Fast, typo tolerant search engine for building delightful search experiences.
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Typesense is a fast, typo-tolerant search engine for building delightful search experiences.

Typesense Demo



  • Typo tolerant: Handles typographical errors elegantly.
  • Simple and delightful: Simple to set-up and manage.
  • Tunable ranking: Easy to tailor your search results to perfection.
  • Fast: Meticulously designed and optimized for speed.


You can download the binary packages that we publish for Linux (x86-64) and Mac.

You can also run Typesense from our official Docker image:

Quick Start

Here's a quick example showcasing how you can create a collection, index a document and search it on Typesense.

Let's begin by starting the Typesense server via Docker:

docker run -p 8108:8108 -v/tmp/data:/data typesense/typesense:0.9.2 --data-dir /data --api-key=Hu52dwsas2AdxdE

Install the Python client for Typesense (we have clients for other languages too):

pip install typesense

We can now initialize the client and create a companies collection:

import typesense

client = typesense.Client({
  'master_node': {
    'host': 'localhost',
    'port': '8108',
    'protocol': 'http',
    'api_key': 'abcd'
  'timeout_seconds': 2

create_response = client.collections.create({
  "name": "companies",
  "fields": [
    {"name": "company_name", "type": "string" },
    {"name": "num_employees", "type": "int32" },
    {"name": "country", "type": "string", "facet": true }
  "default_sorting_field": "num_employees"

Now, let's add a document to the collection we just created:

document = {
 "id": "124",
 "company_name": "Stark Industries",
 "num_employees": 5215,
 "country": "USA"


Finally, let's search for the document we just indexed:

search_parameters = {
  'q'         : 'stork',
  'query_by'  : 'company_name',
  'filter_by' : 'num_employees:>100',
  'sort_by'   : 'num_employees:desc'


Did you notice the typo in the query text? No big deal. Typesense handles typographic errors out-of-the-box!

Detailed Guide

A detailed guide is available on Typesense website.

Build from source

Building with Docker

The docker build script takes care of all required dependencies, so it's the easiest way to build Typesense:

TYPESENSE_VERSION=nightly ./ --build-deploy-image --create-binary [--clean] [--depclean]

Building on your machine

Typesense requires the following dependencies:

  • C++11 compatible compiler (GCC >= 4.9.0, Apple Clang >= 8.0, Clang >= 3.9.0)
  • Snappy
  • zlib
  • OpenSSL (>=1.0.2)
  • curl
  • ICU
./ --create-binary [--clean] [--depclean]

The first build will take some time since other third-party libraries are pulled and built as part of the build process.


How does this differ from using Elasticsearch?

Elasticsearch is better suited for larger teams who have the bandwidth to administer, scale and fine-tune it and especially when have a need to store billions of documents and scale horizontally.

Typesense is built specifically for decreasing the "time to market" for a delightful search experience. This means focussing on developer productivity and experience with a clean API, clear semantics and smart defaults so that it just works without turning many knobs.

Speed is great, but what about the memory footprint?

A fresh Typesense server will take less than 5 MB of memory. As you start indexing documents, the memory use will increase correspondingly. How much it increases depends on the number and type of fields you index.

We've strived to keep the in-memory data structures lean. To give you a rough idea: when 1 million Hacker News titles are indexed along with their points, Typesense consumes 165 MB of memory. The same size of that data on disk in JSON format is 88 MB. We hope to add better benchmarks on a variety of different data sets soon. In the mean time, if you have any numbers from your own datasets, please send us a PR!


If you've any questions or run into any problems, please create a Github issue and we'll try our best to help.

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