
          "glyph": "vscode"
        },
        {
          "state": "full"
        },
        {
          "state": "on"
        },
        {
          "chips": [
            "markdown preview"
          ]
        }
      ]
    },
    {
      "label": "Slack unfurl",
      "cells": [
        {
          "glyph": "slack"
        },
        {
          "state": "partial"
        },
        {
          "state": "off"
        },
        {
          "chips": [
            "image proxy"
          ]
        }
      ]
    },
    {
      "label": "Gmail body",
      "cells": [
        {
          "glyph": "gmail"
        },
        {
          "state": "partial"
        },
        {
          "state": "off"
        },
        {
          "chips": [
            "img tag"
          ]
        }
      ]
    },
    {
      "label": "Agent",
      "cells": [
        {
          "glyph": "mcp"
        },
        {
          "state": "none"
        },
        {
          "state": "off"
        },
        {
          "chips": [
            "hw:payload",
            "hwz/1",
            "markdown twin"
          ]
        }
      ]
    }
  ],
  "notes": "pixels for humans · hw:payload for agents"
}
JSON

Inside that SVG, alongside the pixels - two tiers, two jobs:

recreate & modify - the complete table IR

<hw:payload schema="matrix/1" media-type="application/json">
{
  "title": "One artifact. Many readers.",
  "subtitle": "how each consumer ingests the same SVG",
  "columns": [
    { "id": "reader", "label": "READER",    "kind": "text",  "align": "left",   "role": "label" },
    { "id": "mark",   "label": "",          "kind": "glyph", "align": "center", "glyph_tint": "full" },
    { "id": "pixels", "label": "PIXELS",    "kind": "check", "align": "center" },
    { "id": "motion", "label": "MOTION",    "kind": "pill",  "align": "center" },
    { "id": "via",    "label": "READS VIA", "kind": "chip",  "align": "left" }
  ],
  "rows": [
    { "label": "GitHub README", "cells": [{ "glyph": "github" }, { "state": "full" }, { "state": "on" }, { "chips": ["camo", "css animation"] }] }
    <!-- … 4 more rows · lossless -->
  ]
}
</hw:payload>

read at a budget - ≈200 tokens to know what an artifact is

<hw:envelope format="hwz/1" media-type="application/json">
{
  "v": "hwz/1",
  "id": "sha256:0077750046fc2780fa4ba19fcd884fcf39f54632306d8c3d2d97f0b7cbf2df47",
  "k": "matrix",
  "title": "One artifact. Many readers.",
  "intent": "structured comparison: One artifact. Many readers.",
  "state": "active",
  "data": {
    "subvariant": "registry",
    "cols": ["mark", "pixels", "motion", "via"],
    "rows": {
      "GitHub README": "github",
      "VS Code preview": "vscode",
      "Slack unfurl": "slack",
      "Gmail body": "gmail",
      "Agent": "mcp"
    },
    "rows_total": 5
  },
  "frames": [{ "t": "matrix", "l": "One artifact. Many readers." }],
  "prov": { "by": "hyperweave", "ver": "0.4.0a5", "genome": "primer.porcelain", "ts": "2026-06-11T15:51:03.185542+00:00" }
}
</hw:envelope>

The envelope is the lossy digest; only the payload round-trips.

The round-trip: extract hw:payload, edit the JSON, POST /v1/compose with it as matrix: byte-identical re-render. The envelope's id is the sha256 of the payload, so an agent verifies "this artifact really is this data" before trusting either.
The look is a pointer, not a copy: prov.genome: "primer.porcelain" names the aesthetics; payload + that one string is the entire recreation recipe.
Markdown twin: every matrix has a GFM projection. --markdown-out on the CLI, respond:"json" over HTTP, render_target="markdown" over MCP.

