anchor-kb 0.2.1

ANCHOR - agent-first knowledge canvas with PDF ingest, FMU simulation, and source-grounded provenance

ANCHOR

ANything Can Have Order and Reason
Agent-First Knowledge Canvas with Source-Grounded Provenance

ANCHOR is a tool that lets you and your agent work with engineering documents.

Drop a PDF onto a canvas. The agent reads it and pulls the values you need into a spec table. Every value links back to the page and bounding box it came from, so you can click and see the source.

Drop FMU simulation models onto the same canvas and wire the extracted values into their parameters.

It runs on your laptop. Data lives under ~/anchor-data. Agents talk to it over MCP, so it works with Claude Code, Cursor, or any MCP client. There's an HTTP API and a CLI too.

First five minutes: docs/getting-started/tutorial.md.

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Install

Two paths, depending on whether you want to use ANCHOR or hack on it.

Use it (from PyPI)

uv tool install anchor-kb

anchor and anchor-mcp are now on your PATH globally. The wheel includes the prebuilt frontend, so no Node toolchain is required to just run it.

If you want LLM-backed gold region extraction on your first PDF upload, create a .env file before starting ANCHOR; see Enable gold region extraction. Installation itself does not require an API key.

anchor serve              # → http://127.0.0.1:8002

Requires Python >= 3.12. CI tests Linux and runs CLI smoke checks on macOS and Windows; verify browser and PDF workflows on your target platform.

If you prefer plain pip:

pipx install anchor-kb
# or, in a virtualenv:
pip install anchor-kb

Optional extras:

Extra	Install	Adds
fmus	uv tool install 'anchor-kb[fmus]'	FMU simulation runtime (fmpy). Without it, FMU tools fail closed unless you opt into the synthetic demo with ANCHOR_FMU_DEMO=1.

Hack on it (from source)

git clone https://github.com/Novia-RDI-Seafaring/anchor
cd anchor
uv sync --extra dev          # adds pytest, ruff, import-linter
pnpm --dir web install

Start the backend in one terminal:

uv run anchor serve

Start the frontend development server in a second terminal:

pnpm --dir web dev

Open http://localhost:5173 for the development UI. The backend remains on http://127.0.0.1:8002.

Source development requires Node.js 20+ and pnpm 10. If pnpm is not installed globally, run the frontend commands through Corepack instead:

corepack pnpm@10 --dir web install
# Run this in the second terminal after starting the backend.
corepack pnpm@10 --dir web dev

Document, canvas, and server commands default --data-dir to ~/anchor-data. Set ANCHOR_DATA_DIR to change the default for CLI and MCP commands. An explicit --data-dir takes priority. Use the same path when registering agents or working with extension manifests.

Releases are tag-driven: pushing a v* tag triggers the release workflow, which publishes to PyPI via OIDC trusted publishing (no token sits in the repo). See PUBLISHING.md for the full release process.

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Quick start

To produce gold regions, configure a .env file before running anchor demo or anchor ingest; see Enable gold region extraction. Without it, ANCHOR still produces the silver document extraction.

# 0. One-shot: seed a `demo` workspace with six placeholder spec slots
#    and start the server. If you already have the optional local demo PDF,
#    ANCHOR ingests it too; otherwise ingest your own PDF in step 3.
anchor demo

# Or step by step:

# 1. Pick a folder for your data; create your first canvas
anchor canvas create my-first-canvas --data-dir ~/anchor-data

# 2. Start the server (serves the canvas UI, HTTP API, and browser SSE updates)
anchor serve --data-dir ~/anchor-data

# 3. (in another terminal) Ingest a PDF
anchor ingest /path/to/datasheet.pdf --data-dir ~/anchor-data

# 4. Open http://localhost:8002/c/my-first-canvas in your browser

See docs/getting-started/tutorial.md for a walked-through anchor demo -> "agent fills the placeholders" tour.

That's the whole loop. Every PDF you ingest becomes a structured set of regions on disk; every canvas you create is a folder you can zip and email.

