What IronClaw does#
IronClaw is an open-source secure runtime designed to run AI agents in encrypted enclaves on the NEAR AI Cloud. It provides a robust alternative to traditional AI frameworks by ensuring that sensitive data never leaves the secure environment. Key features include:
Key Features
- One-Click Deployment: Easily launch your AI agent with a single click, ensuring a hassle-free setup.
- Encrypted Vault: Store API keys, tokens, and passwords securely, with the AI only accessing them when necessary.
- Defense-in-Depth Security: Multiple layers of security protect your data from unauthorized access.
- Built on Rust: Leverage the memory safety and performance benefits of Rust, minimizing vulnerabilities.
- Sandboxed Tools: Each tool runs in its own isolated environment, preventing cross-contamination of data.
Use Cases
- Personal AI Assistants: Deploy secure AI agents that can assist with tasks without compromising user data.
- Automated Workflows: Integrate IronClaw into business processes to automate tasks while ensuring data security.
- Development and Testing: Use IronClaw for developing and testing AI applications in a secure environment, reducing the risk of data leaks.
Who IronClaw is for#
AI teams testing tool-using agents
IronClaw fits teams that need repeatable checks before agents call internal tools, write data, or trigger workflows.
Skip if
Skip it if your LLM use case is read-only summarization with no tool access.
Security reviewers building agent test cases
Reviewers can encode expected failures and unsafe-action scenarios in a project-specific evaluation suite.
Skip if
Use a broader AI governance platform if you need policy management, audit workflows, and enterprise reporting.
The problem it solves#
Agentic applications can take actions, call tools, and move data across systems, which makes casual prompt testing too weak. A demo that works once does not prove the agent behaves safely under adversarial or unexpected instructions.
AI teams need repeatable tests for unsafe behavior before agents reach real credentials, money movement, customer systems, or production automation.
How it solves it#
Agent security focus
IronClaw centers on testing agent behavior rather than generic model chat quality. That focus fits teams adding tool access or automation to LLM applications.
Evaluation workflow
The project provides a public codebase for building and running agent evaluations, giving teams a concrete place to encode unsafe-action scenarios.
Apache-2.0 project code
Apache-2.0 licensing supports internal evaluation programs and commercial AI product teams that need to adapt the toolkit.
Strengths and trade-offs#
Strengths
- Tests the action layerIronClaw is useful because agent risk often lives in tool calls and decision loops, not only in text output. That makes it relevant before connecting agents to sensitive systems.
- Useful for early safety gatesTeams can use IronClaw-style evaluations while designs are still changing, instead of waiting for a formal security review at launch.
Trade-offs
- -Needs team-specific scenariosAgent risk depends on the tools, permissions, and data an application exposes. IronClaw still requires teams to write evaluations that match their product.
- -Not a full governance platformTeams still need access control, logging, incident response, and human review around agent deployments.
What it's built on#
- Languages
- JavaScriptPythonRust
- Frameworks
- React
FAQ#
What is IronClaw for?
IronClaw is for evaluating and securing AI agents, especially agents that can take actions through tools.
What license does IronClaw use?
IronClaw is Apache-2.0 licensed.
Does IronClaw replace a security review?
No. It can support security testing, but teams still need threat modeling, access controls, and operational review.
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