# Connectors preset
https://hyperweave.app/v1/matrix/connectors/primer.static?variant=porcelain

# Any table, one URL: base64url MatrixSpec JSON (8 KB cap)
https://hyperweave.app/v1/matrix/custom/primer.static?spec=<base64url>

# CLI, with the markdown twin alongside
hyperweave compose matrix --spec-file table.json -g primer --variant porcelain --markdown-out table.md

Another matrice configuration for visualizing benchmarks:

MODEL		SWE-bench Verified	INPUT (per Mtok)	OUTPUT (per Mtok)
FRONTIER · CLOSED WEIGHTS
Claude Fable 5	anthropic	95 %	$10	$50
Claude Opus 4.8	anthropic	88.6 %	$5	$25
GPT-5.5	openai	82.6 %	$5	$30
Gemini 3.1 Pro	gemini	80.6 %	$2	$12
OPEN WEIGHTS
DeepSeek V4-Pro	deepseek	80.6 %	$0.44	$0.87
Kimi K2.6	kimi	80.2 %	$0.95	$4
GLM-5	zai	77.8 %	$1	$3.2
Mistral Medium 3.5	mistral	77.6 %	$1.5	$7.5

SWE-bench Verified % · USD per Mtok · current flagships, jun 2026 · sources: vals.ai · artificialanalysis.ai · model cards

hyperweave compose matrix --spec-file /dev/stdin -g primer --variant cream -o frontier-benchmarks.svg <<'JSON'
{
  "title": "Frontier vs Open",
  "subtitle": "coding & price · SWE-bench Verified against price per million tokens · current flagships, jun 2026",
  "columns": [
    {
      "id": "model",
      "label": "MODEL",
      "kind": "text",
      "align": "left",
      "role": "label"
    },
    {
      "id": "mark",
      "label": "",
      "kind": "glyph",
      "align": "center",
      "glyph_tint": "full"
    },
    {
      "id": "swe",
      "label": "SWE-bench Verified",
      "kind": "numeric",
      "align": "center",
      "polarity": "higher",
      "unit": "%"
    },
    {
      "id": "pin",
      "label": "INPUT",
      "sublabel": "per Mtok",
      "kind": "numeric",
      "align": "center",
      "polarity": "lower",
      "unit": "$"
    },
    {
      "id": "pout",
      "label": "OUTPUT",
      "sublabel": "per Mtok",
      "kind": "numeric",
      "align": "center",
      "polarity": "lower",
      "unit": "$"
    }
  ],
  "rows": [
    {
      "label": "Claude Fable 5",
      "cells": [
        {
          "glyph": "anthropic"
        },
        {
          "value": 95.0
        },
        {
          "value": 10
        },
        {
          "value": 50
        }
      ],
      "section": "FRONTIER · CLOSED WEIGHTS"
    },
    {
      "label": "Claude Opus 4.8",
      "cells": [
        {
          "glyph": "anthropic"
        },
        {
          "value": 88.6
        },
        {
          "value": 5
        },
        {
          "value": 25
        }
      ],
      "section": "FRONTIER · CLOSED WEIGHTS"
    },
    {
      "label": "GPT-5.5",
      "cells": [
        {
          "glyph": "openai"
        },
        {
          "value": 82.6
        },
        {
          "value": 5
        },
        {
          "value": 30
        }
      ],
      "section": "FRONTIER · CLOSED WEIGHTS"
    },
    {
      "label": "Gemini 3.1 Pro",
      "cells": [
        {
          "glyph": "gemini"
        },
        {
          "value": 80.6
        },
        {
          "value": 2
        },
        {
          "value": 12
        }
      ],
      "section": "FRONTIER · CLOSED WEIGHTS"
    },
    {
      "label": "DeepSeek V4-Pro",
      "cells": [
        {
          "glyph": "deepseek"
        },
        {
          "value": 80.6
        },
        {
          "value": 0.44
        },
        {
          "value": 0.87
        }
      ],
      "section": "OPEN WEIGHTS"
    },
    {
      "label": "Kimi K2.6",
      "cells": [
        {
          "glyph": "kimi"
        },
        {
          "value": 80.2
        },
        {
          "value": 0.95
        },
        {
          "value": 4.0
        }
      ],
      "section": "OPEN WEIGHTS"
    },
    {
      "label": "GLM-5",
      "cells": [
        {
          "glyph": "zai"
        },
        {
          "value": 77.8
        },
        {
          "value": 1.0
        },
        {
          "value": 3.2
        }
      ],
      "section": "OPEN WEIGHTS"
    },
    {
      "label": "Mistral Medium 3.5",
      "cells": [
        {
          "glyph": "mistral"
        },
        {
          "value": 77.6
        },
        {
          "value": 1.5
        },
        {
          "value": 7.5
        }
      ],
      "section": "OPEN WEIGHTS"
    }
  ],
  "sections": [
    "FRONTIER · CLOSED WEIGHTS",
    "OPEN WEIGHTS"
  ],
  "notes": "SWE-bench Verified % · USD per Mtok · current flagships, jun 2026 · sources: vals.ai · artificialanalysis.ai · model cards"
}
JSON