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Using ANCHOR with an AI agent

ANCHOR exposes its tools over MCP (Model Context Protocol). For Claude Code, register the local stdio server with:

claude mcp add --transport stdio --scope user anchor -- \
  anchor-mcp --data-dir ~/anchor-data --base-url http://localhost:8002
claude mcp list

Restart Claude Code (Cmd+Q, reopen). In any conversation, run /mcp and you should see anchor listed with its available tools. The exact list depends on optional extensions such as FMU support. Then talk normally:

"Ingest the PDF at ~/Downloads/lkh-pump.pdf and create a canvas called pump-analysis with a document node for it."

"What does the document say about max inlet pressure for the LKH-5 at 50 Hz? Place the answer as a fact card on the pump-analysis canvas, with an evidence edge back to the source page."

Claude calls the MCP tools directly. Your browser tab on localhost:8002/c/pump-analysis, if open, sees nodes appear live via SSE. Multi-client real-time sync between agents and humans is the default.

For Cursor:

anchor install cursor --data-dir ~/anchor-data

See the agent configuration guide for Codex, OpenCode, Cursor, Claude Code, and generic stdio examples.

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Where data lives

Every canvas is a folder. Every document is a folder. Both shareable as zips, both diffable in git.

~/anchor-data/
├─ bronze/                 # raw PDFs (your originals)
│   └─ datasheet.pdf
├─ silver/<doc-slug>/      # docling extraction + per-page markdown + page PNGs
│   ├─ index.json          # outline, tables, figures
│   ├─ pages.meta.json
│   └─ pages/{1.md, 1.png, …}
├─ gold/<doc-slug>/        # structured regions with page + bbox provenance
│   └─ pages/{1.regions.json, 1/r1-spec-block.png, …}
└─ canvases/<canvas-slug>/
    ├─ meta.json
    ├─ state.json          # latest snapshot
    └─ events.jsonl        # append-only log; every action ever taken

This layout is the contract. You can hand-edit JSON files, copy a canvas folder to another machine, or version-control the whole thing.

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Configuration

Select the data directory and server bind address with the CLI flags --data-dir, --host, and --port. The following ANCHOR_ environment variables configure storage, processing, and browser access:

Variable	Default	Purpose
ANCHOR_DATA_DIR	~/anchor-data	Default storage root for CLI and MCP commands. An explicit --data-dir takes priority.
ANCHOR_OPENAI_API_KEY	(unset)	Optional: enables LLM polish + region extraction in the gold layer
ANCHOR_OPENAI_BASE_URL	(unset)	Override the OpenAI-compatible endpoint. For Azure OpenAI v1 use https://<resource>.openai.azure.com/openai/v1/; for Ollama use http://localhost:11434/v1.
ANCHOR_POLISH_MODEL	gpt-5.4	Model name for page-MD polishing
ANCHOR_REGION_MODEL	gpt-5.4	Model name for region extraction
ANCHOR_EMBED_MODEL	BAAI/bge-small-en-v1.5	Local sentence-transformer model used by default for semantic search. Recorded in every embeddings.json so cross-model search refuses to mix vectors.
ANCHOR_DPI	150	Render DPI for page images
ANCHOR_CORS_ORIGINS	(unset)	Comma-separated additional origins permitted by the HTTP server

If you don't set ANCHOR_OPENAI_API_KEY, ingest still produces silver (deterministic Docling extraction + per-page markdown). Gold extraction (LLM-driven structured regions) is skipped. The system stays useful without an API key: silver is the workable substrate; gold is the polish.

Enable gold region extraction

Gold regions are created during PDF ingestion only. Configure a vision-capable LLM endpoint before uploading a document or running anchor ingest. Documents already ingested as silver-only are not backfilled automatically; ingest them again after enabling a provider.

ANCHOR reads .env from the directory where you start anchor serve, anchor demo, or anchor ingest. For users installed with uv tool install anchor-kb, create that .env file in your chosen launch directory before the first upload.

