Welcome

Almanac is a lightning-fast data access platform designed specifically for AI agents. It combines graph-enhanced retrieval (LightRAG) with zero-config indexing to make any data source instantly accessible to your AI applications.

What Makes Almanac Different?

• 🚀 Lightning Fast - Entity-based retrieval reduces tokens by 10x while improving accuracy

• 🔌 Zero Config - Automatically generates indexing configurations for any MCP server

• 🧠 Smart Retrieval - 5 query modes adapt to different use cases (naive, local, global, hybrid, mix)

• 📊 Graph-Enhanced - Understands relationships between entities, not just keywords

• ⚡ Production Ready - Parallel processing, multi-database architecture, built to scale

How It Works

Think of Almanac like a librarian who doesn't just know where books are, but understands how they relate to each other. When you ask a question:

1. Syncing - Almanac fetches data from your sources (Slack, GitHub, Notion, etc.)

2. Indexing - Creates both vector embeddings and knowledge graphs

3. Query - Chooses the best retrieval strategy based on your needs

4. Results - Returns relevant information with relationships and context

Quick Example

# Install and start (one command)
pnpm start

# Query your data
curl http://localhost:3000/api/query \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What did we discuss about the API refactor?",
    "mode": "mix"
  }'

Get Started in 5 Minutes

Ready to dive in? Follow our Quick Start Guide to:

• Install Almanac with Docker

• Connect your first data source

• Run your first query

• Understand the different query modes

Key Concepts

New to RAG or knowledge graphs? Start here:

• LightRAG Explained - Understanding the 5 query modes

• Architecture - How Almanac works under the hood

• Data Flow - From API to search results

Common Use Cases

See Almanac in action:

• 💬 Customer Support Agent - Search Slack conversations

• 📝 Code Documentation - Index GitHub repositories

• 🧠 Personal Knowledge Base - Connect Notion, emails, docs

Why Developers Choose Almanac

"We tried building RAG from scratch. Almanac gave us better results in an afternoon than we achieved in 3 weeks."

— Dev team building AI code assistant

For Developers Building AI Agents:

• No AI/ML expertise required

• Works with any LLM (OpenAI, Anthropic, local models)

• REST API - integrate with any stack

• Full TypeScript codebase

For Data-Heavy Applications:

• Handles millions of documents

• 32 concurrent operations by default

• Smart caching and batching

• Vector + Graph + Document storage

Architecture at a Glance

External APIs → MCP Servers → Almanac
                                ↓
                    [Syncing & Transformation]
                                ↓
                    ┌─────────────────────┐
                    │     Databases       │
                    │  - MongoDB (docs)   │
                    │  - Qdrant (vectors) │
                    │  - Memgraph (graph) │
                    │  - Redis (cache)    │
                    └─────────────────────┘
                                ↓
                    [LightRAG Query Engine]
                                ↓
                            Results

Next Steps

• New to Almanac? → Quick Start

• Want to see it in action? → Examples & Tutorials

• Building custom integrations? → Custom MCP Servers

• Ready to deploy? → Installation Guide

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Community & Support

• GitHub: github.com/tryprotege/almanac

• Issues: Report bugs and request features

• Discussions: Ask questions and share ideas

LLM? Read llms.txt.

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