Diagrams - Topologies & Motion

HyperWeave diagrams are topology graphs (pipelines, fan-outs, hub-and-spoke, DAGs, sequences, state machines) rendered as portable SVGs. Like matrices, each carries the full payload / envelope / markdown projection set, so an agent reads the structure, not the pixels. Direction is carried by motion.

hyperweave compose diagram --spec-file /dev/stdin -g primer --variant porcelain --chrome bare -o one-artifact.svg <<'JSON'
{
  "topology": "fanout",
  "title": "One Artifact, Every Surface",
  "subtitle": "a self-contained SVG renders wherever markdown does",
  "notes": "every surface",
  "glyph_tint": "full",
  "nodes": [
    {
      "label": "HyperWeave SVG",
      "desc": "self-contained · portable",
      "role": "hero",
      "glyph": "hyperweave",
      "style": "card+glyph"
    },
    {
      "label": "GitHub",
      "desc": "README · Issues · PRs",
      "glyph": "github",
      "style": "card+glyph"
    },
    {
      "label": "Obsidian",
      "desc": "vault · daily notes",
      "glyph": "obsidian",
      "style": "card+glyph"
    },
    {
      "label": "Slack",
      "desc": "threads · unfurled",
      "glyph": "slack",
      "style": "card+glyph"
    },
    {
      "label": "Email",
      "desc": "inline · PDF export",
      "glyph": "gmail",
      "style": "card+glyph"
    },
    {
      "label": "Agent Context",
      "desc": "hw:reasoning parsed",
      "glyph": "anthropic",
      "style": "card+glyph"
    }
  ]
}
JSON

hyperweave compose diagram --spec-file /dev/stdin -g primer --variant cream --chrome bare -o frontier-serving.svg <<'JSON'
{
  "topology": "dag",
  "title": "Frontier Serving",
  "subtitle": "one key to three frontier labs, shared KV, telemetry skips the ranks",
  "notes": "frontier serving",
  "glyph_tint": "full",
  "nodes": [
    {
      "id": "req",
      "label": "requests",
      "desc": "clients"
    },
    {
      "id": "router",
      "label": "OpenRouter",
      "desc": "one key",
      "glyph": "openrouter",
      "style": "card+glyph"
    },
    {
      "id": "anthropic",
      "label": "Anthropic",
      "glyph": "anthropic",
      "style": "card+glyph"
    },
    {
      "id": "openai",
      "label": "OpenAI",
      "glyph": "openai",
      "style": "card+glyph"
    },
    {
      "id": "gemini",
      "label": "Gemini",
      "glyph": "gemini",
      "style": "card+glyph"
    },
    {
      "id": "kv",
      "label": "kv-cache",
      "desc": "redis",
      "glyph": "redis",
      "style": "card+glyph"
    },
    {
      "id": "obs",
      "label": "metrics",
      "desc": "grafana",
      "glyph": "grafana",
      "style": "card+glyph"
    }
  ],
  "edges": [
    {
      "source": "req",
      "target": "router"
    },
    {
      "source": "router",
      "target": "anthropic"
    },
    {
      "source": "router",
      "target": "openai"
    },
    {
      "source": "router",
      "target": "gemini"
    },
    {
      "source": "anthropic",
      "target": "kv"
    },
    {
      "source": "openai",
      "target": "kv"
    },
    {
      "source": "gemini",
      "target": "obs"
    },
    {
      "source": "router",
      "target": "obs"
    }
  ]
}
JSON

hyperweave compose diagram --spec-file /dev/stdin -g primer --variant porcelain --chrome bare -o mcp-gateway.svg <<'JSON'
{
  "topology": "pipeline",
  "title": "MCP Gateway",
  "subtitle": "host → gateway → server · request and response as two lanes",
  "notes": "mcp gateway",
  "edge_motion": "dash",
  "glyph_tint": "full",
  "nodes": [
    {
      "id": "host",
      "label": "Claude Code",
      "desc": "MCP host",
      "glyph": "claudecode",
      "style": "card+glyph"
    },
    {
      "id": "gw",
      "label": "MCP gateway",
      "glyph": "mcp",
      "role": "hero",
      "style": "card+glyph"
    },
    {
      "id": "server",
      "label": "hyperweave",
      "desc": "MCP server",
      "glyph": "hyperweave",
      "style": "card+glyph"
    }
  ],
  "edges": [
    {
      "source": "host",
      "target": "gw",
      "direction": "both"
    },
    {
      "source": "gw",
      "target": "server",
      "direction": "both"
    }
  ]
}
JSON

