Ironclaw
IronClaw focuses on running AI agents that can touch real systems without putting secrets at risk. Built on NEAR AI Cloud and Rust, it wraps OpenClaw-style personal agents in encrypted enclaves, WebAssembly sandboxes, and an encrypted credential vault. The headline idea is simple: agents can browse, code, and automate while API keys, tokens, and passwords never become LLM-visible text.
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Pricing
- Starter: $0 per month. Activate 1 agent instance in a secure TEE environment and pay per usage token using NEAR AI Inference.
- Basic: $20 per month. Includes everything in Starter, plus credits for up to 2 agent instances and roughly 13 million tokens with usage pooling.
- Pro+: $200 per month. Includes everything in Basic, supports up to 5 agent instances, around 130 million tokens, and priority support.
- Self-hosted: Open-source code can be deployed on a team’s own infrastructure, with costs determined by underlying cloud and operations choices.
Features
- Encrypted credential vault: Stores API keys, tokens, and passwords encrypted at rest, injecting them only at the network boundary for explicitly allowlisted endpoints.
- Trusted Execution Environment (TEE): Each IronClaw instance boots inside a hardware-backed encrypted enclave on NEAR AI Cloud, protecting data in memory from the host and provider.
- WebAssembly tool sandboxing: Every tool runs in its own Wasm container with capability-based permissions, no filesystem access, strict resource limits, and constrained outbound networking.
- Leak detection for secrets: Outbound traffic is scanned in real time, and anything that resembles credential exfiltration is blocked before it reaches the internet.
- Rust-based runtime: The entire runtime is written in Rust, avoiding classes of memory bugs like buffer overflows and use-after-free, and skipping a garbage collector.
- OpenClaw compatibility and simple deploy: Offers the same agent capabilities as OpenClaw with one-click deployment on NEAR AI Cloud or local runs, plus open-source code on GitHub.
Use cases
- Security-conscious AI developers: Building agents that call production APIs, financial systems, or internal services where credential exposure is unacceptable.
- Platform and DevOps teams: Providing company-wide AI assistants while keeping strict guardrails around infrastructure and internal tools.
- Fintech and healthtech companies: Experimenting with agentic workflows on regulated or sensitive data while still respecting compliance and risk constraints.
- Research labs and data teams: Running exploratory agents over proprietary datasets while reducing exposure to model providers and third parties.
- Uncommon Use Cases: Used by red-team and security researchers to prototype exfil-resistant agent setups; adopted by solo founders who want “serious” security without standing up their own enclave stack.