jupyter-ai-agents
🪐 ✨ Jupyter AI Agents are agents equipped with tools like 'execute', 'insert_cell', and more, to transform your Jupyter Notebooks into an intelligent, interactive workspace!
Stars: 58
README:
The Jupyter AI Agents are equipped with tools like 'execute', 'insert_cell', and more, to transform your Jupyter Notebooks into an intelligent, interactive workspace!
Jupyter AI Agents <---> JupyterLab
|
| RTC (Real Time Collaboration)
|
Jupyter Clients
Jupyter AI Agents empowers AI models to interact with and modify Jupyter Notebooks. The agent is equipped with tools such as adding code cells, inserting markdown cells, executing code, enabling it to modify the notebook comprehensively based on user instructions or by reacting to the Jupyter notebook events.
This agent is innovative as it is designed to operate on the entire notebook, not just at the cell level, enabling more comprehensive and seamless modifications. The agent can also run separetely from the Jupyter server as the communication is achieved through RTC () via the Jupyter NbModel Client and the Jupyter Kernel Client.
The LangChain Agent Framework is used to manage the interactions between the AI model and the tools.
This library is documented on https://jupyter-ai-agents.datalayer.tech.
To install Jupyter AI Agents, run the following command.
pip install jupyter_ai_agentsOr clone this repository and install it from source.
git clone https://github.com/datalayer/jupyter-ai-agents
cd jupyter-ai-agents
pip install -e .The Jupyter AI Agents can directly interact with JupyterLab. The modifications made by the Jupyter AI Agents can be seen in real-time thanks to Jupyter Real Time Collaboration. Make sure you have JupyterLab installed with the Collaboration extension.
pip install jupyterlab jupyter-collaboration ipykernelWe ask you to take additional actions to overcome limitations and bugs of the pycrdt library. Ensure you create a new shell after running the following commands.
pip uninstall -y pycrdt datalayer_pycrdt
pip install datalayer_pycrdtWe put here a quick example for a Out-Kernel Stateless Agent via CLI helping your JupyterLab session.
Start JupyterLab, setting a port and a token to be reused by the agent, and create a notebook test.ipynb.
jupyter lab --port 8888 --IdentityProvider.token MY_TOKENYou can also start JupyterLab with the following command.
make jupyterlabJupyter AI Agents supports multiple AI model providers (more information can be found on this documentation page).
The following takes you through an example with the Azure OpenAI provider. Read the Azure Documentation to get the needed credentials and make sure you define them in the following .env file.
cat << EOF >>.env
OPENAI_API_VERSION="..."
AZURE_OPENAI_ENDPOINT="..."
AZURE_OPENAI_API_KEY="..."
EOFPrompt Agent
To use the Jupyter AI Agents, an easy way is to launch a CLI (update the Azure deployment name based on your setup).
# Prompt agent example.
jupyter-ai-agents prompt \
--url http://localhost:8888 \
--token MY_TOKEN \
--model-provider azure-openai \
--model-name gpt-4o-mini \
--path test.ipynb \
--input "Create a matplotlib example"You can also start prompt with the following command.
make promptExplain Error Agent
# Explain Error agent example.
jupyter-ai-agents explain-error \
--url http://localhost:8888 \
--token MY_TOKEN \
--model-provider azure-openai \
--model-name gpt-4o-mini \
--path test.ipynbYou can also start request the error explanation with the following command.
make explain-error
To uninstall the agent, execute.
pip uninstall jupyter_ai_agentsYou can start a Jupyter AI Agents server to be used in combination with the Datalayer service.
make start# Clone the repo to your local environment
# Change directory to the jupyter_ai_agents directory
# Install package in development mode - will automatically enable
# The server extension.
pip install -e ".[test,lint,typing]"Install dependencies:
pip install -e ".[test]"To run the python tests, use:
pytestpip uninstall jupyter_ai_agentsSee RELEASE.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for jupyter-ai-agents
Similar Open Source Tools
desktop
ComfyUI Desktop is a packaged desktop application that allows users to easily use ComfyUI with bundled features like ComfyUI source code, ComfyUI-Manager, and uv. It automatically installs necessary Python dependencies and updates with stable releases. The app comes with Electron, Chromium binaries, and node modules. Users can store ComfyUI files in a specified location and manage model paths. The tool requires Python 3.12+ and Visual Studio with Desktop C++ workload for Windows. It uses nvm to manage node versions and yarn as the package manager. Users can install ComfyUI and dependencies using comfy-cli, download uv, and build/launch the code. Troubleshooting steps include rebuilding modules and installing missing libraries. The tool supports debugging in VSCode and provides utility scripts for cleanup. Crash reports can be sent to help debug issues, but no personal data is included.
dir-assistant
Dir-assistant is a tool that allows users to interact with their current directory's files using local or API Language Models (LLMs). It supports various platforms and provides API support for major LLM APIs. Users can configure and customize their local LLMs and API LLMs using the tool. Dir-assistant also supports model downloads and configurations for efficient usage. It is designed to enhance file interaction and retrieval using advanced language models.
ChatOpsLLM
ChatOpsLLM is a project designed to empower chatbots with effortless DevOps capabilities. It provides an intuitive interface and streamlined workflows for managing and scaling language models. The project incorporates robust MLOps practices, including CI/CD pipelines with Jenkins and Ansible, monitoring with Prometheus and Grafana, and centralized logging with the ELK stack. Developers can find detailed documentation and instructions on the project's website.
tiledesk-dashboard
Tiledesk is an open-source live chat platform with integrated chatbots written in Node.js and Express. It is designed to be a multi-channel platform for web, Android, and iOS, and it can be used to increase sales or provide post-sales customer service. Tiledesk's chatbot technology allows for automation of conversations, and it also provides APIs and webhooks for connecting external applications. Additionally, it offers a marketplace for apps and features such as CRM, ticketing, and data export.