hyperweave compose diagram --spec-file /dev/stdin -g primer --variant noir --chrome bare -o frontier-handoff.svg <<'JSON'
{
  "topology": "pipeline",
  "title": "Frontier Handoff",
  "subtitle": "one task relayed across four labs, the comet is the payload",
  "notes": "frontier handoff",
  "edge_motion": "beam",
  "node_style": "glyph-circle",
  "glyph_tint": "full",
  "nodes": [
    {
      "label": "GPT",
      "glyph": "openai"
    },
    {
      "label": "Claude",
      "glyph": "anthropic",
      "role": "hero"
    },
    {
      "label": "Gemini",
      "glyph": "gemini"
    },
    {
      "label": "Ollama",
      "glyph": "ollama"
    }
  ]
}
JSON

The Verb Algebra

Every HyperWeave artifact is a re-ingestible object, not just an image. It carries its full spec (hw:payload) and a hash-verified digest (hwz/1 envelope), so an agent can work with it directly, never parsing pixels.

Six verbs, split two ways.

Write · mints a new artifact, returns a content-addressed link (/v1/a/{id}), never inline SVG:

Verb	What it does
`compose`	a spec → an artifact
`transform`	edit an artifact's spec → a new artifact (new id + lineage)

Read · never mutates the artifact:

Verb	What it does	Returns
`extract`	pull the payload, envelope, or markdown back out	the requested depth
`verify`	recompute the id, proving the artifact is its data	`{valid, id}`
`diff`	compare two artifacts	the structural delta
`query`	ask a question of the envelope	the answer

Every verb runs the same over HTTP (POST /v1/{verb}) and MCP (hw_{verb}):

# compose an artifact, then read its spec straight back, no rendering
hyperweave compose matrix --spec-file table.json -g primer -o table.svg
curl -X POST https://hyperweave.app/v1/extract \
  -H 'Content-Type: application/json' \
  -d '{"source": "<svg or /v1/a/{id} url>", "respond": "payload"}'

Genomes - Aesthetic DNA

A genome is a portable, machine-readable aesthetic specification. It encodes the complete visual identity (chromatic system, surface material, motion vocabulary, geometric form language) as a set of CSS custom properties that any agent can consume and apply consistently across every artifact type.

Four built-in genomes ship today. Custom genome generation via AI skill files coming soon.

	brutalist	automata	chrome	primer
Aesthetic	Raw material	Cellular	Metallic	Minimal
Variants	22 (8 dark, 14 light)	16 tones, any two pair	5 named	8 (4 dark, 4 light)
Motion	Animated border SMIL	Animated cell grid	Animated border SMIL	Animated state marks
Divider	`seam` · `sigil`	`dissolve`	`band`	`aura`

Every broken <img> URL renders the SMPTE RP 219 test pattern with ERR_NNN matching the HTTP status, instead of a browser broken-image icon.

Install

uv add hyperweave            # CLI + SVG rendering (the base)
uv add 'hyperweave[serve]'   # + HTTP server  (hyperweave serve)
uv add 'hyperweave[mcp]'     # + MCP server   (hyperweave mcp)
uv add 'hyperweave[all]'     # + both servers
# or swap `uv add` for `pip install`

Requires Python 3.12+. The base install is CLI + rendering; the HTTP and MCP servers are optional extras so the core stays lean.

Entry Points

Four interfaces, one pipeline. Every path produces the same artifact through the same compositor.

