8 Best smolagents Alternatives in 2026 (Open Source)
smolagents — 🤗 smolagents: a barebones library for agents that think in code.. 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
These 8 open-source tools do the same job. They are ordered by how closely they match smolagents, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| smolagents(original) | 29.6k | +531 | 2026-09-30 |
| CodeAct | 1.7k | +11 | 2024-05-23 |
| TaskWeaver | 6.2k | +6 | 2026-03-23 |
| Code Interpreter API | 3.8k | +-2 | 2024-11-07 |
| LLM Agents | 1.1k | +2 | 2025-06-23 |
| MiniChain | 1.2k | +-0 | 2023-12-07 |
| AgentRun | 380 | +2 | 2024-11-10 |
| Codex | 127.4k | +9,531 | 2026-09-30 |
| Instrukt | 330 | +0 | 2025-05-14 |
1. 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
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
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
4. LLM Agents
Build agents which are controlled by LLMs
What sets it apart: Minimal educational agent implementation in very few lines of code, making LLM agent architecture transparent and easy to understand
Best for: understanding-agent-architecture; learning-tool-augmented-llms; building-simple-agents
5. MiniChain
A tiny library for coding with large language models.
What sets it apart: vs LangChain / LlamaIndex: extremely smaller and simpler — core prompt chaining with typed validation and Gradio visualization, without the complexity of full agent frameworks
Best for: Retrieval-augmented QA and multi-turn chat; Chain-of-thought reasoning pipelines; Developers wanting minimal LLM abstractions without framework bloat
6. 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
7. Codex
Lightweight coding agent that runs in your terminal
What sets it apart: Unlike Claude Code (Anthropic-only), Codex uniquely integrates with existing ChatGPT subscriptions and offers both CLI and cloud-based (Codex Web) agent variants
Best for: OpenAI ecosystem users wanting a terminal-first coding agent with ChatGPT plan integration; Teams already paying for ChatGPT Enterprise who want CLI-based code automation
8. 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