paperloom

Folder-scoped LLM-maintained research wiki. Karpathy's llm-wiki pattern, for scientific papers.

$ mkdir my-research && cd my-research
$ paperloom init
Vault created at /home/you/my-research

$ paperloom ingest ~/Downloads/papers/
Ingesting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 12/12
12 ingested, 0 skipped, 0 failed (of 12)

$ claude "/contribute the I-JEPA paper"
[Claude Code reads sources/raw/2301.08243/paper.md, drafts a plan,
 writes sources/research/2301.08243-assran-i-jepa.md via the MCP tools]

TL;DR

Paperloom is a small MCP server + CLI that gives a coding agent (Claude Code, Gemini CLI, ...) the file primitives to maintain a personal research wiki out of a folder of markdown files — batch PDF ingest, search, note creation, tagging — while the agent supplies all the actual reading and reasoning. Unlike a generic llm-wiki setup or MindBase's global data folder, a paperloom vault is one self-contained directory (git init && paperloom init and you're done) built around ingesting corpora of 50-1000 papers at once, and it never requires an LLM API key of its own — your host agent already has one.

Related MCP server: ScholarMCP

Credits

Paperloom stands on two shoulders:

• Andrej Karpathy for the LLM-wiki pattern that this whole project instantiates.

• Frank Chu's MindBase for proving the pattern could be a product, and for the CLAUDE.md schema conventions we borrow and extend.

Paperloom differs by being folder-scoped (one KB per directory, no global state), batch-ingest-first (built for corpora of 50-1000 papers), and never requiring an LLM API key of its own.

See docs/credits.md for the full story.

Quickstart

Not yet on PyPI — install from source (see Install below), then:

mkdir my-vault && cd my-vault
paperloom init
paperloom ingest ~/Downloads/papers/

paperloom init doesn't create .mcp.json for you — add it yourself (one-time, per vault):

cat > .mcp.json << 'EOF'
{ "mcpServers": { "paperloom": { "command": "paperloom", "args": ["mcp"] } } }
EOF

Then point your coding agent at the vault and start with /contribute or just ask it what's in the wiki. See docs/quickstart.md for the full walkthrough.

What it is / isn't

It is:

• A set of file-manipulation MCP tools (search, read_page, create_note, ...) plus a CLI for batch PDF ingestion.

• Folder-scoped — every vault is a self-contained directory, no global state, no daemon.

• Zero-API-key by design — the host coding agent is the LLM.

• Built for real corpora — batch ingest, resumable, parallel MinerU jobs, per-paper failure isolation.

It isn't:

• A web UI. Point Obsidian at the vault if you want one.

• A vector database or semantic search engine. Ripgrep + agent reasoning covers real usage up to hundreds of papers; see the build spec's non-goals if you're curious why this is deliberate.

• Its own LLM router. The Ollama plugin (v0.2) is the only "paperloom calls an LLM directly" path, and it's opt-in, for headless jobs only.

• Multi-user, auth'd, or a SaaS. paperloom mcp is stdio-only, one process per client.

Install

Not yet published to PyPI. Clone (or copy) this repo, then install with uv, not plain pip — verified directly: a fresh pip install . genuinely fails with a resolution-too-deep error (pip's resolver can't handle the combined dependency graph of mineru[core] + fastmcp together), while uv pip install . resolves the exact same graph cleanly in a few minutes.

git clone https://github.com/Alpsource/paperloom
cd paperloom

curl -LsSf https://astral.sh/uv/install.sh | sh   # if you don't have uv yet
uv venv
uv pip install .
source .venv/bin/activate

(uv pip install -e . instead of . if you want to hack on paperloom itself — see CONTRIBUTING.md.)

You also need ripgrep on PATH — it's a system binary, not a pip package:

# Debian/Ubuntu
sudo apt install ripgrep
# macOS
brew install ripgrep
# Fedora
sudo dnf install ripgrep

Optional extras:

uv pip install "paperloom[ollama]"   # offline synthesis via a local Ollama model
uv pip install "paperloom[grobid]"   # bibliography extraction via GROBID
uv pip install "paperloom[dev]"      # pytest, ruff, mypy, pre-commit, mkdocs-material, pip-audit

mineru[core] (the actual local PDF parser, pulled in automatically as a core dependency) is heavy — it installs PyTorch, and downloads several GB of model weights the first time it actually parses a PDF. There's no way around this if you want local PDF parsing; budget the disk space and time (and, ideally, a GPU — CPU-only parsing works but is much slower) for that first real paperloom ingest run.