MCP

{
  "mcpServers": {
    "hyperweave": {
      "command": "hyperweave",
      "args": ["mcp"]
    }
  }
}

# Static badge
hw_compose(type="badge", title="BUILD", value="passing", genome="brutalist")

# Data-driven badge - unified token grammar (gh:owner/repo.metric, pypi:pkg.metric, ...)
hw_compose(type="badge", title="STARS", data="gh:anthropics/claude-code.stars", genome="brutalist")

# Strip with multiple live metrics
hw_compose(type="strip", title="readme-ai",
           data="gh:eli64s/readme-ai.stars,gh:eli64s/readme-ai.forks,pypi:readmeai.version",
           genome="chrome")

# Marquee with mixed text + live tokens
hw_compose(type="marquee",
           data="text:NEW RELEASE,gh:anthropics/claude-code.stars,text:DOWNLOAD",
           genome="brutalist")

# Read or edit an existing artifact - the verb algebra
hw_extract(svg_or_url="<svg or /v1/a/{id} url>", respond="payload")
hw_transform(svg_or_id="<svg or /v1/a/{id} url>",
             mutations=[{"op": "replace", "path": "/title", "value": "SHIPPED"}])

hw_discover(what="all")

CLI

# Badge
hyperweave compose badge "build" "passing" --genome brutalist

# Strip with metrics
hyperweave compose strip "readme-ai" "STARS:2.9k,FORKS:278" -g brutalist

# Live data through the unified --data token grammar
hyperweave compose badge "STARS" --data 'gh:anthropics/claude-code.stars' -g brutalist

# Marquee with mixed text + live tokens
hyperweave compose marquee --data 'text:NEW RELEASE,gh:owner/repo.stars,text:DOWNLOAD' -g brutalist

# Session receipt from an agent transcript (Claude Code / Codex)
hyperweave compose receipt session.jsonl -o receipt.svg

# Validate a spec without rendering
hyperweave validate spec.json

# Profile card (live GitHub data, path-segment identity)
hyperweave compose stats eli64s -g chrome -o stats.svg

# Star history chart
hyperweave compose chart stars eli64s/readme-ai -g brutalist -o chart.svg

# Custom genome from a local JSON file (validated against the profile contract)
hyperweave compose badge "DEPLOY" "live" --genome-file ./my-genome.json
hyperweave validate-genome ./my-genome.json

HTTP API

# URL grammar: /v1/{type}/{title}/{value}/{genome}.{motion}
curl 'https://hyperweave.app/v1/strip/readme-ai/brutalist.static?value=STARS:2.9k,FORKS:278'

# Live data via the unified ?data= grammar (works on badge / strip / marquee)
curl 'https://hyperweave.app/v1/badge/STARS/chrome.static?data=gh:anthropics/claude-code.stars'
curl 'https://hyperweave.app/v1/strip/readme-ai/brutalist.static?data=gh:eli64s/readme-ai.stars,gh:eli64s/readme-ai.forks'
curl 'https://hyperweave.app/v1/marquee/SCROLL/brutalist.static?data=text:NEW%20RELEASE,gh:anthropics/claude-code.stars'

# Chromatic variants (automata: 16 solo tones, pair any two via &pair=...; chrome: horizon/abyssal/lightning/graphite/moth)
curl 'https://hyperweave.app/v1/badge/PYPI/automata.static?variant=teal&pair=violet&data=pypi:hyperweave.version'
curl 'https://hyperweave.app/v1/badge/build/passing/automata.static?size=compact'

# Genome-themed dividers
curl 'https://hyperweave.app/v1/divider/band/chrome.static'
curl 'https://hyperweave.app/v1/divider/seam/brutalist.static'
curl 'https://hyperweave.app/v1/divider/dissolve/automata.static'

# Genome-agnostic dividers
curl 'https://hyperweave.app/a/inneraura/dividers/zeropoint'

# Structured frames: /v1/{matrix|diagram}/{preset}/{genome}.{motion}
# (preset 'custom' takes a base64url spec; diagrams add ?chrome=bare)
curl 'https://hyperweave.app/v1/matrix/connectors/primer.static?variant=porcelain'
curl 'https://hyperweave.app/v1/diagram/pipeline/primer.static?variant=porcelain&chrome=bare'

# POST compose
curl -X POST https://hyperweave.app/v1/compose \
  -H "Content-Type: application/json" \
  -d '{"type":"strip","title":"hyperweave","genome":"brutalist","value":"STARS:2.9k"}'

# Verb algebra over an existing artifact: extract · verify · transform · diff · query
curl -X POST https://hyperweave.app/v1/extract \
  -H "Content-Type: application/json" \
  -d '{"source":"<svg or /v1/a/{id} url>","respond":"payload"}'

# Local server
hyperweave serve --port 8000

How It Works

Every artifact is the output of a single composition formula:

ARTIFACT = FRAME × PROFILE × GENOME × SLOTS × MOTION × ENVIRONMENT

Python builds context dicts. Jinja2 builds SVG. YAML defines config. Three layers, no mixing. Zero f-string SVG in Python.

