Semantic and Text Search
GigaRAG searches by both semantics and text matches and reranks the output to give you clean and accurate results.
Shared Memory for AI Agents
Share your agent memory like you share Google Docs. GigaRAG connects all your agents to a central Hive Mind with access level control.
Memory designed to scale
fast with your agents
GigaRAG Composite Retrieval Engine allows your agents to retrieve information from memory more effectively.
Graph Memory Layer
Lets your AI Agents not just answer direct questions, but reason and understand relationships between topics.
Memory Hierarchy
GigaRAG enforces structure on what would be a cluttered memory space, reducing both retrieval time and off-topic results.
Agentic RAG
GigaRAG is not just a simple search engine. Searches can be chained by your AI agent to precisely find the necessary information.
99.99% Uptime SLA
GigaRAG is built for production workflows. Outages are rare and you can rely on GigaRAG to always be online.
Connects to all your AI tools
Connect via MCP, API, or as a module. GigaRAG is compatible with all modern AI apps.
Shared memory, without
the infra hassle
GigaRAG gives you a beautiful, ready-to-go memory space that takes one click to create and works with all your workflows.
State of the Art Semantics
GigaRAG uses BGE-M3 under the hood, which scores 1.3X higher in retrieval quality than OpenAI's text-embedding-3-large.
Fully Serverless
NEVER worry about server infrastructure. GigaRAG scales with your project requirements.
256-Bit Encryption
AES-256-GCM encryption locks your data at rest and in transit for maximum data privacy.
MCP Integrations
GigaRAG MCP allows you to share your memory between your favorite AI tools.
Millisecond Retrieval
GigaRAG servers measure extremely low latency over the internet.
Auto-Scaling Infrastructure
GigaRAG is built on top of an auto scaling infra to ensure optimal performance, even during peak hours.
FOR DEVELOPERSIntegrate GigaRAG directly
Integrate GigaRAG directly
into your application
Choose a language below to see how easy it is to connect GigaRAG into your app.
POST /v1/search
const res = await fetch('https://api.gigarag.com/v1/search', {
method: 'POST',
headers: {
Authorization: `Bearer ${process.env.GIGARAG_API_KEY}`,
'Content-Type': 'application/json',
},
body: JSON.stringify({ query: 'how do we throttle clients', limit: 5 }),
});
const { results } = await res.json();
console.log(results[0].ref, results[0].title);Got any questions?
Here are some answers to frequently asked questions.
Shared memory for AI agents. Your agents save what they learn as memos, organised into workspaces, buckets and threads, and any agent you connect can search and read them later.
GigaRAG embeds your memos for you, so there is no model to pick or host. The free plan uses multilingual-e5-small and paid plans use BGE-M3.
No. Storage, embedding and indexing are handled for you. There are no clusters to size and no servers to patch.
Anything your agents write through the API or MCP, plus imports from Notion, Google Drive, GitHub, Linear, HubSpot, Zendesk, Intercom, ClickUp, Monday, Box, Discord, Telegram, Fathom, Fireflies, tl;dv, Readwise, Raindrop, RSS feeds and webhooks.
Every search runs full-text and semantic search together and merges the two lists with reciprocal rank fusion, so an exact identifier and a loose paraphrase both find the right memo.
Through the MCP server, the REST API, the GigaRAG CLI, or a quick-connect link you paste into a chat. One connection can reach every workspace you belong to.
Yes. Invite people to a workspace with a role, or share a single bucket with someone to view or edit. API keys can be limited to the buckets you choose.
There is a free plan with up to 100 memos. Pro is $19 a month and Team is $79 a month, each billed per workspace, with weekly and yearly billing also available.
All traffic is encrypted with TLS, every workspace is isolated from every other, and API keys can be revoked at any time. We do not train models on your data.
Yes. A documented REST API covers search, memos, buckets, threads and links, and the same key works with the MCP server. The reference is at gigarag.com/docs/api.