Tested primarily on Linux; Windows works via WSL2 (see the build spec's own notes) but isn't the primary target.

First vault (5 minutes)

mkdir my-research && cd my-research
paperloom init

This copies the scientific-paper-vault template in: CLAUDE.md (the schema — see below), empty context.md/index.md, and the sources//artifacts//logs/ skeleton. It also writes .paperloom/config.yaml and runs git init if you haven't already.

paperloom ingest ~/Downloads/some-papers/

Every PDF gets parsed by MinerU into sources/raw/<paper-id>/paper.md + meta.json. IDs are detected from the arXiv/DOI pattern on the first page when possible, falling back to a content hash. This step never touches sources/research/ — ingestion and wiki-writing are deliberately separate.

claude "/contribute sources/raw/2301.08243"

Your coding agent reads CLAUDE.md, drafts a plan (which pages to create, which to update), shows it to you, and on approval writes real wiki pages via the MCP tools. Repeat for more papers, then try:

claude "What does my wiki know about JEPA?"

See examples/ml-robotics-vault/ for a fully populated example vault you can browse instead of building one from scratch.

Architecture

graph LR
    PDF[Original PDF] -->|paperloom ingest, MinerU| RAW
    subgraph RAW["sources/raw/<paper-id>/  (immutable)"]
        direction TB
        R1[paper.pdf]
        R2[paper.md]
        R3[meta.json]
    end
    RAW -->|"/contribute — host agent reads, writes"| RESEARCH
    subgraph RESEARCH["sources/research/  (agent-owned)"]
        direction TB
        W1[paper pages]
        W2[method pages]
        W3[dataset / concept / synthesis pages]
    end
    USER[You] -->|daily notes| CONTRIB["sources/contributors/<you>/"]
    CONTRIB -.->|"/contribute"| RESEARCH

Three layers, three trust levels: sources/raw/ is a faithful, never-edited transcription; sources/research/ is where the agent's actual judgment lives, always citing back to raw/; sources/contributors/ is your own daily log, appended to but never rewritten. See docs/schema.md for the full page-shape reference.

The 9 tools

That's the whole list, on purpose — see the build spec for what's deliberately not a core tool (semantic search, auto-linting fixups, multi-user anything) and why.

Plugins

Need a tool beyond the 9? Write a plugin — a Python module exposing register(mcp), loaded from three places (built-in, third-party via pip entry points, or vault-local in .paperloom/plugins/) with the later ones overriding the earlier on a name collision. See docs/plugins.md for the full guide and the reference example_plugin.py (word_count, find_orphans).

Ollama backend

For headless/scheduled jobs (nightly /rebuild-context, a cron'd /lint) where no host agent is actively driving the session, uv pip install "paperloom[ollama]" adds a synth tool that runs a prompt through a local Ollama model — no API key, fully offline. Use it for mechanical grunt work; the interactive host agent is still where the actual judgment happens. (v0.2 — not yet built; tracked as §17 item 10 in the build spec.)

Migrating from MindBase

paperloom migrate-from-mindbase ~/mindbase-data/projects/my-research/

Copies (never moves) sources/raw/, sources/research/, sources/contributors/, context.md, README.md, and logs/ into a new paperloom vault, re-deriving indices from disk rather than trusting MindBase's index.yaml. (v0.2 — not yet built; tracked as §17 item 9 in the build spec.)

Optional: browse your vault visually

Paperloom vaults are plain markdown with [[wikilinks]], so Obsidian works on one out of the box:

1. Open Obsidian → "Open folder as vault" → your paperloom vault root.

2. Optionally install the Dataview plugin — the YAML frontmatter is Dataview-queryable.

3. Ctrl-G for the graph view.

Not required, not depended on — just a happy accident of the file format.

Roadmap

Planned plugins (v0.3+, community-contributable), not core-tool additions:

• arxiv_watcher — poll arXiv for new papers matching saved queries.

• marp_export — turn a synthesis page into a Marp slide deck.

• graph_export — export the [[wikilink]] graph as GraphViz/JSON.

• citekey_lint — validate \cite{...} references in draft artifacts.

Core (the 9 tools, the CLI, the plugin system, the schema) is considered done as of v0.1 — see CHANGELOG.md.

Contributing

See CONTRIBUTING.md — setup, test commands, and what's pinned by the build spec vs. open for change. Issues and PRs welcome, especially plugins.

License

Apache-2.0.

Citation

@software{paperloom,
  title  = {Paperloom: a folder-scoped, LLM-maintained research wiki},
  author = {{paperloom contributors}},
  year   = {2026},
  url    = {https://github.com/Alpsource/paperloom}
}