For OpenAI, create .env containing:

ANCHOR_OPENAI_API_KEY=<your-openai-api-key>
ANCHOR_POLISH_MODEL=gpt-5.4
ANCHOR_REGION_MODEL=gpt-5.4

For Azure OpenAI, ANCHOR currently supports the Azure OpenAI v1 endpoint through the standard OpenAI-compatible client using API-key authentication:

ANCHOR_OPENAI_API_KEY=<your-azure-openai-key>
ANCHOR_OPENAI_BASE_URL=https://<resource-name>.openai.azure.com/openai/v1/
ANCHOR_POLISH_MODEL=<vision-capable-deployment-name>
ANCHOR_REGION_MODEL=<vision-capable-deployment-name>

The Azure deployment name is used as model and must support image input and JSON-formatted chat completion output. Azure Entra ID authentication and the older Azure deployment/API-version endpoint shape are not configured by ANCHOR environment variables today. See Microsoft's Azure OpenAI v1 API documentation for endpoint details.

From the same directory as .env, start ANCHOR and upload the PDF in the UI:

anchor serve

Alternatively, ingest a file directly from the same directory:

anchor ingest "C:\path\to\datasheet.pdf" --data-dir "$HOME\anchor-data"

Successful gold extraction creates ~/anchor-data/gold/<doc-slug>/pages/*.regions.json and returns a non-zero region_count when regions are identified.

For Ollama / local-LLM recipes, see docs/guides/agent-setup.md.

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Commands

anchor serve     [--data-dir DIR] [--host HOST] [--port PORT]
anchor demo      [--data-dir DIR] [--host HOST] [--port PORT] [--no-serve]
anchor ingest    PDF_PATH [--data-dir DIR] [--skip-polish] [--skip-regions]
anchor list      [--data-dir DIR]
anchor index     SLUG [--data-dir DIR]
anchor regions   SLUG [--page N] [--data-dir DIR]
anchor page-text SLUG PAGE [--data-dir DIR]
anchor embed     [SLUG] [--overwrite] [--data-dir DIR]
anchor search    "<query>" [--k N] [--data-dir DIR]
anchor canvas    list   [--data-dir DIR]
anchor canvas    create SLUG [--title TITLE] [--data-dir DIR]
anchor canvas    placeholders SLUG [--data-dir DIR]
anchor canvas    snapshot SLUG [--data-dir DIR] [--base-url URL]
anchor install     claude-code [--data-dir DIR] [--dry-run]
anchor install     cursor [--data-dir DIR] [--dry-run]
anchor install     print [--data-dir DIR]
anchor extensions  list  [--data-dir DIR] [--verbose]
anchor extensions  info  NAME [--data-dir DIR]
anchor extensions  add   MANIFEST_PATH [--scope system|project] [--data-dir DIR] [--force]
anchor extensions  remove NAME [--scope system|project] [--data-dir DIR]
anchor extensions  discover [--data-dir DIR]
anchor extensions  schema
anchor version

For new Claude Code setups, use the client-native claude mcp add command shown above. The anchor install claude-code helper remains in the CLI but is not the recommended setup path for current Claude Code releases.

anchor-mcp [--data-dir DIR] runs the MCP server over stdio (used by Claude Code's MCP harness; you don't normally invoke it yourself).

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Architecture (one paragraph)

ANCHOR is a hexagonal modular monolith. Pure domain code in core/ (no I/O, no framework imports - enforced by lint-imports). Concrete protocol implementations in infra/. Transport adapters in adapters/ (HTTP, MCP, CLI, SSE). The Python wheel ships the React frontend bundle inside it (anchor/_web_dist/) so one process serves both the API and the UI. State changes are events, persisted to events.jsonl per canvas, broadcast to subscribers (agents on MCP, browsers on SSE). See the architecture docs.

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Extensions and the Open Ingestion Protocol

ANCHOR's canvas is one OIP consumer. PDF ingestion is one OIP producer, bundled with this build. The protocol, specified at github.com/Novia-RDI-Seafaring/OIP, is governance-neutral: any tool that produces ingested knowledge in OIP shape can plug in, and any OIP-aware consumer can read its output. A transcription tool, a code-region extractor, a web crawler, or your own ingestion logic does not need to import ANCHOR. It only needs to ship an OIP manifest at a known location.

