8 Best CodeAct Alternatives in 2026 (Open Source)

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

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

ToolGitHub starsStars / 30dLast commit
CodeAct(original)1.7k+112024-05-23
smolagents29.6k+5312026-09-30
TaskWeaver6.2k+62026-03-23
Code Interpreter API3.8k+-22024-11-07
GPT-Code3.5k+-62023-07-29
AgentRun380+22024-11-10
E2B14.1k+4142026-09-30
e2b2.4k+262026-09-30
Lumos477+02024-03-19

  1. 1. 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

    ⭐ 29.6kCodeAct vs smolagents →Full profile →

  2. 2. 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

    ⭐ 6.2kCodeAct vs TaskWeaver →Full profile →

  3. 3. 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

    ⭐ 3.8kCodeAct vs Code Interpreter API →Full profile →

  4. 4. GPT-Code

    An open source implementation of OpenAI's ChatGPT Code interpreter

    What sets it apart: vs ChatGPT Code Interpreter / Open Interpreter: self-hosted open-source web UI for AI code generation and execution — own your data and conversations without ChatGPT Plus subscription

    Best for: Self-hosted Code Interpreter alternative; Data analysis and visualization with AI assistance; Document processing and automation scripting

    ⭐ 3.5kCodeAct vs GPT-Code →Full profile →

  5. 5. 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

    ⭐ 380CodeAct vs AgentRun →Full profile →

  6. 6. 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

    ⭐ 14.1kCodeAct vs E2B →Full profile →

  7. 7. e2b

    Python & JS/TS SDK for running AI-generated code/code interpreting in your AI app

    What sets it apart: Purpose-built cloud infrastructure for AI-generated code execution — secure sandboxes designed specifically for LLM output, not repurposed containers

    Best for: AI apps needing safe code execution from LLM outputs; Building AI coding assistants with runnable code; Data analysis agents that generate and run Python

    ⭐ 2.4kCodeAct vs e2b →Full profile →

  8. 8. Lumos

    Code and data for "Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs"

    What sets it apart: vs GPT-4 agents: unified modular framework achieving competitive performance with 7B-13B models — planning + grounding + execution separation enables task-agnostic agent architecture from Allen AI

    Best for: Multi-step reasoning: web navigation, QA, math problem-solving; Research into efficient agent architectures with small models; Building agents competitive with GPT-4 at lower cost

    ⭐ 477CodeAct vs Lumos →Full profile →