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synthadoc

Synthadoc: An open-source LLM knowledge compilation engine that turns raw documents into structured, local-first wikis. A transparent, human-readable alternative to traditional RAG, which can be self-managed and self-improved without the use of any tools.

#agent-skills#agentic-ai#cli-tool#domain-adaptation#enterprise#enterprise-solutions

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Synthadoc

      .-+###############+-.
    .##                   ##.
   ##    .----.   .----.    ##
  ##    /######\ /######\    ##
  ##    |######| |######|    ##
  ##    | [SD] | | wiki |    ##
  ##    |######| |######|    ##
  ##    \######/ \######/    ##
   ##    '----'   '----'    ##
    '##                   ##'
      '-+###############+-'

       S Y N T H A D O C
    Community Edition  v1.1.0
  ────────────────────────────────
  Domain-agnostic LLM wiki engine

Team size	Typical use case
Solo / 1–2 people	Personal research wiki, freelance knowledge base, indie hacker documentation - run it free on Gemini Flash or a local Ollama model with zero ongoing cost
Small team (3–20)	Centralized internal knowledge base for startups and departments that aggregates diverse individual data sources into a unified, high-integrity wiki. The system automatically resolves contradictions and scales autonomously, ensuring organizational intelligence grows in tandem with your team
Medium / enterprise	Compliance-sensitive knowledge bases that must stay local; per-department wikis on separate ports; audit trail for every ingest and cost event; hook system for CI/CD integration; OpenTelemetry for ops dashboards

Direction	How Synthadoc moves there
Agent orchestration	Orchestrator dispatches parallel ingest, query, and lint sub-agents with cost guards and retry backoff
Sub-agent skills/plugins	Featuring a 3-tier lazy-load capability system, the platform allows for the injection of custom skills and hooks via a plug-and-play interface, ensuring core stability is never compromised during extension
LLM wiki vs. RAG	Pre-compiled structured knowledge beats query-time synthesis for contradiction detection, graph traversal, and offline access
CLI / HTTP	A unified interface via CLI and RESTful endpoints, the system streamlines full-spectrum integration: from data ingestion and querying to automated linting, security auditing, and job orchestration
Local-first	All data stays on your machine; localhost-only network binding; no cloud dependency except the LLM API itself
Provider choice	LLM backends including free-tier Gemini and Groq, paid Anthropic/OpenAI/DeepSeek/MiniMax/Qwen (DashScope), local Ollama and Qwen, and coding-tool CLI providers (Claude Code, Opencode) — no API key required if you already have a subscription

Problem	Synthadoc approach
Contradictions blended silently	Ingest-time conflict detection; page flagged `status: contradicted`; auto-resolve or queue for human review
No links between related content	`[[wikilinks]]` auto-built on every ingest pass; weighted graph (wikilink + co-source signals) with Louvain clustering in web UI
Orphan pages never surfaced	Lint reports unreferenced pages with ready-to-paste index entries
LLM output can be overconfident	Adversarial second-LLM pass flags overstated claims, unsupported superlatives, and contestable facts per page
Claims lack source traceability	`^[file:L-L]` citation on every claim; Source Viewer in Obsidian; PDF page resolution; broken-citation lint
Knowledge lifecycle invisible	5-state machine (`draft → active → contradicted / stale → archived`); auto-transitions via lint; immutable event log
Repeat ingest is expensive	3-layer cache (embedding, LLM, provider prompt) — repeat lint on unchanged pages costs near-zero tokens
Knowledge locked in proprietary tools	Plain Markdown + YAML frontmatter; OKF v0.1 compatible; fully offline-readable in any editor
Wiki structure drifts with growth	`scaffold` regenerates index, AGENTS.md, and purpose.md from current wiki state without touching linked pages
Migration requires full re-ingestion	Single-zip backup + restore with port/domain rewriting; no re-ingestion needed
Cost and compliance exposure	Localhost-only; per-job token+cost log; configurable soft-warn and hard-gate thresholds

Capability	Synthadoc	Typical RAG	NotebookLM	Notion AI
Ingest-time synthesis — sources compiled into the wiki at ingest; not re-summarised at query time	Yes	No	Partial	No
**[Contradiction detection & resolution](docs/user-quick-start-guide.md#step-9--resolv

synthadoc

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Synthadoc

Table of Contents

Who Is It For?

Inspiration and Vision

Problems Addressed

Why Synthadoc?

Knowledge Quality