Smithers

Agent workflows you can watch live, rewind, fork, and replay.

Tell your coding agent to do real, multi-step work, then Smithers runs it for minutes or days: watch every step live, gate the risky ones behind human approvals, and rewind, fork, or replay any run. The same workflow runs across Claude Code, Codex, Pi, AI SDK models, and remote sandboxes.

Time travel: fork a run from any earlier frame and branch an alternate timeline. Every step is a database row, so live watching, rewind, and replay are built in.

What you get

• ⏪ Full observability and time travel: watch every step live, then rewind, fork, or replay any run from any point.
• 🛡️ Durable runs that survive crashes: every completed step is persisted the moment it finishes, so a run resumes from where it stopped instead of starting over.
• 🔌 Any agent, any model: Claude Code, Codex, Pi, Antigravity, and more, plus any model through the AI SDK. Swap the harness without rewriting the workflow.
• 🛠️ Higher-quality output: review loops, human approvals, and evals give agents the structure that real work demands.
• 🧩 Dozens of ready-to-run workflows: planning, implementation, review, debugging, tickets, audits, and long-horizon missions. Your agent can author new ones.

When to use Smithers

Smithers is the durable runtime for coding-agent work: when the unit of work is an agent editing a real repository over many steps, and you need that work to be inspectable, approvable, and recoverable.

Why not just let my agent orchestrate itself?

Claude Code, Codex, and the other harnesses already fan out subagents, and for work that fits in one sitting they are the right tool. The fan-out is ephemeral, though: it lives inside one session, one vendor, and one terminal.

When the work has to survive the session, hand the fan-out to Smithers. Your agent still drives everything; the run just stops being disposable. Detailed comparisons: vs. Claude Code Workflows, vs. Temporal, and vs. LangGraph. The longer argument is in the open, durable version of agent workflows.

Get started

Smithers is driven by your coding agent, not a GUI you click. Your agent runs Smithers on your behalf: it scaffolds workflows, kicks off runs, watches them, and handles approvals.

One command sets everything up. From inside your project:

bunx smithers-orchestrator init

init does everything:

• Installs the smithers skill into the coding agents on your machine (Claude Code, Pi, and more), so your agent knows how and when to use Smithers. No mkdir, no curl.
• Scaffolds .smithers/ with ready-made workflows (hello, implement, plan, review, debug, and more) your agent can pick from.

Then just ask:

"orchestrate an agent to add rate limiting and keep iterating until the tests pass."

Your agent picks the right workflow, starts the run, and keeps going through retries and review loops until the work is actually done.

To wire the MCP server into every detected agent too, run bunx smithers-orchestrator mcp add. See Agent Support for the full per-agent matrix, and skills/smithers/ for the onboarding skill itself.

What a workflow looks like

A workflow is a JSX tree of tasks. You usually don't write these by hand: you prompt your agent, and it writes them from the same primitives the built-in pack uses. Each example below starts with the prompt that produces it.

This page is the 90-second version. The Tour is the 15-minute version: it builds a real code-review workflow one capability at a time.

Loop until a reviewer approves

"implement this request and keep iterating until a reviewer signs off"

import { createSmithers, Loop, CodexAgent } from "smithers-orchestrator";
import { z } from "zod";

const { Workflow, Task, smithers, outputs } = createSmithers({
  input: z.object({ request: z.string() }),
  impl: z.object({ summary: z.string(), filesChanged: z.array(z.string()) }),
  review: z.object({ approved: z.boolean(), feedback: z.string() }),
});

const coder = new CodexAgent({
  model: "gpt-5.6-luna",
  config: { model_reasoning_effort: "medium" },
});
const reviewer = new CodexAgent({
  model: "gpt-5.6-sol",
  config: { model_reasoning_effort: "xhigh" },
  sandbox: "read-only",
});

export default smithers((ctx) => (
  <Workflow name="implement-reviewed">
    <Loop until={ctx.latest(outputs.review, "validate")?.approved} maxIterations={5}>
      <Task id="implement" output={outputs.impl} agent={coder}>
        {`Implement: ${ctx.input.request}
Address this reviewer feedback first: ${ctx.latest(outputs.review, "validate")?.feedback ?? "none yet"}`}
      </Task>

      <Task id="validate" output={outputs.review} agent={reviewer}>
        {`Review the working-tree changes for: ${ctx.input.request}.
Approve only when the change is correct and tested.`}
      </Task>
    </Loop>
  </Workflow>
));

This is the loop a one-shot agent call can't give you: implement, review, feed the feedback back in, repeat until approved. Every iteration is persisted, so a crash mid-loop resumes at the current iteration instead of iteration one.

The bigger version of this idea (split a request into tickets, implement them in parallel worktrees, gate on your approval, land through a merge queue) is examples/parallel-tickets.jsx: a small engineering team in one file.

Durable by default

Durability is the differentiator. Runs survive crashes, restarts, and flaky tools because every completed step is persisted to SQLite the moment it finishes. The runtime always knows what's done and what to run next. Approvals, human questions, retries, and replay are first-class.

prompt → render workflow → run task → validate output → persist to SQLite → re-render → resume · inspect · replay

That loop is the whole model: a task runs, its output is validated against a schema and written down, then the workflow re-renders from persisted state to decide the next task. A crash at any point resumes from the last write, not from the top.

A run killed mid-task, then resumed: the completed task is skipped, the interrupted task re-runs, the run finishes. No recovery code.

bunx smithers-orchestrator up workflow.tsx --input '{"description":"Fix bug"}'
bunx smithers-orchestrator up workflow.tsx --run-id abc123 --resume true   # resume after a crash
bunx smithers-orchestrator rewind abc123 --frame 4                          # time-travel to an earlier frame
bunx smithers-orchestrator fork abc123                                      # branch an alternate timeline
bunx smithers-orchestrator replay abc123                                    # replay from a checkpoint

Drive and watch your runs

Prefer the CLI? The seeded workflows run directly, and whether your agent started a run or you did, you can see exactly what's happening:

bunx smithers-orchestrator workflow run hello   # smallest possible run; prompt lives at .smithers/prompts/hello.mdx
bunx smithers-orchestrator workflow run plan --prompt "add rate limiting and API key rotation"

bunx smithers-orchestrator ps              # list active, paused, and recently completed runs
bunx smithers-orchestrator inspect RUN_ID  # steps, agents, approvals, and outputs for one run
bunx smithers-orchestrator logs RUN_ID     # tail the event log
bunx smithers-orchestrator chat RUN_ID     # read the agent's chat output

ps shows you what needs attention (a paused approval, a recent failure); inspect drills into a single run so you can follow each step and agent as it works. Run bunx smithers-orchestrator starters to browse plain-English starters.

Prefer a live page over every run? bunx smithers-orchestrator monitor opens the Smithers Monitor: the grouped run list, each run's execution tree with per-node status, and the structur