Stagent: Open-source ops layer for Claude Agent SDK with local governance and workflow orchestration

✍️ OpenClawRadar📅 Published: April 14, 2026🔗 Source
Stagent: Open-source ops layer for Claude Agent SDK with local governance and workflow orchestration
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What Stagent does

Stagent is an open-source, local-first coordination workspace that sits on top of the Claude Agent SDK and Claude API. It standardizes how you route, supervise, and measure agent work across both runtimes without replacing them. The core problem it solves: you shouldn't need a spreadsheet to track what your AI agents are doing, what they cost, or whether they have permission to run dangerous commands.

Key features and architecture

The system includes 15 product surfaces: home dashboard, execution board, inbox, monitoring, cost ledger, chat, environment scanner, and more. It provides 6 workflow orchestration patterns: sequence, parallel fork/join, checkpoint, planner-executor, autonomous loop, and multi-agent swarm. There are 52+ reusable agent profiles including specialist personas like code reviewer, researcher, document writer, wealth manager, and travel planner bundled as Claude Code skills with tool policies and behavioral instructions.

Human-in-the-loop governance includes allow once, always allow, and deny permissions. Every tool request routes through a notification queue, with AskUserQuestion always prompting regardless of saved permissions. Budget guardrails provide daily/monthly spend caps that hard-stop new provider calls when exceeded, with warnings at 80% and graceful completion of already-running work.

The cross-runtime cost ledger tracks token velocity, model concentration, runtime share, and per-task audit trails across Claude and Codex in one view. Scheduled runs support recurring or one-shot prompts with agent-profile selection, firing limits, and expiry windows.

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Technical implementation

The entire product was built using Claude Code with Opus, from database schema to UI components. The architecture is local-first with zero external dependencies, using SQLite in WAL mode with Drizzle ORM (16+ tables). Everything runs on your machine with no cloud or telemetry.

The approval system uses the notification table as a message queue. When an agent requests a dangerous tool, canUseTool polls the notification table until a human responds, enabling governance without websockets or external queues.

Workflow patterns include autonomous loops that run agents iteratively where each iteration sees prior output (inspired by Karpathy's "one GPU research lab" concept), multi-agent swarms using a Mayor→Workers→Refinery pattern with bounded concurrency (2-5 workers) and step-level retry, and fork/join parallel that splits research questions across branches and synthesizes results.

The blueprint catalog means you never manually configure workflows. You pick a template (code review, research deep-dive, sprint planning), fill in variables, and the blueprint resolves profiles, prompts, and conditional steps automatically.

The environment scanner discovers all your Claude Code and Codex CLI artifacts — skills, hooks, MCP servers, permissions, memory files — and presents a unified health score, with typical scans taking 10-50ms.

Tech stack

Next.js 16, React 19, TypeScript, Tailwind v4, shadcn/ui, SQLite, Drizzle ORM, Claude Agent SDK, Codex App Server.

📖 Read the full source: r/ClaudeAI

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👀 See Also