SandBase Operational

SandBase is an agent infrastructure platform for developers. With one API key, it provides unified access to more than 2,000 models and data APIs, an E2B-compatible sandbox runtime, and compatibility with the OpenAI Agents API. Developers can let agents access real-world data, run code, and process files; package familiar workflows into Personal Agents; embed them in their own products or use them through CLI and MCP tools; and pay based on usage.

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Model access: Compatible with the OpenAI Agents API standard, allowing developers to use the familiar OpenAI SDK to create agents, establish sessions, and run tasks.
Data APIs: Covers real-world data such as X, YouTube, web search, and company information. Some APIs provide 100 free calls per day.
Sandbox execution: Provides an E2B-compatible runtime where agents can run code, process files, and return results. Existing E2B code can be migrated at low cost.
Tool integration: Supports SKILL.md, CLI, and local MCP bridging, enabling direct integration with existing AI tools such as Claude Code, Codex, and Cursor.
Skill templates: Includes reusable Skills for Twitter intelligence, YouTube transcription, SEO analysis, company research, and more to help agents complete practical work.

Register an account: Visit the SandBase website https://www.sandbase.ai/ to register, then open the Console to obtain a SandBase API Key.
Quick integration: In AI tools such as Claude Code, Codex, and Cursor, paste the instruction set up https://www.sandbase.ai/SKILL.md so the AI assistant can automatically configure the environment.
Select a model: Use the OpenAI-compatible endpoint api.sandbase.ai/v1 and the OpenAI SDK to specify the required model, create an agent, and configure task instructions.
Connect data: Use the same API key to call data APIs for X, YouTube, web search, and more. Review capability details and pricing before making calls.
Run tasks: After creating an agent session, submit tasks. The agent will run code in the sandbox, organize information, and generate result files.
Review results: Manually verify that the original links are valid and that the output meets the filtering criteria. Integrate the workflow into your own product after confirming its reliability.

Unified access: One API key connects models, data APIs, and the sandbox, eliminating repetitive engineering for multi-platform authentication and account management.
Protocol compatibility: Compatible with the OpenAI Agents API and E2B SDK, allowing developers to reuse existing coding patterns with low migration costs.
Ecosystem integration: Directly embeds into mainstream AI tools such as Claude Code, Codex, and Cursor through SKILL.md, CLI, and MCP bridging, using agents as an acquisition channel.
Real-world data: Connects to real-world data APIs such as X, YouTube, and web search, enabling agents to complete tasks with practical outputs.
Broad coverage: According to official figures, connects to more than 2,000 models and APIs across more than 100 platforms, meeting diverse selection needs.

Project website: https://www.sandbase.ai/
GitHub repository: https://github.com/sandbaseai/

Expert-level training data production: Organizes industry experts for frontier model companies to produce high-barrier SFT data in batches, covering fields with “expert-level cognitive density” such as finance, law, medicine, and STEM.
RL environments and “anti-pattern” data: Builds reinforcement learning environments and “anti-pattern” training datasets for models, helping them improve reasoning and error correction through multiple rounds of trial and error. It has also open-sourced datasets and training outputs such as Dynasor and ProRL.
Benchmark evaluation and “building the exam room”: Provides expert-level benchmark evaluation services that simulate real examination environments to test models’ actual performance on highly challenging professional tasks. Evaluation results are provided as a reference for model laboratories.
Advancing model capabilities in vertical industries: Targets specialized professional scenarios beyond programming, producing training tasks that reproduce real workflows so models can perform reliably on long-chain professional tasks.