Runloop sells cloud VM-isolated devboxes and sandboxes for AI agents. Around them it now offers agent lifecycle tooling: the Agent API, Axons and Broker event streams, Agent Gateway and MCP Hub for credential isolation, Benchmark Job Orchestration with W&B Weave, Reflex background agents, and VPC deployment. It is execution and eval infrastructure for agent builders and training workloads, not an RL-data or environments vendor.
ARC (Accrual Remote Coder) runs on Runloop Sandboxes, Axons and Broker and went from concept to production in 7 days. About 80% of Accrual's coding now runs through ARC, as stated in the case study. It quotes Siddarth Chandrasekaran, Accrual's co-founder and CTO.
Why it matters: A named customer in a regulated vertical (accounting) using the full agent lifecycle stack. Accrual is also featured on the homepage.
A tutorial with runnable examples. The agent runs in a Runloop devbox, Kernel Managed Auth holds the logins and credentials for the browser session, and spend limits (for example auto-approve up to $50, human approval for $50-$200) are enforced by orchestrator code outside the agent.
Why it matters: A reference pattern for keeping secrets away from agents that use browsers and payments. It is an integration tutorial, not an announced formal partnership.
ION builds self-improving websites run by agents and moved its full infrastructure to Runloop in three days, according to founder Samraaj Bath. The post claims Runloop supports 30,000+ concurrent environments and devbox starts under 100 ms.
Why it matters: A named production customer, though the case study is vendor-published and the capacity and latency figures are vendor claims.
Trajectory, a continual-learning platform that uses agents for model training and fine-tuning, burst to more than 10,000 concurrent devboxes. Lessons from the work led to HTTP/2 multiplexing in SDK 1.20.2, a wider Ubuntu base-image test matrix (Trajectory pinned 2018 and 2020 images), and package-mirror infrastructure for reliability under load.
Why it matters: A vendor-published scale signal for a training-style workload, with concrete lessons about the transport layer.
An X-Runloop-Host header lets customers serve devboxes behind their own branded domains without rewriting the Host header. It works only with authenticated tunnels and a valid bearer token.
Why it matters: Lets teams expose sandboxed agent services on their own domains without breaking host validation or cookies.
A TypeScript SDK (@runloop/remote-agents-sdk) that connects applications to Runloop-hosted remote agents (Claude Code, OpenCode and others) through Axons and Broker. It supports ACP (Agent Client Protocol) and Claude Code's native protocol. The repo runloopai/remote-agents-sdk is MIT-licensed.
Why it matters: An open SDK for running Claude Code or ACP-compatible agents remotely on Runloop infrastructure, with session replay and suspend/resume.
Runloop announced it is part of Stripe Projects and provides sandboxed execution for agents that build, test and launch commerce applications. Per the release, its sandbox was used in a demonstration at the Stripe Sessions 2026 keynote.
Why it matters: An ecosystem tie to Stripe (the CEO co-founded Index, which Stripe acquired) and a sign of Runloop's push into agentic commerce.
Announced via PR Newswire: a service that schedules and runs thousands of agent benchmark scenarios across models in live codebases, terminals and browser workflows, sets baselines, catches regressions, and sends trace data (reasoning steps, tool use, execution paths) to W&B Weave. Runloop calls it an industry first, which is a vendor claim. CIO First covered it the same day, and Runloop's media page also lists a CIO First item dated 2026-07-27.
Why it matters: Relevant to eval and RL teams that need large-scale agent regression testing with traces in W&B.
Axons are cloud agent sessions with multi-client streaming, durable history, SQLite-backed structured state, audit/replay and event-driven wakeups. They work with Devboxes, Secrets, Agent Gateway and MCP Gateway.
Why it matters: Gives long-running multi-agent workflows a coordination and audit layer, which helps data-ops traceability.
Runloop devboxes can serve as the sandbox backend for OpenAI Agents SDK agents, with suspend/resume, snapshots, blueprints and Axon observability. OpenAI's openai-agents-python repo lists a 'runloop' optional extra (runloop_api_client) next to E2B, Modal, Daytona and Vercel. Runloop's docs carry an integration guide.
Why it matters: OpenAI's own SDK packages the integration, so it is confirmed. It is one of several sandbox providers and a developer integration, not evidence that OpenAI is a customer.
A way to define, version and deploy agents onto Devboxes from Git, npm, PyPI or object storage. Agents are immutable and versioned with semver or a pinned commit. Stated use cases are benchmarking (keep the environment fixed while swapping agent versions), multi-agent workflows and production reliability.
Why it matters: Separates agent versioning from environment builds, which makes eval comparisons more reproducible.
An HTTP request to a tunnel URL now wakes a suspended devbox (vendor says in under a second). The first request gets a 503 with retry instructions. Aimed at sporadic workloads such as GitHub webhooks and PR reviews.
Why it matters: Long-lived agent endpoints can stay suspended until they are called, which cuts idle compute costs.