ComposeSpec → engine.py → assembler.py (CSS) → lanes.py (validate) → templates.py (Jinja2) → SVG

Every artifact ships with:

Re-ingestible payload: the full spec (hw:payload) plus a hash-verified hwz/1 envelope, so an agent can recover, verify, and edit it - the basis of the verb algebra.
Semantic metadata: provenance, reasoning, spatial trace, aesthetic DNA. Machine-readable context so the next agent in the chain knows what it's looking at and why.
CSS state machines: data-hw-status, data-hw-state, data-hw-regime drive visual transitions through the Custom Property Bridge. No JavaScript.
Pure CSS/SMIL animation: all motion uses compositor-safe properties (transform, opacity, filter). No script tags. Works anywhere SVGs render: GitHub's Camo proxy, email clients, Notion embeds.
Accessibility: WCAG AA, prefers-reduced-motion, prefers-color-scheme, forced-colors, ARIA markup. Structural, not decorative.

Dimension	Count
Frame types	10 (badge, strip, icon, divider, marquee, stats, chart, matrix, diagram, receipt)
Genomes	4 (automata, brutalist, chrome, primer)
Motion configs	6 (1 static + 5 border SMIL)
Glyphs	192 (183 brand marks + 9 geometric shapes)
Divider variants	10: 5 genome-themed (`band` chrome, `seam` + `sigil` brutalist, `dissolve` automata, `aura` primer) + 5 genome-agnostic (`block`, `current`, `takeoff`, `void`, `zeropoint`) at /a/inneraura/dividers/
Metadata tiers	5 (Tier 0 silent → Tier 4 reasoning)
Bundled fonts	5 (JetBrains Mono, Orbitron, Chakra Petch, Barlow Condensed, Inter), embedded per artifact, no external font requests

Stack: Pydantic, FastAPI, FastMCP v3, Jinja2, Typer.

Data Connectors

HyperWeave binds live data into any artifact through a unified token grammar (?data=...). Tokens are comma-separated; each token is either a literal (text:, kv:) or a live fetch (<provider>:<identifier>.<metric>).

Prefix	Source	Identifier shape	Metrics
`gh` / `github`	GitHub	`owner/repo`	`stars`, `forks`, `watchers`, `contributors`, `issues`, `pull_requests`, `last_push`, `build`, `license`, `language`
`pypi`	PyPI + pepy.tech	`package`	`version`, `license`, `python_requires`, `downloads`
`npm`	npm	`package`	`version`, `license`, `downloads`
`crates` / `cargo`	crates.io	`crate`	`version`, `downloads`, `recent_downloads`, `license`
`hf` / `huggingface`	Hugging Face	`org/model`	`downloads`, `likes`, `tags`, `pipeline_tag`, `library_name`, `license`, `gated`, `last_modified`
`docker`	Docker Hub	`namespace/repo`	`pull_count`, `star_count`, `last_updated`
`arxiv`	arXiv	`id` (e.g. `2310.06825`)	`title`, `authors`, `published`, `updated`, `categories`, `summary`, `journal_ref`, `doi`
`scorecard`	OpenSSF Scorecard	`owner/repo`	`score` (overall trust), plus per-check: `code_review`, `maintained`, `vulnerabilities`, `token_permissions`, ...
`dora`	GitHub Actions	`owner/repo`	`deploy_frequency`, `lead_time`, `change_failure_rate`, `mttr` (30-day window)
`text`	literal	-	renders the payload as displayed text
`kv`	literal	`KEY=VALUE`	static role-tagged value

Caching: live values for 5–10 min; a failed fetch caches 60s and shows - rather than a fabricated zero.
Isolation: each provider has its own circuit breaker, so one upstream outage can't trip the others.
Escaping: commas inside text: / kv: values escape as \,.

→ Open an issue to request a connector.

Contributing

HyperWeave is early. If you're interested in building genomes, extending frame types, or just seeing what this looks like in your own README, join the Discord.

hyperweave

The Problem

Agentic Artifacts

Matrices - Generative Tables

Diagrams - Topologies & Motion

The Verb Algebra

Genomes - Aesthetic DNA

Install

Entry Points

MCP

CLI

HTTP API

How It Works

Data Connectors

Contributing

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