Provides hardware-isolated, sub-60ms, ultra-low-overhead sandboxes to run untrusted LLM/agent code. Offers event-level snapshots, kernel-level egress control, credential vaulting, and drop-in E2B SDK compatibility for high-density AI agent deployment.
[next-mdx-remote-client] error compiling MDX: Unexpected character `5` (U+0035) before name, expected a character that can start a name, such as a letter, `$`, or `_` More information: https://mdxjs.com/docs/troubleshooting-mdx
Provides a unified integration layer that lets AI agents call and manage third‑party service APIs while keeping credentials and approval workflows out of the agent's reach. Offers plugin-based integrations, per-tenant envelope encryption (KEK), configurable permission modes, and an optional Hub to host OAuth/webhook surfaces.
Frames LLM routing as a sequential decision process and introduces LLMRouter plus the xRouteBench benchmark to develop, evaluate, and deploy learned routing policies across heterogeneous LLMs, optimizing response quality versus inference cost.
Ingests and normalizes security telemetry, runs multi-model AI agents to produce replayable investigations and automated triage/response; key features include a step-by-step Investigation Ledger, CI-gated eval harness, and self-hostable deployments.
