8 Best e2b Alternatives in 2026 (Open Source)

e2b — Python & JS/TS SDK for running AI-generated code/code interpreting in your AI app . Purpose-built cloud infrastructure for AI-generated code execution — secure sandboxes designed specifically for LLM output, not repurposed containers

These 8 open-source tools do the same job. They are ordered by how closely they match e2b, with live GitHub data so you can see which projects are actively maintained.

ToolGitHub starsStars / 30dLast commit
e2b(original)2.4k+262026-09-30
E2B14.1k+4142026-09-30
Code Interpreter API3.8k+-22024-11-07
Open Interpreter68.5k+8982026-09-30
AgentRun380+22024-11-10
smolagents29.6k+5312026-09-30
TaskWeaver6.2k+62026-03-23
Instrukt330+02025-05-14
CodeAct1.7k+112024-05-23

  1. 1. E2B

    Open-source, secure environment with real-world tools for enterprise-grade agents.

    What sets it apart: Purpose-built sandboxed execution for AI-generated code with sub-second startup — vs generic containers which require more setup and have slower cold starts

    Best for: Running untrusted AI-generated code safely; Building code execution features into AI applications

    ⭐ 14.1ke2b vs E2B →Full profile →

  2. 2. Code Interpreter API

    👾 Open source implementation of the ChatGPT Code Interpreter

    What sets it apart: vs raw LangChain code execution: sandboxed Code Interpreter replica with file I/O and conversation memory — the closest open-source implementation of ChatGPT's Code Interpreter feature

    Best for: Developers wanting open-source ChatGPT Code Interpreter functionality; Data analysis automation with file input/output; Building code execution agents with sandboxed safety

    ⭐ 3.8ke2b vs Code Interpreter API →Full profile →

  3. 3. Open Interpreter

    A natural language interface for computers

    What sets it apart: vs ChatGPT Code Interpreter: runs locally with full internet access, no file size limits, any package available, and persistent state

    Best for: Power users wanting natural language control of their computer; Rapid prototyping and data analysis via conversational coding

    ⭐ 68.5ke2b vs Open Interpreter →Full profile →

  4. 4. AgentRun

    The easiest, and fastest way to run AI-generated Python code safely

    What sets it apart: Single-line safe Python code execution from LLMs in Docker containers with automatic dependency management, safety checks, and resource limiting

    Best for: safe-llm-code-execution; sandboxed-python-runtime; giving-code-execution-to-llm-agents

    ⭐ 380e2b vs AgentRun →Full profile →

  5. 5. smolagents

    🤗 smolagents: a barebones library for agents that think in code.

    What sets it apart: vs LangChain: code-first agent design uses 30% fewer tokens by writing Python instead of JSON tool calls; vs CrewAI: lighter ~1000 lines core with HuggingFace Hub integration for sharing agents/tools

    Best for: Building code-writing AI agents with sandboxed execution; HuggingFace ecosystem users wanting agent capabilities; Multi-modal agent applications

    ⭐ 29.6ke2b vs smolagents →Full profile →

  6. 6. TaskWeaver

    The first "code-first" agent framework for seamlessly planning and executing data analytics tasks.

    What sets it apart: Unlike text-only agent frameworks like AutoGen, TaskWeaver preserves full code execution state and in-memory data across turns, enabling seamless multi-step data analytics that manipulate DataFrames and complex structures directly

    Best for: Data scientists needing automated multi-step analytics pipelines with code generation; Teams building AI agents that must handle complex data structures like DataFrames natively

    ⭐ 6.2ke2b vs TaskWeaver →Full profile →

  7. 7. Instrukt

    Integrated AI environment in the terminal. Build, test and instruct agents.

    What sets it apart: vs LangChain CLI / Open Interpreter: terminal-native TUI with Docker sandboxing, modular agent packages, and language-aware code indexing — designed for headless servers and SSH workflows

    Best for: Terminal-native code analysis and documentation Q&A via RAG; Developers wanting secure sandboxed AI agent execution; SSH/headless server environments needing AI tooling

    ⭐ 330e2b vs Instrukt →Full profile →

  8. 8. CodeAct

    Official Repo for ICML 2024 paper "Executable Code Actions Elicit Better LLM Agents" by Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji.

    What sets it apart: vs ReAct/text-based agents: executable Python code as unified action space with containerized execution, achieving 20% higher success rate than JSON/text actions

    Best for: Research on code-based agent action spaces; Building agents that execute Python code as their primary action mechanism

    ⭐ 1.7ke2b vs CodeAct →Full profile →