Self-Hosted · Private · MCP-Native

Your Knowledge.
Your Server.
Your Rules.

RagGo brings AI-powered semantic search to your documents — fully private, self-hosted, and zero cloud dependency. For universities, teams, and anyone who can't compromise on data privacy.

↓ Download Free View Pricing
Runs Locally No Cloud Required MCP Compatible
raggo — terminal
The Problem

The Privacy Dilemma

Organizations that need private AI search the most are the ones least served by current options.

Option A

Cloud RAG Services

OpenAI Assistants, Pinecone, Weaviate Cloud. Powerful, but your documents leave your building. FERPA, HIPAA, and most institutional data policies prohibit it.

Privacy risk
Option B

DIY Stacks

LangChain + Chroma + custom glue code. Private, but weeks of engineering — vector databases, embedding pipelines, custom interfaces. Inaccessible for most teams.

Too complex
Option C

Enterprise Platforms

Glean, Coveo, and similar. They solve both problems — but at $30k+ per year. Built for Fortune 500, priced for Fortune 500. Research labs and small firms can't justify it.

Wrong price point

RagGo: Turnkey, private, and accessible.

Install in minutes. Ingest your documents. Search with AI. All on your hardware. Starting at $0.

Get Started Free →
Capabilities

Everything you need,
nothing you don't.

A complete, production-grade RAG stack — not a demo, not a proof of concept.

Document RAG

Ingest PDFs, Markdown, code files, and plain text. Semantic search returns the most relevant chunks ranked by meaning — not just keywords.

Graph RAG

Explore class, function, and module relationships through an API knowledge graph. Understand your codebase at the architecture level, not just file-by-file.

MCP-Native

Drop-in backend for any MCP-compatible LLM client — Claude, Cursor, and more. Document search, graph queries, and system metrics exposed as standard MCP tools.

Web UI

Full browser-based interface for ingestion, semantic search, service monitoring, and configuration. No command line required for everyday use.

Bundled Qdrant

The vector database downloads and starts automatically. No Docker, no separate installs, no infrastructure knowledge needed. Just run RagGo.

Local-First Security

Binds to localhost by default. Self-signed TLS for internal gRPC. Your data is never routed through a third-party server — by architecture, not just policy.

Data Sovereignty

Your data never
leaves your hardware.

Not a marketing claim — a technical guarantee. RagGo's architecture makes external data transmission structurally impossible in default mode.

0
External API calls during document processing
100%
Local — embeddings, vector search, and storage
1
Executable to install. No cloud accounts, no containers.
Getting Started

Up and running
in under 10 minutes.

1

Install

Download the Windows installer or Linux package. Run it. RagGo auto-detects your environment and downloads Qdrant automatically.

./raggo.exe install
2

Ingest

Point RagGo at a folder, file, or codebase. It chunks, embeds, and indexes your documents locally. No size limits on paid plans.

raggo ingest ./my-docs/
3

Search

Open the Web UI or connect your MCP client. Ask questions in plain English. Get semantically ranked answers from your own data.

raggo search "your query"
Use Cases

Built for privacy-first teams.

From solo researchers to institutional IT — RagGo adapts to your context.

Research without compliance risk.

Universities handle research IP, student records (FERPA), and grant-funded data that institutional policy often prohibits from leaving campus networks. RagGo gives you AI-powered search that runs entirely on your own infrastructure.

  • Ingest research papers, datasets, and lab code in a private collection
  • Semantic search across interdisciplinary archives without cloud exposure
  • Library-managed deployment for course materials and institutional documents
  • Path to SSO/SAML integration for institutional identity providers (Phase 2)
Example query
$ raggo search "quantum error correction superconducting"

Found 7 results · 38ms

[1] Chen et al. 2024 — 96%
    /papers/chen-qec-2024.pdf · p.12

[2] MIT Lab Preprint — 91%
    /papers/mit-qc-preprint.pdf · p.3

[3] Noise-Resilient Gates Survey — 87%
    /papers/patel-2023-gates.pdf · p.8

Client data stays with you.

Law firms, accounting practices, healthcare clinics, and financial advisors handle data subject to strict confidentiality requirements. RagGo gives professional services teams AI-powered search without regulatory risk.

  • Search client contracts, filings, and correspondence with semantic precision
  • Maintain compliance with HIPAA, attorney-client privilege, and financial regulations
  • Team pricing ($15/seat) designed for small professional services firms
  • No vendor lock-in — your data stays in your format on your hardware
Example query
$ raggo search "indemnification clause merger agreement"

Found 5 results · 24ms

[1] Acme Corp MSA 2024 — 94%
    /clients/acme/contracts/msa.pdf · p.8

[2] Standard Merger Template — 88%
    /templates/merger-agreement.pdf · p.14

Private RAG for your MCP toolchain.

RagGo is MCP-native from the ground up. Drop it into your LLM client as a backend, query your codebase with GraphRAG, or use it as a private knowledge base for your AI assistant — all with a standardized API.

  • MCP endpoint works with Claude, Cursor, and any MCP-compatible client
  • GraphRAG for exploring class/function relationships in your codebase
  • gRPC microservice for programmatic ingestion and search
  • Runs entirely on CPU — no OpenAI API key required
MCP config (claude_desktop_config.json)
{
  "mcpServers": {
    "raggo": {
      "command": "raggo",
      "args": ["mcp"]
    }
  }
}
Pricing

Start free.
Scale when ready.

No credit card required to get started. Upgrade when your use case demands it.

Free
$0
Forever
  • Limited document ingestion
  • Text & Markdown files
  • Single user
  • Web UI access
  • MCP endpoint
Download Free
Individual
$20
per month
  • Unlimited ingestion
  • All file types incl. PDF
  • Single user
  • GraphRAG access
  • Priority support
Get Individual →
Team
$15
per seat / month (10+ seats)
  • Everything in Individual
  • 10+ seat minimum
  • Shared collections
  • Usage dashboard
  • Volume discounts
Contact Sales
Institution
Custom
university-wide
  • Everything in Team
  • SSO / SAML integration
  • Admin dashboard
  • Compliance documentation
  • Dedicated support
Talk to Us
FAQ

Common Questions

No. In default mode, RagGo binds to 127.0.0.1 and all processing — chunking, embedding, and vector search — happens on your hardware. The only external network activity is the optional one-time download of Qdrant during first-run setup.
RagGo bundles a local embedding model that runs on CPU — no GPU required, no OpenAI API key needed. The ability to bring your own embedding model is on the roadmap for future releases.
The free tier supports plain text and Markdown. Paid tiers support PDFs, code files (Go, Python, JavaScript, TypeScript, and more), and additional formats. Full PDF page extraction is on the Phase 1 roadmap.
MCP (Model Context Protocol) is a standard for connecting LLM clients to external tools and data sources. If you use Claude, Cursor, or a similar AI assistant, MCP lets RagGo appear as a native search backend. It is entirely optional — you can use RagGo exclusively through the Web UI if you prefer.
Currently RagGo supports Windows and Linux. macOS support is a Phase 1 priority given the developer and academic Mac user base, and is actively being worked on.
Your data is yours — it lives entirely on your hardware. If you downgrade or stop subscribing you retain access to everything you've ingested; you may lose access to premium features like GraphRAG or unlimited ingestion. Nothing is ever deleted by us because we don't have access to it in the first place.

Start for free.
Own your search.

No credit card. No cloud account. No compromise.

↓ Download RagGo