Arkon
Integrates Google Gemini models as a provider for language model, embedding, and vision capabilities.
Integrates OpenAI models (GPT-4, GPT-5) as a provider for language model, embedding, and vision tasks.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Arkonsearch the wiki for the employee onboarding process"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Arkon - The Open-Source Enterprise AI Knowledge Hub & MCP Server
Arkon is a self-hosted, enterprise-grade knowledge management layer that bridges organizational data and AI clients. It runs as a centralized MCP Server (Model Context Protocol), compiling your SOPs, policies, and internal docs into a structured, traceable knowledge wiki - then serving that wiki to Claude and other LLMs through a single permission-scoped endpoint.
π Why Arkon?
In most organizations, AI adoption is fragmented. Employees copy-paste documents into chatbots, producing inconsistent context, security risks, and duplicated work.
Arkon treats AI as a managed organizational resource. Every employee gets the right context, automatically and securely - filtered by their department and role.
Related MCP server: deeplore
β¨ Key Features
π§ Intelligent Knowledge Wiki - the MRP Pipeline
Unlike a vector database that just chunks and indexes, Arkon's MRP pipeline (Map β Reduce β Plan-review β Refine β Verify β Commit) actually compiles documents into a coherent wiki of interlinked pages.
Plan review before write: every ingestion produces a human-reviewable plan listing which wiki pages will be created or updated. Editors can regenerate the plan with feedback before any page is written.
Page merge instead of overwrite: when a new source touches an existing wiki page, content is LLM-merged so prior knowledge is never lost.
Traceable claims: every page records the source documents it was compiled from.
Image-aware: vision captions are baked into source text before compilation, so wiki pages reference the right images in the right places.
Resumable: drafts persist mid-pipeline; a crashed run resumes without re-doing the expensive LLM work.
π Wiki Browser & Knowledge Graph
Three-panel layout: page tree, content, backlinks & outlinks.
Full-text + semantic (pgvector) search.
Interactive knowledge graph visualization (per-scope or global).
Wikilink-style cross-references between pages.
Version history and rollback on every page.
Draft proposal β editor review β approval workflow.
π’ Department & Global Scopes
Precise context boundaries with their own scoped wiki, document set, and department rosters.
Department-level isolation for HR, Legal, Engineering, etc.
Global scope for company-wide SOPs, policies, and general knowledge.
Hard scope enforcement: members only see knowledge from their assigned departments or the global realm - at the API, MCP, and search layers.
π Fine-Grained RBAC
Role-based access control at the department and global levels.
Built-in roles: Viewer Β· Contributor Β· Editor Β· Admin to control draft, edit, and setting access.
Granular permissions (
doc:read:own_dept,wiki:edit:all,org:settings:manage, ...).Audit log for every privileged action - settings changes, plan approvals, and role updates.
π MCP Server for Claude & Other AI Clients
Employees connect Claude Desktop or Claude.ai to Arkon via OAuth 2.1 + PKCE β just add the server URL and sign in through the browser. No manual token copying required. The MCP server exposes:
Wiki tools -
search_wiki,read_wiki_page,list_wiki_pages,read_wiki_index.Source drill-down -
get_source,get_source_outline,get_source_pages,list_sources.Edit workflow -
propose_wiki_edit,edit_wiki_page,list_pending_drafts,review_draft,approve_draft,reject_draft.Discovery -
list_knowledge_types,get_knowledge_type_docs.
All tools enforce per-token scope (department, knowledge type, source list).
π§° AI Skills Distribution
Upload custom agent packages once and distribute them across the org.
Versioned skill packages (.zip with
SKILL.md).Department-scoped visibility.
Contribution workflow for end-user updates.
π€ Pluggable AI Providers
Catalog-driven selection - admins pick from a curated list with context window, cost, and capability metadata on display.
LLM: Anthropic Claude (Opus / Sonnet / Haiku 4.x), Google Gemini (3.x Pro / Flash / Flash-Lite, 2.5), OpenAI (GPT-5.x, GPT-4.x).
Embedding: Google
gemini-embedding-*, OpenAItext-embedding-3-*- switchable with online re-embed migration (active model atomically flipped on completion, no zero-result search window).Vision: Google Gemini Flash, OpenAI GPT-4o family.
π Privacy & Security First
Self-hosted. Deploy on-prem or in your private cloud via Docker.
No telemetry. Outbound traffic goes only to the AI provider you choose.
Encrypted at rest. API keys stored with Fernet encryption in PostgreSQL.
π οΈ Tech Stack
Backend: FastAPI Β· PostgreSQL + pgvector Β· Redis (arq workers) Β· MinIO
Frontend: Next.js Β· Tailwind CSS
AI integration: Model Context Protocol via FastMCP
Document parsing: PDF, DOCX, DOC, plain text, URLs, embedded images
π» Server Requirements
Arkon runs 7 Docker containers (PostgreSQL + pgvector, Redis, MinIO, FastAPI API, 2 ARQ workers, Next.js frontend). The table below provides recommended configurations based on team size:
Starter | Team | Enterprise | |
Team size | 1 β 20 | 20 β 100 | 100+ |
vCPU | 2 cores | 4 cores | 8+ cores |
RAM | 4 GB | 8 GB | 16+ GB |
Storage | 40 GB SSD | 100 GB SSD | 250+ GB NVMe SSD |
OS | Ubuntu 22.04+ / Debian 12+ | Ubuntu 22.04+ / Debian 12+ | Ubuntu 22.04+ / Debian 12+ |
Use case | Evaluation / small teams | Departmental deployment | Organization-wide rollout |
RAM is the primary bottleneck β the MRP pipeline workers load large LLM context windows into memory during wiki compilation.
