> For the complete documentation index, see [llms.txt](https://docs.telys.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.telys.ai/readme.md). # Overview Telys is an on-device, offline-first vector-memory engine. The open `telys` Python SDK drives a closed, signed native runtime. Collections, embeddings, and search run on the machine that imports the SDK — nothing is sent to a server to store or query your data. The SDK defines the API, verifies release signatures and licenses, and loads the runtime lazily through `ctypes`. The runtime — vector store, IVF index, and embedding kernels — is provisioned once with `telys login`, verified offline against embedded public keys, then runs with no network access. Use Telys for durable semantic memory you control: coding assistants that remember a repository, chat apps that recall past conversations, and RAG pipelines that keep their index and embeddings on the user's hardware. Current version: **0.1.0b2**. {% hint style="info" %} New to Telys? Install the SDK, sign in and activate a license, then walk the **Quickstart** to create your first collection and run a search. {% endhint %} ## Start here


Install	Install the SDK with pipx or pip on Python 3.10+.	/pages/P1GCNzSPvejoC1Cftn9l
Sign in	Run `telys login` and provision the signed runtime.	/pages/jbtYRrlfm2suAHJekJo2
First collection	Create a collection, add text, and search it end to end.	/pages/3P7KO0CUnD1NfTy6KAyk
Verify install	Confirm the runtime is ready and its signature is valid.	/pages/h4z9ZPSmvEnW3qsGotXt

## Understand Telys


How it works	The thin SDK, the signed runtime, and how they load.	/pages/4dMaM8DFGDPl772eoH6B
Collections	The core concept — what a collection stores and how.	/pages/ZBd4ROKi3CD4dcp51PD4
On-device & offline	Why your data and embeddings never leave the machine.	/pages/8qFzJkRN9mhP0vZqJwZv

## Build


SDK guides	Create collections, add and upsert, search, filter, tune.	/pages/EIWgp0wPwoYMjxP37Daw
MCP	Give Claude, Cursor, or Codex a memory over five tools.	/pages/ubMgZcamUs49e9HJGixL
Self-hosting	Run a Telys server and connect with RemoteTelys.	/pages/GRqsH5DPQpYEB3ISZf53

## Reference


SDK API index	Every public class and method on the shipped surface.	/pages/noudKEHV7DgrGM5qy5p6

## Install and run your first search Install the SDK and provision the runtime, then store and query memory locally. ```bash pipx install telys # or: pip install telys telys login # device-code sign-in; verifies and installs the runtime ``` Create a collection, add documents, and search — all on-device. The embedder runs in the native kernel that `telys login` installed. ```python from telys import Telys from telys.embedding import AlgentaMultigramEmbedder db = Telys("./memory") emb = AlgentaMultigramEmbedder() col = db.create_collection( "notes", dim=emb.profile.dimension, # kernel-sourced multigram dimension partition_by="scope", embedder=emb, ) col.add_texts( texts=["Telys runs entirely on-device.", "Vectors never leave the machine."], ids=["n1", "n2"], metadata=[{"scope": "demo"}, {"scope": "demo"}], ) col.save() # commit the collection to disk hits = col.search_text("where does my data live?", top_k=2) print(hits["ids"]) # ['n2', 'n1'] ``` {% hint style="success" %} `create_collection` builds and caches the collection in memory; `col.save()` is the commit point that writes it to disk. `db.collections()` lists only saved collections. {% endhint %} Next: [**Install Telys**](/start-here/install.md), then build end to end in the [**Quickstart**](/quickstarts/quickstart-first-collection.md). --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.telys.ai/readme.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.