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e2b cookbook

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Basic Information

E2B Cookbook is a collection of example code and guides for developers building applications with the E2B SDK. It provides Hello World tutorials in TypeScript and Python, end-to-end examples that demonstrate how to use E2B to run code interpreters and analyze datasets, and references to open-source apps built on E2B. The repository shows how to connect many LLM providers and runtimes, illustrating provider-specific features like code interpretation and sandboxed execution. It documents integrations with popular AI frameworks and SDKs and includes practical recipes for running Docker and Playwright inside E2B. The primary audience is developers seeking hands-on examples and templates to integrate large language models, streaming, and code-execution capabilities into web and data analysis projects.

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1084
App URL
https://github.com/e2b-dev/e2b-cookbook
Github Repository
https://github.com/e2b-dev/e2b-cookbook/blob/main/README.md

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Features
The cookbook contains categorized, runnable examples in both Python and TypeScript, including Hello World guides and many provider-specific code-interpreter examples for OpenAI, GPT-4o, Anthropic, Mistral, Groq, Fireworks, Llama, Together AI, and WatsonX. It demonstrates sandboxed execution patterns and secure code interpreter setups. Integrations include LangChain, LangGraph, Autogen, the Vercel AI SDK, and AgentKit. Example use cases include dataset upload and analysis, scraping and analyzing Airbnb data, website topic visualization, a Next.js app with streaming and code interpreter, and instructions for running Docker and Playwright within E2B. The repo also links to open-source E2B apps to illustrate real-world application patterns.
Use Cases
This repository accelerates prototyping by providing ready-made, provider-specific recipes and runnable examples that show how to wire the E2B SDK into Python and TypeScript projects. It reduces integration friction by illustrating code-interpreter setups, sandboxed execution, and adapters for frameworks like LangChain and LangGraph. The examples cover practical tasks such as data analysis, visualization, web scraping, and streaming LLM responses so developers can copy and adapt proven patterns. It doubles as hands-on learning material paired with real-world app references to make it easier to move from experimentation to building production-style AI features.

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