Storage scales with your document corpus β pgvector indexes, MinIO file storage, and PostgreSQL WAL logs are the main consumers.
All AI inference happens externally (Anthropic / Google / OpenAI APIs), so GPU is not required.
A reverse proxy (Nginx / Caddy) with SSL is recommended for production. See Setup Guide.
π¦ Quick Start (Docker)
Arkon is built for teams, not individuals. If you're looking for a personal knowledge setup, Obsidian + Claude Skills is a much simpler fit.
Not a tech person? Skip the self-hosting hassle β reach out for a free guided demo tailored for your team. No config, no Docker, just a walkthrough of what Arkon can do for your organization.
Prerequisites: Docker & Docker Compose, plus an API key from your preferred AI provider (Anthropic, Google, or OpenAI).
Clone the repository:
git clone https://github.com/nduckmink/arkon.git cd arkonConfigure environment:
cp .env.docker.example .env.docker # Edit .env.docker - set SECRET_KEY, admin credentials, and Postgres/MinIO secretsLaunch:
docker compose --env-file .env.docker up -d --buildAccess the portal at
http://localhost:3119, sign in as admin, then go to Settings to pick your embedding / LLM / vision models and paste API keys.
β See Setup Guide for development mode and the full env reference.
π Connecting Claude
In Claude Desktop or Claude.ai β Settings β Connectors, add a custom connector:
Name:
ArkonURL:
https://your-arkon-server/mcp
Click Connect β a browser window opens with the Arkon login form β sign in with your Arkon credentials β done. Arkon uses OAuth 2.1 + PKCE so no manual token setup is required.
To make Claude consistently use Arkon, add this to Claude's Custom Instructions (Settings β Custom Instructions):
Whenever answering questions related to the company β its processes, products,
people, departments, policies, or projects β always search Arkon first using
the search_wiki tool before relying on general knowledge.For stronger enforcement, create a Project in Claude Desktop, attach Arkon as a connector, and paste the same text as Project Instructions.
β See MCP & Claude for the complete tool reference.
πΊοΈ Roadmap
MRP Pipeline - deterministic compilation with plan review, page merge, and resume-on-crash.
MCP Server - scoped wiki + source + draft tools.
Department & Global Scopes - department isolation with RBAC.
Wiki draft & revision workflow - propose, review, approve, rollback.
AI Skills - versioned, department-scoped agent packages.
Catalog-driven model selection - LLM, embedding, and vision picked from a curated list with cost/context-window metadata.
Online embedding migration - atomic re-embed with no search downtime.
Audit log - privileged actions tracked.
Rich media ingestion - bulk folder upload with images, videos, and Excel/spreadsheet parsing baked into the MRP pipeline.
Usage analytics dashboard
External data import - connectors for SharePoint, Google Drive, Notion, and other common org data sources.
Arkon CLI - one-command setup for employees.
Notification system - for draft reviews and plan approvals.
β Star History
π License
Arkon is licensed under the PolyForm Internal Use License 1.0.0. Free for internal business operations; you may not offer Arkon as a service to third parties.
For enterprise support or custom integrations, please contact the maintainers.
Keywords: Enterprise AI, Model Context Protocol, MCP Server, Knowledge Management System, Self-hosted RAG, AI Knowledge Base, Claude MCP, LLM Context Management, Open Source Wiki.
π¬ Contact
Main Author Minh Nguyen (Nguyα» n Δα»©c Minh) β Vietnam (GMT+7) Β· Fluent English βοΈ duckmink.bitsness@gmail.com
Company BITSNESS TECHNOLOGY AND SOLUTIONS COMPANY LIMITED βοΈ bitsness.ad@bitsness.vn
For enterprise inquiries, demo requests, or custom integrations β email us directly.
Available Tools
1 toolextract_contentA
Extract content from a URL or file using Content Core's auto engine.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | Optional URL to extract content from | |
| file_path | No | Optional file path to extract content from |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for disclosing behavior. However, it only mentions 'auto engine' without explaining output format, error handling, authentication requirements, or side effects. This leaves significant behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence that front-loads the core action and resource. It contains no fluff or redundant information, earning a perfect score for conciseness and structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple with two optional parameters and an output schema present, so the description covers the basic purpose. However, it does not clarify that at least one of url or file_path is expected, nor does it indicate what happens if both are omitted. This is a notable gap in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers both parameters fully (100% coverage), so the baseline is 3. The description adds the relationship that the two parameters are alternatives ('a URL or file'), which is useful but not substantial. Most parameter meaning is already available in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'extract' and clearly names the resource ('content from a URL or file'). It conveys the tool's primary function unambiguously. With no sibling tools present, there is no need for differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'using Content Core's auto engine' implies the tool is for automated extraction, but there is no explicit guidance on when to use this tool versus alternatives. Since no sibling tools exist, the absence of explicit comparisons is acceptable, but context is still only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Only one tool exists, so there is no possibility of confusion between tools.
The single tool uses a clear verb_noun pattern (extract_content), and with only one tool, there are no inconsistent naming conventions.
A single tool is on the thin side, but for a focused content extraction purpose, it can be acceptable. However, it lacks the typical 3-15 tool scope.
The tool covers the core function of extracting content from both URLs and files. Some potential advanced features are missing, but for the stated purpose, it is reasonably complete.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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