End-to-end infrastructure for training and inferencing open weight models

AC2, an end-to-end infrastructure platform for training and inferencing open weight models, has launched with SDK, runtime, and tracing packages that support coding agents like Claude Code, Cursor, and Codex. The platform enables users to evaluate agents, train models using GRPO, supervised fine-tuning, or on-policy self-distillation, and route inference through policies and endpoints.

Onboard with your coding agent The fastest way to get started is to let your coding agent set AC2 up for you. Paste this prompt into Claude Code, Cursor, Codex, or any coding agent: Quickstart /quickstart . Packages - ac2.sdk — platform client for datasets, workloads, sessions, traces, and secrets. - ac2.runtime — agent framework for agents, environments, orchestrators, tasks, graders, and episodes. - ac2.tracing — OpenTelemetry-based observability SDK. Add @traced to any function to capture timing, inputs, outputs, and nested spans. Exports to ClickHouse and Langfuse. Quickstart Install the SDK and train a model. Runtime Define agents, environments, orchestrators, and eval tasks. SDK Manage datasets, deployments, runs, traces, and secrets from Python. MCP server Analyze eval and train traces from your editor. Choose a workflow Evaluate an agent Run evals, grade traces, and compare behavior. Train a model Choose GRPO, supervised fine-tuning, or on-policy self-distillation. Route inference Deploy a model and route traffic through Policies and Endpoints. FAQ Find answers for failed runs, datasets, package installation, and cleanup.