synto 0.1.1

Turn private notes into local knowledge packs and synthesized wikis

Synto

Turn your raw notes into a self-improving, interlinked wiki — powered by a local LLM.

Synto is a knowledge compiler. Point it at any source of expertise and it produces a portable wiki pack any AI agent can install and query.

Local-first, provider-flexible. Runs 100% locally with Ollama or LM Studio. Switch to a cloud provider — Groq, OpenRouter, Mistral — when you need more power. Your notes and source material never leave your machine unless you choose.

Synto succeeds obsidian-llm-wiki-local (608 ★, 9k+ downloads) — same proven local pipeline, redesigned for distributable knowledge packs.

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Why it exists

AI agents are only as good as the context you give them. A folder of notes or a raw PDF is not context — it's noise. Synto processes your source material through a local LLM pipeline, extracts concepts, writes cross-linked articles, and exports a structured directory: articles, an index, a concept graph, and agent-readable metadata. The pack installs like a package. Any agent gets a domain expert layer, not a file dump.

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Use cases

Architectural reference from a textbook

Dobryakov took Tanenbaum's Distributed Systems, compiled it into a cross-linked wiki, and installed it into Claude/Cursor. Instead of generic suggestions, the AI started reasoning from established distributed systems principles — recommending consistent hashing and two-phase commit instead of "use a shared database." Any technical book, spec, or documentation set becomes a live context layer for your AI.

Karpathy's LLM Wiki

Andrej Karpathy's neural-network series is 20 hours of dense, high-quality material. Compile the transcripts and blog posts into a Synto pack and the content becomes queryable: any agent in your project can explain backpropagation, walk through the attention mechanism, or cite a specific lecture — from the source, not from model weights.

Your own research notes

Works today with Markdown. Drop notes in raw/, run synto run, and get a cross-linked wiki exported as an agent-ready pack. Expose it via synto serve as a local MCP server, or ship the pack directory for any file-aware agent to use.

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How it works

Three stages, two LLM tiers:

1. Ingest — fast model reads each source, extracts concepts and summaries
2. Compile — heavy model writes one cross-linked article per concept
3. Export — synto pack export produces an agent-ready directory

The fast model handles analysis (4B parameters is enough). The heavy model handles writing (14B+ recommended locally, or any cloud model). Both tiers are configurable independently.

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Install

pip install synto
# or
uv tool install synto

For MCP server support:

pip install "synto[mcp]"

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Quick start

synto setup                                   # configure LLM provider
synto init ~/my-vault                         # create vault

# add Markdown notes to ~/my-vault/raw/
synto run --vault ~/my-vault                  # ingest + compile into wiki/.drafts/
synto review --vault ~/my-vault               # inspect drafts interactively
# or: synto approve --all --vault ~/my-vault

synto pack export --target agents --vault ~/my-vault
synto serve --vault ~/my-vault                # expose as MCP server (requires [mcp])

If you want a one-command flow, use synto run --auto-approve --vault ~/my-vault. That skips draft review and publishes directly into wiki/.

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What's in a pack

pack/
  articles/           one Markdown file per concept
  index/INDEX.json    machine-readable index with stable article IDs
  agent/
    manifest.json     capabilities, pack metadata
    concepts.json     concept registry
    sources.json      source provenance
  AGENTS.md           agent-readable entrypoint
  CLAUDE.md           Claude Code context file

Any file-aware agent can read the articles directly. INDEX.json enables fast concept lookup without a database. synto serve exposes list_articles, read_article, and find_concept as MCP tools.

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What ships now

• Full ingest → compile → approve pipeline; currently supports Markdown notes and Obsidian vaults
• synto pack export --target agents — portable knowledge pack with INDEX.json and agent metadata
• synto serve — read-only MCP server (list_articles, read_article, find_concept)
• synto query — index-routed Q&A with optional synthesis to wiki/synthesis/
• synto review — interactive draft review: approve, reject, edit, or diff before publishing
• synto watch — file watcher: auto-ingest and compile on every save
• synto maintain — wiki health check, stub creation, orphan cleanup
• synto eval — offline structural evaluation (coverage, citation support, link resolution)
• synto compare — A/B model comparison without touching your vault
• Multi-language: notes are ingested and compiled in their source language
• 20+ LLM providers supported via OpenAI-compatible API

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Data & Privacy

• Local by default. Ollama and LM Studio process all content on your machine — notes never leave it.
• Cloud providers. If you configure an OpenAI-compatible cloud provider, note content and wiki text are sent to that service. Review their privacy policy before use.
• No remote analytics. Synto does not send usage analytics anywhere. Local runtime and cost metrics stay in your vault database.
• Pack exports. Exported packs include raw notes, sources, wiki articles, queries, and synthesis by default. Review your vault before sharing a pack.
• API keys. Stored in ~/.config/synto/config.toml (user-owned, not inside the vault). Never commit that file.

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Requirements

• Python 3.11+
• An LLM provider: Ollama (local), LM Studio, or any OpenAI-compatible endpoint

Recommended local setup: a 4B model for ingest (e.g. Gemma 4), a 14B+ model for compilation (e.g. Qwen 2.5 14B). Works entirely offline.

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Migrate from obsidian-llm-wiki-local

Existing obsidian-llm-wiki-local vaults must be migrated first. Run migrate-olw to copy wiki.toml and .olw/ into the current Synto layout before using normal commands.

synto migrate-olw --vault ~/my-old-vault

Copies wiki.toml → synto.toml and .olw/ → .synto/. Notes and articles are untouched. Old files are preserved — delete them once you've verified everything works.