Oxen
Oxen is a data version control library, written in Rust. It's designed to be fast, reliable, and easy to use. Oxen can be used in a variety of ways, from a simple command line tool to a remote server to sync to, to integrations into other ecosystems such as python.
gitingest
GitIngest is a tool that allows users to turn any Git repository into a prompt-friendly text ingest for LLMs. It provides easy code context by generating a text digest from a git repository URL or directory. The tool offers smart formatting for optimized output format for LLM prompts and provides statistics about file and directory structure, size of the extract, and token count. GitIngest can be used as a CLI tool on Linux and as a Python package for code integration. The tool is built using Tailwind CSS for frontend, FastAPI for backend framework, tiktoken for token estimation, and apianalytics.dev for simple analytics. Users can self-host GitIngest by building the Docker image and running the container. Contributions to the project are welcome, and the tool aims to be beginner-friendly for first-time contributors with a simple Python and HTML codebase.
code2prompt
code2prompt is a command-line tool that converts your codebase into a single LLM prompt with a source tree, prompt templating, and token counting. It automates generating LLM prompts from codebases of any size, customizing prompt generation with Handlebars templates, respecting .gitignore, filtering and excluding files using glob patterns, displaying token count, including Git diff output, copying prompt to clipboard, saving prompt to an output file, excluding files and folders, adding line numbers to source code blocks, and more. It helps streamline the process of creating LLM prompts for code analysis, generation, and other tasks.
cursor-tools
cursor-tools is a CLI tool designed to enhance AI agents with advanced skills, such as web search, repository context, documentation generation, GitHub integration, Xcode tools, and browser automation. It provides features like Perplexity for web search, Gemini 2.0 for codebase context, and Stagehand for browser operations. The tool requires API keys for Perplexity AI and Google Gemini, and supports global installation for system-wide access. It offers various commands for different tasks and integrates with Cursor Composer for AI agent usage.
ai-starter-kit
SambaNova AI Starter Kits is a collection of open-source examples and guides designed to facilitate the deployment of AI-driven use cases for developers and enterprises. The kits cover various categories such as Data Ingestion & Preparation, Model Development & Optimization, Intelligent Information Retrieval, and Advanced AI Capabilities. Users can obtain a free API key using SambaNova Cloud or deploy models using SambaStudio. Most examples are written in Python but can be applied to any programming language. The kits provide resources for tasks like text extraction, fine-tuning embeddings, prompt engineering, question-answering, image search, post-call analysis, and more.
sandbox
Sandbox is an open-source cloud-based code editing environment with custom AI code autocompletion and real-time collaboration. It consists of a frontend built with Next.js, TailwindCSS, Shadcn UI, Clerk, Monaco, and Liveblocks, and a backend with Express, Socket.io, Cloudflare Workers, D1 database, R2 storage, Workers AI, and Drizzle ORM. The backend includes microservices for database, storage, and AI functionalities. Users can run the project locally by setting up environment variables and deploying the containers. Contributions are welcome following the commit convention and structure provided in the repository.
shellChatGPT
ShellChatGPT is a shell wrapper for OpenAI's ChatGPT, DALL-E, Whisper, and TTS, featuring integration with LocalAI, Ollama, Gemini, Mistral, Groq, and GitHub Models. It provides text and chat completions, vision, reasoning, and audio models, voice-in and voice-out chatting mode, text editor interface, markdown rendering support, session management, instruction prompt manager, integration with various service providers, command line completion, file picker dialogs, color scheme personalization, stdin and text file input support, and compatibility with Linux, FreeBSD, MacOS, and Termux for a responsive experience.
pacha
Pacha is an AI tool designed for retrieving context for natural language queries using a SQL interface and Python programming environment. It is optimized for working with Hasura DDN for multi-source querying. Pacha is used in conjunction with language models to produce informed responses in AI applications, agents, and chatbots.
arcade-ai
Arcade AI is a developer-focused tooling and API platform designed to enhance the capabilities of LLM applications and agents. It simplifies the process of connecting agentic applications with user data and services, allowing developers to concentrate on building their applications. The platform offers prebuilt toolkits for interacting with various services, supports multiple authentication providers, and provides access to different language models. Users can also create custom toolkits and evaluate their tools using Arcade AI. Contributions are welcome, and self-hosting is possible with the provided documentation.
gemini-pro-bot
This Python Telegram bot utilizes Google's `gemini-pro` LLM API to generate creative text formats based on user input. It's designed to be an engaging and interactive way to explore the capabilities of large language models. Key features include generating various text formats like poems, code, scripts, and musical pieces. The bot supports real-time streaming of the generation process, allowing users to witness the text unfold. Additionally, it can respond to messages with Bard's creative output and handle image-based inputs for multimodal responses. User authentication is optional, and the bot can be easily integrated with Docker or installed via pipenv.
letta
Letta is an open source framework for building stateful LLM applications. It allows users to build stateful agents with advanced reasoning capabilities and transparent long-term memory. The framework is white box and model-agnostic, enabling users to connect to various LLM API backends. Letta provides a graphical interface, the Letta ADE, for creating, deploying, interacting, and observing with agents. Users can access Letta via REST API, Python, Typescript SDKs, and the ADE. Letta supports persistence by storing agent data in a database, with PostgreSQL recommended for data migrations. Users can install Letta using Docker or pip, with Docker defaulting to PostgreSQL and pip defaulting to SQLite. Letta also offers a CLI tool for interacting with agents. The project is open source and welcomes contributions from the community.