ai-agent-workflow_skill

This skill helps build resumable multi-agent workflows with durable execution and automatic stream recovery across client reconnections.

TypeScript

8

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill andrelandgraf/fullstackrecipes --skill ai-agent-workflow

SKILL.md2.9 KB

Overview

This skill builds resumable, multi-agent AI workflows with durable execution, tool loops, and automatic stream recovery when clients reconnect. It provides step-level persistence, streaming resumption, and orchestration primitives so long-running or interactive flows survive restarts and network drops. The goal is reliable, production-ready agent orchestration for full-stack web AI apps.

How this skill works

The skill wires a Workflow Development Kit into your TypeScript stack to persist workflow steps and agent state to a database. It captures streaming output and saves offsets so chat or agent streams can resume automatically on reconnect. Custom durable agents and tool loops run under durable execution semantics, and APIs/hooks let the client start, pause, resume, and monitor workflows.

When to use it

Coordinating multiple AI agents that must share state across steps
Long-running or multi-step automations that must survive server restarts
Interactive chat experiences that require stream resumption on client reconnect
Workflows that call external tools repeatedly and need durable retry/looping
Persisting provenance and results of agent executions for auditing or debugging

Best practices

Set up type-safe env configuration and database (Neon + Drizzle) before integrating workflows
Persist chat and step outputs with UUID v7-style IDs for chronological ordering
Use structured logging (Pino) to capture workflow lifecycle events and errors
Design steps to be idempotent so resumed execution is safe after partial runs
Expose explicit start/resume endpoints and use a client hook for automatic reconnection

Example use cases

A multi-agent research assistant that spawns separate agents for retrieval, synthesis, and summarization and resumes after network drops
An automated onboarding pipeline that runs identity checks, data enrichment, and email sequences with durable retries
A customer support chat that streams draft responses from an agent and resumes streaming when a user reconnects
An ETL-style workflow where agents call external APIs and loop tools until data quality checks pass
A content-generation pipeline that persists intermediate drafts and allows manual review before continuing

FAQ

You should use a transactional database that supports connection pooling in serverless environments; Neon Postgres with Drizzle is the recommended pattern here.

How does stream recovery work on reconnect?

The system saves stream offsets and partial outputs to the workflow store. When a client reconnects, the hook or API resumes the stream from the saved offset so the user sees continuity.