e2b
by Community
Open-source, secure environment with real-world tools for enterprise-grade agents.
OSS
e2b
Added 1 June 2026
Overview
e2b is an open-source sandbox environment that runs code in isolated containers with access to real-world tools and system capabilities. It enables builders to execute untrusted code safely for AI agents and applications that need to perform computations, file operations, or system interactions without compromising host security.
Best for
Best for
Teams building AI agents that need to execute dynamic code safely in production environments
Use cases
- Running code generated by LLMs in isolated sandboxes
- Building AI agents that execute scripts and system commands safely
- Testing and validating user-submitted code without risk
Notes
e2b is an open-source sandbox environment that runs code in isolated containers with access to real-world tools and system capabilities. It enables builders to execute untrusted code safely for AI agents and applications that need to perform computations, file operations, or system interactions without compromising host security.
12,433 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.
Use cases
- Running code generated by LLMs in isolated sandboxes
- Building AI agents that execute scripts and system commands safely
- Testing and validating user-submitted code without risk
Pros
- Open-source with active community support (12k+ stars)
- Provides real system tools and capabilities within sandboxed environment
- Enterprise-grade security model for production agent deployments
Cons
- Requires infrastructure setup and container orchestration knowledge
- Performance overhead from isolation layer compared to direct execution
- Limited to Python ecosystem based on current language support
Indexed from awesome-langchain and enriched against its public facts.
Pros
- Open-source with active community support (12k+ stars)
- Provides real system tools and capabilities within sandboxed environment
- Enterprise-grade security model for production agent deployments
Cons
- Requires infrastructure setup and container orchestration knowledge
- Performance overhead from isolation layer compared to direct execution
- Limited to Python ecosystem based on current language support
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