synthadoc
axoviq-ai · axoviq-ai/synthadoc
Compiles raw documents like PDFs, spreadsheets and web pages into a local Markdown wiki with cross-references and cited answers, usable via CLI, Obsidian, or MCP.
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.
Preview
What you can do with it
- Turn a folder of PDFs and Word files into a searchable personal wiki
- Build a project knowledge base your agent can query over MCP
- Detect contradictions and orphan pages across accumulated documents
Safety check
No risky patterns found in the files we scanned.
Automated scan of the repo files. Always review code you run.
From the README
Synthadoc
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## | [SD] | | wiki | ##
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S Y N T H A D O C
Community Edition v1.4.1
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Domain-agnostic LLM wiki engine
Document version: v1.4.1
Engineered for solo users and enterprises alike, providing a domain-specific knowledge base that scales seamlessly while maintaining accuracy through autonomous self-optimization.
Built for individuals, small teams, and large organizations who need a knowledge base that stays accurate as documents accumulate.
Synthadoc reads your raw source documents — PDFs, spreadsheets, PPTs, web pages, images, videos, Word files, TXTs, and AI session transcripts (.jsonl) — and uses an LLM to synthesize them into a persistent, structured wiki. Cross-references are built automatically, contradictions are detected and surfaced, orphan pages are flagged, and every answer cites its sources. Outputs are stored as local Markdown files, ensuring seamless integration and autonomous management within Obsidian or any wiki-compliant ecosystem.
▶ From Documents to Wiki
▶ Four Interfaces: CLI, Obsidian, Web UI & MCP
▶ Agentic Maintenance Workflow
📝 Blogs & Media — YouTube · Coderlegion · DEV.to · Medium
📂 End-to-end Example — AquaFlow Capital M&A due diligence walkthrough
Table of Contents
- Who Is It For?
- Inspiration and Vision
- Problems Addressed
- Why Synthadoc?
- Architecture
- What's Included
- Installation
- Quick-Start Guide
- Creating Your Own Wiki
- Configuration
- Command Reference by Use Case
- Administrative Reference
- Understanding Logs and the Audit Trail
- Customization
- Links
Who Is It For?
Synthadoc scales from a single researcher to a company-wide knowledge platform:
| 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 |
No cloud account. No vendor lock-in. The wiki is plain Markdown — open it in any editor, back it up with git, sync it with any cloud drive.
Inspiration and Vision
"The LLM should be able to maintain a wiki for you." — Andrej Karpathy, LLM Wiki gist
Most knowledge-management tools retrieve and summarize at query time. Synthadoc inverts this: it compiles knowledge at ingest time. Every new source enriches and cross-links the entire corpus, not just appends a new chunk. The wiki is the artifact — readable, editable, and browsable without any tool running.
Long-term alignment:
| 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 know |