lacuna-wiki 2.0.7

Give your agent a knowledge graph that compounds.

Give your agent a knowledge graph that compounds.

Drop in a URL. Your agent handles the rest.

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Lacuna is a single MCP tool — wiki — that you plug into your existing agent harness (Claude Code, Hermes, OpenClaw) to give it a searchable, compounding personal research graph. Feed it a YouTube URL, an arXiv link, or a downloaded PDF. Your agent runs the structured extraction. The knowledge accumulates across every session.

Your vault is plain markdown — works natively with Obsidian. Browse your knowledge graph as a human, query it as an agent. Same files, no sync required.

It is the lacuna — the missing link between your raw inputs and your second brain.

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Quick Start

pip install lacuna-wiki
lacuna init ~/my-vault

init creates your vault directory structure, sets up the DuckDB index in ~/.lacuna/, and asks whether to wire the MCP server into Claude Code and/or Hermes automatically. Takes about 10 seconds.

Then wire it into your agent harness manually if needed:

Claude Desktop / Claude Code (~/.claude/mcp.json)

{
  "mcpServers": {
    "lacuna": {
      "command": "lacuna",
      "args": ["mcp"],
      "env": { "LACUNA_VAULT": "/path/to/my-vault" }
    }
  }
}

Hermes (~/.hermes/config.yaml)

mcp_servers:
  lacuna:
    command: lacuna
    args: [mcp]
    env:
      LACUNA_VAULT: /path/to/my-vault

OpenClaw

openclaw mcp set lacuna '{"command":"lacuna","args":["mcp"],"env":{"LACUNA_VAULT":"/path/to/my-vault"}}'

lacuna init detects Claude Code, Hermes, and OpenClaw and offers to wire this config automatically.

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What Your Agent Unlocks

Once connected, your agent gets one composable tool:

wiki(q="attention mechanisms")          # hybrid semantic + keyword search
wiki(page="transformer-architecture")   # navigate to a specific page
wiki(pages=["sdpa", "flash-attn"])      # pull multiple pages in one shot
wiki(q="...", scope="sources")          # search raw source chunks directly

That's it. One tool. Your entire research graph.

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Omnivorous Inputs

Feed Lacuna anything — it knows what to do:

Source	Command
📺 YouTube URL	lacuna add-source https://youtube.com/watch?v=...
📄 arXiv link	lacuna add-source https://arxiv.org/abs/2310.06825
📑 Local PDF	lacuna add-source ~/papers/my-paper.pdf
🌐 Any URL	lacuna add-source https://example.com/blogpost

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The Structured Skills

This is where Lacuna is different from dropping a folder of PDFs into a vector store.

Lacuna ships with agent skills for Claude Code and Hermes that encode a structured, multi-turn extraction workflow — not "summarize this" but a disciplined process that produces tagged, wikilinked pages with full citations. When your agent ingests a paper, it follows the skill's protocol: pulling core concepts, mapping relationships to your existing graph, and flagging gaps.

Install them into your harness:

lacuna install-skills --claude-global    # → ~/.claude/skills/
lacuna install-skills --hermes-global    # → ~/.hermes/skills/
lacuna install-skills --openclaw-global  # → ~/.openclaw/skills/
lacuna install-skills --hermes PATH      # custom Hermes skills directory

Skills included:

• ingest — structured multi-turn knowledge extraction from a source
• query — cited, honest answers from your graph (flags what's missing)
• adversary — re-verifies old claims against their cited sources

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The Compounding Graph

Lacuna outputs aren't isolated notes. Each extraction is structured to deliberately compound — new pages wikilink to existing ones, concepts accumulate across sessions, and the graph gets richer with every source you add.

Under the hood: hybrid BM25 + vector search over a DuckDB store. No format lock-in — your vault is just a folder.

my-vault/
├── wiki/                  # compiled knowledge pages (Obsidian-readable)
│   ├── attention.md
│   ├── transformer-architecture.md
│   └── ...
├── raw/                   # original sources
│   ├── vaswani2017/
│   └── ...
└── .lacuna.toml           # vault config

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Embedding Backend

Lacuna needs an OpenAI-compatible embeddings endpoint. The easiest path is Ollama:

# Install Ollama: https://ollama.com/download
ollama pull nomic-embed-text:v1.5

Then set your vault's .lacuna.toml (created by lacuna init):

[embed]
url = "http://localhost:11434"   # Ollama's default port
model = "nomic-embed-text:v1.5"  # default — can omit
dim = 768                         # default — can omit

LACUNA_EMBED_URL, LACUNA_EMBED_MODEL, and LACUNA_EMBED_DIM env vars also work for one-off overrides.

Changing models? Set embed.dim in .lacuna.toml before running lacuna init — the schema is created from that value. Changing the model or dim after ingesting sources will invalidate existing embeddings. A lacuna reindex command to re-embed everything in place is planned; for now, delete ~/.lacuna/vaults/<your-vault>/ and re-run lacuna init to start fresh.

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Requirements

• Python 3.11+
• pdftotext (poppler-utils) for PDF extraction: apt install poppler-utils / brew install poppler
• An embedding server (Ollama, OpenAI, or any OpenAI-compatible endpoint)

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Status

Early release. The core loop — add source → agent ingests → agent queries — is solid. The structured skills are where the value is; treat them as opinionated defaults you can adapt.

Windows support is in progress (Linux/macOS fully supported today).

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License

MIT © Markus Williams, 2026