The CLI surfaces this:

anchor extensions list                        # what producers can this ANCHOR see?
anchor extensions discover                    # where does it look for manifests?
anchor extensions add <path-to-manifest.json> # register a new producer (system-wide)
anchor extensions schema                      # print a starter manifest to edit
anchor extensions info anchor-pdfs            # full manifest for one producer

Discovery, in priority order:

1. Per-data-dir: <data-dir>/.oip/producers.d/*.json (highest priority; bound to a specific workspace tree)
2. System-wide: ~/.config/oip/producers.d/*.json (any installer can drop a manifest here; visible to every OIP consumer on the machine)
3. Bundled: compiled into this ANCHOR wheel (anchor-pdfs, anchor-fmus, and anchor-cad; SysML tools are also exposed by the bundled MCP server)

For implementation status: today, an OIP-registered producer is visible in extensions list but ANCHOR doesn't yet spawn external producer MCP servers and proxy their tools. That's the next engineering lift. See the OIP repo for the spec and EXTENSIONS.md for ANCHOR's host-side roadmap.

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Tests

uv sync --extra dev                       # one-time: install pytest/ruff/import-linter
uv run pytest                             # ~340 backend tests
uv run lint-imports                       # 6 dependency-rule contracts
pnpm --dir web test                       # ~180 web tests (Vitest)
pnpm --dir web exec tsc --noEmit          # web typecheck

The test seam is function-based pytest with in-memory implementations of every port. Real I/O tests use tmp_path. The frontend tests cover canvas primitives, the SSE event store, and the inline-edit hooks.

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Status & roadmap

v0.2 (current): canvas primitive + PDF ingestion in one package, real-time SSE sync, MCP integration, skill installer for Claude Code/Cursor, backend and web test suites, hexagonal contracts enforced.

Near-term: complete remaining node renderer and asset workflows, then stabilise the extension registration surface.

Mid-term: split the canvas primitive (anchor-canvas) and PDF extension (anchor-canvas-pdfs) into separately-publishable packages, formal extension contract for third-party authors, and per-project anchor.toml for declarative extension sets.

Longer term: other ingestion extensions (audio/video transcription, code, web), shared org docs / personal canvases topology, optional Postgres event store for very large workspaces.

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Security model: read before exposing

ANCHOR's HTTP server is unauthenticated by design. It edits local engineering data (workspaces, documents, FMU files) and is meant to run on your own machine.

• Default bind is 127.0.0.1 (loopback). Nothing else on the LAN can reach it unless you pass --host 0.0.0.0.
• CORS is restricted to the dev Vite origin (localhost:5173); set ANCHOR_CORS_ORIGINS=https://your-host for explicit overrides.
• Workspace slugs and upload filenames are policy-checked and containment-asserted before they hit disk. The v2 codebase does not trust client-supplied paths.

If you want to share an ANCHOR instance on a network, add your own reverse proxy with auth in front of it (Tailscale, OAuth proxy, basic-auth nginx, ...). Don't expose the unauthenticated port directly.

Limitations (v0.2)

These extensions are intentionally rough; we ship them so you can see the shape and contribute, not as finished features:

• anchor_cad: parametric-CAD producer (jscad/openSCAD) ships as a proof of concept; full feature parity with STEP/STL viewing is on the roadmap. SVG export still has a known font-handling bug.
• anchor_sysml: SysML import (BSD-3-Clause fixtures from the OMG reference) and export to SVG/markdown are experimental; we'll swap the hand-rolled IR for the official Pydantic model when that lands.
• anchor_fmus: FMU simulation requires fmpy (install via uv tool install 'anchor-kb[fmus]'). Without it the extension fails closed; set ANCHOR_FMU_DEMO=1 to use the synthetic-output runtime (every result is stamped synthetic=true so the UI can warn you).

License

MIT, see LICENSE.

Citation

If you use ANCHOR, please cite the software repository:

@misc{ANCHOR,
  author       = {Lamin Jatta and Christoffer Bj{\"o}rkskog},
  title        = {ANCHOR: Agent-First Knowledge Canvas with Source-Grounded Provenance},
  year         = {2026},
  howpublished = {\url{https://github.com/Novia-RDI-Seafaring/anchor}},
}

GitHub-compatible citation metadata is provided in CITATION.cff.

Acknowledgments

This work was done in the Business Finland funded project Virtual Sea Trial.

Contributing

Open changes as short-lived branches targeting main; see CONTRIBUTING.md. Run uv run --extra dev pytest and uv run --extra dev lint-imports before pushing backend changes. See EXTENSIONS.md for the proposed third-party extension contract and its current implementation status.