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automation

txtai: Automate Search

Get AI-powered semantic search with txtai, a framework for startup founders, with 12k+ GitHub stars.
intermediate⏱ 30 minutes💵 Free tier + paid plans
12,724 stars846 forksPythonQuality 8/10Updated 7/14/2026100% free · open source
What it is

Use a pre-made AI framework to power your website or app with search capabilities.

What you can make with it

Automations like: a search bar on your e-commerce site that finds products by description or image.

How it helps

This saves you time and resources compared to custom-building a search function from scratch. The framework already includes tried-and-true AI search components.

Real use case example

"A founder of a small e-commerce site wants to let customers easily find products with a visually appealing description. They use this framework to integrate an AI-powered search bar, which saves them around 2 days of development time. The search bar goes live in a week."

If you're new

Starters with basic Python knowledge can pick up the basics of this framework quickly.

If you're senior

Senior developers will appreciate its flexibility and extensibility for more complex search tasks.

Common confusion cleared up

The main confusion is that this framework is 'just for search', but it has many more capabilities and integrations, such as language model workflows and orchestration.

Pairs with
Stripe webhookNotion databaseNext.js
Why we list it on WorkflowStacks: This is included because it's a well-maintained, open-source framework designed specifically for AI search, and it has a large community behind it.
What it does

Creates intelligent search and AI workflows by turning text data into smart, semantically-aware indexes that can be queried conversationally

When to use it
  • •Building a custom knowledge base search for your startup's documentation
  • •Creating an AI assistant that can understand context across your company's data
  • •Automating information retrieval and summarization from large text collections
Quick start
  1. 1Install via pip: pip install txtai
  2. 2Create a simple semantic index of your documents
  3. 3Configure search and retrieval parameters
  4. 4Query your index using natural language questions
Ready-to-paste prompt
from txtai import Embeddings

embeddings = Embeddings({'path': 'sentence-transformers/all-MiniLM-L6-v2'})
embeddings.index([(0, 'first document'), (1, 'second document')])
results = embeddings.search('find documents about X', limit=3)
Saves to your device

Topics

agents
ai
ai-agents
embeddings
information-retrieval
language-model
large-language-models
llm
nlp
python
rag
retrieval-augmented-generation
search
search-engine
semantic-search
sentence-embeddings
transformers
txtai
vector-database
vector-search
What's inside — free to inspect
No purchase needed

Read the entire source before you build — unlike paid marketplaces that hide it behind a buy button.

14
top-level files
6
folders
54.3M
repo size
Apache-2.0
license
Key files
.pre-commit-config.yaml
README.md
File tree
.github/
docker/
docs/
examples/
src/
test/
.coveragerc
.gitignore
.pre-commit-config.yaml
.pylintrc
apps.jpg
CITATION.cff
demo.gif
LICENSE
logo.png
Makefile
mkdocs.yml
pyproject.toml
README.md
setup.py
Quick Actions
Details
Creator
neuml
Language
Python
Category
automation
Published
8/9/2020

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