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Traditional vector databases require you to hand your data to a third party in plaintext. x-vec is different: your document content is AES-encrypted and your embedding vectors are homomorphically encrypted before they leave your machine. The server stores and searches over ciphertexts — computing nearest-neighbor Hamming distances directly on encrypted vectors — without ever seeing the underlying data. When results come back, you decrypt them locally. You get semantic search with the same privacy guarantees as if the data never left your laptop.
Looking for hosted agent memory? x-vec is the low-level encrypted vector database. If you want managed memory for AI agents — send conversation turns, get back searchable facts — see the Memory docs instead.

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Get started

Create a free account at app.xtrace.ai to get your API key and org ID. The free tier is rate-limited but fully functional.

Installation

Install the Python SDK and optional extras

Quickstart

Concept-first walkthrough with full code examples

CLI

Terminal-first workflow — querying in four commands

Embedding models

Ollama, Sentence Transformers, OpenAI, or your own vectors

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Reference

Managed service

XTraceIntegration — security model, chunk operations, context management

Configuration

Crypto backends, key providers, AWS KMS, environment variables

CLI reference

Full usage for every xtrace command

Metadata filtering

Filter syntax, operators, and privacy trade-offs