
docetl
A system for agentic LLM-powered data processing and ETL
Stars: 1538

DocETL is a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks. It offers a low-code, declarative YAML interface to define LLM-powered operations on complex data. Ideal for maximizing correctness and output quality for semantic processing on a collection of data, representing complex tasks via map-reduce, maximizing LLM accuracy, handling long documents, and automating task retries based on validation criteria.
README:
DocETL is a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks. It offers:
- An interactive UI playground for iterative prompt engineering and pipeline development
- A Python package for running production pipelines from the command line or Python code
There are two main ways to use DocETL:
DocWrangler helps you iteratively develop your pipeline:
- Experiment with different prompts and see results in real-time
- Build your pipeline step by step
- Export your finalized pipeline configuration for production use
DocWrangler is hosted at docetl.org/playground. But to run the playground locally, you can either:
- Use Docker (recommended for quick start):
make docker
- Set up the development environment manually
See the Playground Setup Guide for detailed instructions.
If you want to use DocETL as a Python package:
- Python 3.10 or later
- OpenAI API key
pip install docetl
Create a .env
file in your project directory:
OPENAI_API_KEY=your_api_key_here # Required for LLM operations (or the key for the LLM of your choice)
To see examples of how to use DocETL, check out the tutorial.
To run DocWrangler locally, you have two options:
The easiest way to get the DocWrangler playground running:
- Create the required environment files:
Create .env
in the root directory:
OPENAI_API_KEY=your_api_key_here
BACKEND_ALLOW_ORIGINS=http://localhost:3000,http://127.0.0.1:3000
BACKEND_HOST=0.0.0.0
BACKEND_PORT=8000
BACKEND_RELOAD=True
FRONTEND_HOST=0.0.0.0
FRONTEND_PORT=3000
Create .env.local
in the website
directory:
OPENAI_API_KEY=sk-xxx
OPENAI_API_BASE=https://api.openai.com/v1
MODEL_NAME=gpt-4o-mini
NEXT_PUBLIC_BACKEND_HOST=localhost
NEXT_PUBLIC_BACKEND_PORT=8000
- Run Docker:
make docker
This will:
- Create a Docker volume for persistent data
- Build the DocETL image
- Run the container with the UI accessible at http://localhost:3000
To clean up Docker resources (note that this will delete the Docker volume):
make docker-clean
For development or if you prefer not to use Docker:
- Clone the repository:
git clone https://github.com/ucbepic/docetl.git
cd docetl
- Set up environment variables in
.env
in the root/top-level directory:
OPENAI_API_KEY=your_api_key_here
BACKEND_ALLOW_ORIGINS=http://localhost:3000,http://127.0.0.1:3000
BACKEND_HOST=localhost
BACKEND_PORT=8000
BACKEND_RELOAD=True
FRONTEND_HOST=0.0.0.0
FRONTEND_PORT=3000
And create an .env.local file in the website
directory with the following:
OPENAI_API_KEY=sk-xxx
OPENAI_API_BASE=https://api.openai.com/v1
MODEL_NAME=gpt-4o-mini
NEXT_PUBLIC_BACKEND_HOST=localhost
NEXT_PUBLIC_BACKEND_PORT=8000
- Install dependencies:
make install # Install Python package
make install-ui # Install UI dependencies
Note that the OpenAI API key, base, and model name are for the UI assistant only; not the DocETL pipeline execution engine.
- Start the development server:
make run-ui-dev
- Visit http://localhost:3000/playground to access the interactive UI.
If you're planning to contribute or modify DocETL, you can verify your setup by running the test suite:
make tests-basic # Runs basic test suite (costs < $0.01 with OpenAI)
For detailed documentation and tutorials, visit our documentation.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for docetl
Similar Open Source Tools

docetl
DocETL is a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks. It offers a low-code, declarative YAML interface to define LLM-powered operations on complex data. Ideal for maximizing correctness and output quality for semantic processing on a collection of data, representing complex tasks via map-reduce, maximizing LLM accuracy, handling long documents, and automating task retries based on validation criteria.

private-ml-sdk
Private ML SDK is a secure solution for running Large Language Models (LLMs) in Trusted Execution Environments (TEEs) using NVIDIA GPU TEE and Intel TDX technologies. It provides a tamper-proof data processing environment with secure execution, open-source builds, and nearly native speed performance. The system includes components like Secure Compute Environment, Remote Attestation, Secure Communication, and Key Management Service (KMS). Users can build TDX guest images, run Local KMS, and TDX guest images on TDX host machines with Nvidia GPUs. The SDK offers verifiable execution results and high performance for LLM workloads.

paxml
Pax is a framework to configure and run machine learning experiments on top of Jax.

pebblo
Pebblo enables developers to safely load data and promote their Gen AI app to deployment without worrying about the organizationโs compliance and security requirements. The project identifies semantic topics and entities found in the loaded data and summarizes them on the UI or a PDF report.

