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Citra

Give your AI agent eyes for PDFs — with proof.

Local-first PDF evidence for agents. Structured text, tables, OCR, visual crops, and page-level citations your agent can defend — not invent.

Canonical package @sylphx/citra · bin citra · MCP io.github.SylphxAI/citra · live 5.0.0

npm version License: MIT stars

Zero-config in one line

npx -y @sylphx/citra

No Docker. No API key. No global install. Spawns a stdio MCP server agents can use immediately.

Client Setup
Any agent / CLI npx -y @sylphx/citra
Claude Code claude mcp add citra -- npx -y @sylphx/citra
Claude Desktop / Cursor / VS Code / Codex "command": "npx", "args": ["-y", "@sylphx/citra"]
Global CLI npm i -g @sylphx/citracitra

Why Citra feels unfairly good

Plain-text PDF tools make agents guess. Citra returns an Agent Document Twin they can cite.

Pain today With Citra
Page numbers invented or missing Page + geometry + provenance
Tables flattened into soup Rows · columns · cells · bounding boxes
Scanned PDFs become noise OCR path linked to evidence
Install / config / “hope it works” npx -y — done
Silent engine fallbacks Fail closed if the native binary is missing

Five reasons teams pick Citra

  1. Zero-config — real npx MCP, not a 20-step bootstrap.
  2. Evidence, not vibes — citations agents can show a human.
  3. Local-first — PDFs stay on the machine; no required cloud vision API.
  4. Brand-sole — one package, one bin, one story (@sylphx/citra / citra).
  5. Instrument family — compose with Iris (image), Cue (video), Spine, Lookout, Locus.

See the difference

Plain text vs evidence

Without evidence With Citra
“Revenue was about $12M” “Page 14, Table 3, cell (row 4, col 2) = $12.4M
Lost table structure Rows, columns, cells, bounding boxes
Scanned PDF = garbage text OCR with page-linked evidence
Hidden / adversarial text ignored Trust signals when requested

What you get

Three tools. One product surface.

Tool What agents use it for
read_pdf Smart default: markdown, tables, structure, OCR, citations
search_pdf Find page + snippet matches before deep reading
pdf_evidence Crops, renders, inspect, focused evidence ops

Minimal call:

{
  "sources": [{ "path": "/absolute/path/to/report.pdf" }]
}

Flagship use cases

  1. Financial reports — extract table cells agents can cite by page and geometry
  2. Research papers — headings, reading order, page-level quotes
  3. Scanned documents — OCR path with evidence, not a text soup

Platforms

One optional native package is selected for your host only:

Platform Native package
macOS arm64 @sylphx/citra-darwin-arm64
macOS x64 @sylphx/citra-darwin-x64
Linux x64 @sylphx/citra-linux-x64-gnu
Linux arm64 @sylphx/citra-linux-arm64-gnu
Windows x64 @sylphx/citra-win32-x64-msvc

Missing native → fail closed (no silent TypeScript PDF engine).

Product docs

Doc Purpose
docs/POSITIONING.md Strategic positioning
docs/COMPETITIVE.md Peer anchors and wedge
docs/EVIDENCE_CONTRACT.md Evidence = result contract
docs/TOOL_SURFACE.md Few clear tools policy
docs/PRODUCT_INDEPENDENCE.md This repo is SSOT
docs/IPPB.md Independent public product bar
docs/PUBLISH.md npm / git publish status
docs/guide/installation.md Install & host config
skills/citra/SKILL.md Agent skill surface

Surfaces (MCP · CLI · SDK)

MCP (default agent path)

npx -y @sylphx/citra

Claude Desktop / Cursor / VS Code / Codex

{
  "mcpServers": {
    "citra": {
      "command": "npx",
      "args": ["-y", "@sylphx/citra"]
    }
  }
}

Dual-era hosts that send server/discover before initialize (e.g. Gemini Antigravity CLI) are supported on stdio.

CLI

npx -y @sylphx/citra --help

SDK

  • @sylphx/citra/sdkCitra (read / search / evidence)
  • @sylphx/citra/pure-rust → low-level client helpers
  • Same tools as MCP: read_pdf · search_pdf · pdf_evidence
  • Requires the platform optional native package (same as MCP)

Install footprint (honest)

Compare full clean installs, not “JS wrapper tarball vs native executable”:

Metric (measured clean install, linux-x64) Historical TS 3.0.14 Sole-Rust 4.1.0 lineage
Main package on disk ~403 KB ~77 KB
Full node_modules ~82.3 MiB ~24.4 MiB (~3.4× smaller)
Installed files 4,101 20 (~205× fewer)
Production npm deps PDF.js + MCP TS SDK + more {} + one platform native

The native binary is multi-megabyte because it is the PDF engine. That is expected — and still a cleaner install than shipping PDF.js + a large JS tree.

Details: installed footprint comparison

Performance (method-bounded)

Controlled same-host linux-x64 dual-mode A/B vs historical @sylphx/pdf-reader-mcp@3.0.14, using registry-installed sole-Rust natives (measured on the 4.1.x lineage; method applies to current sole-Rust packages):

Mode What it measures Result
persistent_warm long-lived server, repeated identical local read_pdf after warm-up ≥ ~10× median latency improvement on all 8 required fixture classes
startup_inclusive spawn + initialize + one task large advantage on the same fixtures

persistent_warm includes a process-local cache for identical local path+options. First request in a process still pays full parse cost.

Not a multi-host guarantee. Details: 4.1.0 report · claims policy

Engine note

Current production is a native Rust engine on supported platforms via a thin Node launcher.

Local-first. Five platform packages. One clean install. Fail closed without the matching native.

Unusually formed or broken ToUnicode CMaps are handled without crashing; the release binary is panic-unwind so a worker-thread panic fails the request instead of aborting the process (#608).

Engineering history and recovery pins: docs/migration.md — not the product pitch.

Product proof & links


Stop PDF hallucinations. Give agents proof.

npx -y @sylphx/citra

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Give your AI agent eyes for PDFs — structured text, tables, OCR, visual evidence, and page-level citations via MCP. Native Rust, local-first.

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