shinkai-apps
Shinkai apps unlock the full capabilities/automation of first-class LLM (AI) support in the web browser. It enables creating multiple agents, each connected to either local or 3rd-party LLMs (ex. OpenAI GPT), which have permissioned (meaning secure) access to act in every webpage you visit. There is a companion repo called Shinkai Node, that allows you to set up the node anywhere as the central unit of the Shinkai Network, handling tasks such as agent management, job processing, and secure communications.

openai-kotlin
OpenAI Kotlin API client is a Kotlin client for OpenAI's API with multiplatform and coroutines capabilities. It allows users to interact with OpenAI's API using Kotlin programming language. The client supports various features such as models, chat, images, embeddings, files, fine-tuning, moderations, audio, assistants, threads, messages, and runs. It also provides guides on getting started, chat & function call, file source guide, and assistants. Sample apps are available for reference, and troubleshooting guides are provided for common issues. The project is open-source and licensed under the MIT license, allowing contributions from the community.

HuggingFaceModelDownloader
The HuggingFace Model Downloader is a utility tool for downloading models and datasets from the HuggingFace website. It offers multithreaded downloading for LFS files and ensures the integrity of downloaded models with SHA256 checksum verification. The tool provides features such as nested file downloading, filter downloads for specific LFS model files, support for HuggingFace Access Token, and configuration file support. It can be used as a library or a single binary for easy model downloading and inference in projects.

NekoImageGallery
NekoImageGallery is an online AI image search engine that utilizes the Clip model and Qdrant vector database. It supports keyword search and similar image search. The tool generates 768-dimensional vectors for each image using the Clip model, supports OCR text search using PaddleOCR, and efficiently searches vectors using the Qdrant vector database. Users can deploy the tool locally or via Docker, with options for metadata storage using Qdrant database or local file storage. The tool provides API documentation through FastAPI's built-in Swagger UI and can be used for tasks like image search, text extraction, and vector search.

hound
Hound is a security audit automation pipeline for AI-assisted code review that mirrors how expert auditors think, learn, and collaborate. It features graph-driven analysis, sessionized audits, provider-agnostic models, belief system and hypotheses, precise code grounding, and adaptive planning. The system employs a senior/junior auditor pattern where the Scout actively navigates the codebase and annotates knowledge graphs while the Strategist handles high-level planning and vulnerability analysis. Hound is optimized for small-to-medium sized projects like smart contract applications and is language-agnostic.

DeGPT
DeGPT is a tool designed to optimize decompiler output using Large Language Models (LLM). It requires manual installation of specific packages and setting up API key for OpenAI. The tool provides functionality to perform optimization on decompiler output by running specific scripts.

react-native-fast-tflite
A high-performance TensorFlow Lite library for React Native that utilizes JSI for power, zero-copy ArrayBuffers for efficiency, and low-level C/C++ TensorFlow Lite core API for direct memory access. It supports swapping out TensorFlow Models at runtime and GPU-accelerated delegates like CoreML/Metal/OpenGL. Easy VisionCamera integration allows for seamless usage. Users can load TensorFlow Lite models, interpret input and output data, and utilize GPU Delegates for faster computation. The library is suitable for real-time object detection, image classification, and other machine learning tasks in React Native applications.

raglite
RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite. It offers configurable options for choosing LLM providers, database types, and rerankers. The toolkit is fast and permissive, utilizing lightweight dependencies and hardware acceleration. RAGLite provides features like PDF to Markdown conversion, multi-vector chunk embedding, optimal semantic chunking, hybrid search capabilities, adaptive retrieval, and improved output quality. It is extensible with a built-in Model Context Protocol server, customizable ChatGPT-like frontend, document conversion to Markdown, and evaluation tools. Users can configure RAGLite for various tasks like configuring, inserting documents, running RAG pipelines, computing query adapters, evaluating performance, running MCP servers, and serving frontends.

RA.Aid
RA.Aid is an AI software development agent powered by `aider` and advanced reasoning models like `o1`. It combines `aider`'s code editing capabilities with LangChain's agent-based task execution framework to provide an intelligent assistant for research, planning, and implementation of multi-step development tasks. It handles complex programming tasks by breaking them down into manageable steps, running shell commands automatically, and leveraging expert reasoning models like OpenAI's o1. RA.Aid is designed for everyday software development, offering features such as multi-step task planning, automated command execution, and the ability to handle complex programming tasks beyond single-shot code edits.

ChatGPT
The ChatGPT API Free Reverse Proxy provides free self-hosted API access to ChatGPT (`gpt-3.5-turbo`) with OpenAI's familiar structure, eliminating the need for code changes. It offers streaming response, API endpoint compatibility, and complimentary access without an API key. Installation options include Docker, PC/Server, and Termux on Android devices. The API can be accessed through a self-hosted local server or a pre-hosted API with an API key obtained from the Discord server. Usage examples are provided for Python and Node.js, and the project is licensed under AGPL-3.0.

aigne-doc-smith
AIGNE DocSmith is a powerful AI-driven documentation generation tool that automates the creation of detailed, structured, and multi-language documentation directly from source code. It intelligently analyzes codebase to generate a comprehensive document structure, populates content with high-quality AI-powered generation, supports seamless translation into 12+ languages, integrates with AIGNE Hub for large language models, offers Discuss Kit publishing, automatically updates documentation with source code changes, and allows for individual document optimization.

steel-browser
Steel is an open-source browser API designed for AI agents and applications, simplifying the process of building live web agents and browser automation tools. It serves as a core building block for a production-ready, containerized browser sandbox with features like stealth capabilities, text-to-markdown session management, UI for session viewing/debugging, and full browser control through popular automation frameworks. Steel allows users to control, run, and manage a production-ready browser environment via a REST API, offering features such as full browser control, session management, proxy support, extension support, debugging tools, anti-detection mechanisms, resource management, and various browser tools. It aims to streamline complex browsing tasks programmatically, enabling users to focus on their AI applications while Steel handles the underlying complexity.
For similar tasks

Azure-Analytics-and-AI-Engagement
The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customerโs subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.

sorrentum
Sorrentum is an open-source project that aims to combine open-source development, startups, and brilliant students to build machine learning, AI, and Web3 / DeFi protocols geared towards finance and economics. The project provides opportunities for internships, research assistantships, and development grants, as well as the chance to work on cutting-edge problems, learn about startups, write academic papers, and get internships and full-time positions at companies working on Sorrentum applications.

tidb
TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

zep-python
Zep is an open-source platform for building and deploying large language model (LLM) applications. It provides a suite of tools and services that make it easy to integrate LLMs into your applications, including chat history memory, embedding, vector search, and data enrichment. Zep is designed to be scalable, reliable, and easy to use, making it a great choice for developers who want to build LLM-powered applications quickly and easily.

telemetry-airflow
This repository codifies the Airflow cluster that is deployed at workflow.telemetry.mozilla.org (behind SSO) and commonly referred to as "WTMO" or simply "Airflow". Some links relevant to users and developers of WTMO: * The `dags` directory in this repository contains some custom DAG definitions * Many of the DAGs registered with WTMO don't live in this repository, but are instead generated from ETL task definitions in bigquery-etl * The Data SRE team maintains a WTMO Developer Guide (behind SSO)

mojo
Mojo is a new programming language that bridges the gap between research and production by combining Python syntax and ecosystem with systems programming and metaprogramming features. Mojo is still young, but it is designed to become a superset of Python over time.

pandas-ai
PandasAI is a Python library that makes it easy to ask questions to your data in natural language. It helps you to explore, clean, and analyze your data using generative AI.

databend
Databend is an open-source cloud data warehouse that serves as a cost-effective alternative to Snowflake. With its focus on fast query execution and data ingestion, it's designed for complex analysis of the world's largest datasets.
For similar jobs

db2rest
DB2Rest is a modern low-code REST DATA API platform that simplifies the development of intelligent applications. It seamlessly integrates existing and new databases with language models (LMs/LLMs) and vector stores, enabling the rapid delivery of context-aware, reasoning applications without vendor lock-in.

mage-ai
Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.

airbyte
Airbyte is an open-source data integration platform that makes it easy to move data from any source to any destination. With Airbyte, you can build and manage data pipelines without writing any code. Airbyte provides a library of pre-built connectors that make it easy to connect to popular data sources and destinations. You can also create your own connectors using Airbyte's no-code Connector Builder or low-code CDK. Airbyte is used by data engineers and analysts at companies of all sizes to build and manage their data pipelines.

labelbox-python
Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.

telemetry-airflow
This repository codifies the Airflow cluster that is deployed at workflow.telemetry.mozilla.org (behind SSO) and commonly referred to as "WTMO" or simply "Airflow". Some links relevant to users and developers of WTMO: * The `dags` directory in this repository contains some custom DAG definitions * Many of the DAGs registered with WTMO don't live in this repository, but are instead generated from ETL task definitions in bigquery-etl * The Data SRE team maintains a WTMO Developer Guide (behind SSO)

airflow
Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

airbyte-platform
Airbyte is an open-source data integration platform that makes it easy to move data from any source to any destination. With Airbyte, you can build and manage data pipelines without writing any code. Airbyte provides a library of pre-built connectors that make it easy to connect to popular data sources and destinations. You can also create your own connectors using Airbyte's low-code Connector Development Kit (CDK). Airbyte is used by data engineers and analysts at companies of all sizes to move data for a variety of purposes, including data warehousing, data analysis, and machine learning.

chronon
Chronon is a platform that simplifies and improves ML workflows by providing a central place to define features, ensuring point-in-time correctness for backfills, simplifying orchestration for batch and streaming pipelines, offering easy endpoints for feature fetching, and guaranteeing and measuring consistency. It offers benefits over other approaches by enabling the use of a broad set of data for training, handling large aggregations and other computationally intensive transformations, and abstracting away the infrastructure complexity of data plumbing.