Output.ai Framework - Complete Project Context

What is Output.ai?

Output.ai provides infrastructure for building production-grade AI workflows: fact checkers, content generators, data extractors, research assistants, and multi-step agents. Built on Temporal, it guarantees durable execution - if execution fails mid-run, it resumes from the last successful step.

Core Philosophy

Separation of orchestration from I/O:

• Workflows orchestrate execution (must be deterministic - no I/O)
• Steps/Evaluators handle all I/O operations (HTTP, LLM, database calls)

This separation enables automatic retries, resumption, and debugging.

Component Taxonomy

Component	Purpose	Key Rule
Workflow	Orchestrates step execution	Must be deterministic (no I/O, no Date.now(), no Math.random())
Step	Handles all I/O operations	Where HTTP, LLM, DB calls happen
Evaluator	Quality assessment	Returns confidence-scored results for validation loops
Scenario	Test input data	JSON files matching workflow's inputSchema
Prompt	LLM templates	Liquid.js templating with YAML frontmatter config
Eval Test	Offline quality testing	Dataset-driven verification with verify() from @outputai/evals

Project Structure

config/
├── credentials.yml.enc          # Global encrypted credentials
├── credentials.key              # Global decryption key (DO NOT COMMIT)
└── credentials/                 # Environment-specific credentials
    ├── production.yml.enc
    └── production.key
src/
├── shared/                      # Shared code across workflows
│   ├── clients/                 # API clients (e.g., jina.ts, stripe.ts)
│   └── utils/                   # Utility functions (e.g., string.ts)
└── workflows/                   # Workflow definitions
    └── {workflow_name}/
        ├── workflow.ts          # Orchestration logic (deterministic)
        ├── steps.ts             # I/O operations
        ├── types.ts             # Zod schemas (input, output, internal)
        ├── evaluators.ts        # Quality checks (optional)
        ├── utils.ts             # Local utilities (optional)
        ├── credentials.yml.enc  # Workflow-specific credentials (optional)
        ├── prompts/             # LLM templates (optional)
        │   └── generate@v1.prompt
        ├── scenarios/           # Test inputs (optional)
        │   └── happy_path.json
        └── tests/               # Offline eval tests (optional)
            ├── datasets/        # YAML test datasets
            │   └── happy_path.yml
            └── evals/           # Eval evaluators and workflow
                ├── evaluators.ts
                └── workflow.ts

Code Reuse Rules

Shared directory (src/shared/):

• shared/clients/ - API clients using @outputai/http for external services
• shared/utils/ - Helper functions and utilities

Allowed imports:

• Workflows/steps can import from ../../shared/clients/*.js and ../../shared/utils/*.js
• Workflows/steps can import from local files (./types.js, ./utils.js)

Forbidden:

• Importing from sibling workflow folders (../other_workflow/steps.js)
• Steps importing other steps (activity isolation requirement)

Critical Rules

Rule	Correct	Incorrect
Zod import	import { z } from '@outputai/core'	import { z } from 'zod'
HTTP client	import { httpClient } from '@outputai/http'	import axios from 'axios'
Credentials	import { credentials } from '@outputai/credentials'	process.env.SECRET
LLM calls	import { generateText, Output } from '@outputai/llm'	Direct provider SDK
ES imports	import { fn } from './file.js'	import { fn } from './file'
Workflow I/O	Call steps for any I/O	Direct fetch/http in workflow

Determinism violations (never in workflows):

• Date.now(), new Date()
• Math.random(), crypto.randomUUID()
• Direct HTTP/fetch calls
• File system operations
• Environment variable reads

---

Available Tools Inventory

Agents

Agent	Purpose
workflow-planner	Designs workflow architecture, creates implementation blueprints
workflow-debugger	Analyzes workflow execution traces, identifies issues
workflow-quality	Reviews code quality, validates implementations
workflow-prompt-writer	Creates and optimizes LLM prompt templates
workflow-context-fetcher	Gathers documentation and existing patterns

Commands

Command	Purpose	When to Use
/output-plan-workflow	Plan workflow architecture	ALWAYS FIRST - creates implementation blueprint
/output-build-workflow	Build/implement workflows	After planning, or for modifications
/output-debug-workflow	Debug workflow issues	When workflows fail or behave unexpectedly

Skills

Workflow Operations

Skill	Purpose
output-workflow-run	Synchronous workflow execution (waits for result)
output-workflow-start	Asynchronous workflow execution (returns ID)
output-workflow-list	List available workflows
output-workflow-status	Check async workflow status
output-workflow-result	Get async workflow result
output-workflow-reset	Rerun a workflow from after a completed step

Monitoring & Debugging

Skill	Purpose
output-workflow-stop	Stop running workflow
output-workflow-trace	Trace workflow execution
output-workflow-runs-list	List workflow run history
output-dev-workflow-cost	Calculate cost of a workflow run
output-services-check	Verify Output services status

Error Diagnosis

Skill	Catches
output-error-zod-import	Wrong zod import source
output-error-nondeterminism	Date.now, Math.random in workflows
output-error-try-catch	Missing error handling in steps
output-error-missing-schemas	Incomplete Zod schema exports
output-error-direct-io	I/O operations in workflow files
output-error-http-client	Using axios instead of @outputai/http

Meta/Lifecycle

Skill	Purpose
output-meta-pre-flight	Pre-operation validation checks
output-meta-post-flight	Post-operation verification
output-meta-project-context	Load full project context (this skill)

Development

Skill	Purpose
output-dev-folder-structure	Project and workflow directory layout
output-dev-workflow-function	Writing deterministic workflow files
output-dev-step-function	Writing step functions for I/O
output-dev-types-file	Zod schema definitions
output-dev-evaluator-function	Quality assessment functions
output-dev-eval-testing	Offline eval tests with @outputai/evals
output-dev-prompt-file	LLM prompt templates with Liquid.js
output-dev-model-selection	Pick a current LLM model via the AI Gateway listing
output-dev-upgrade-prompt-models	Bulk-upgrade model: fields across .prompt files
output-dev-scenario-file	Test input JSON files
output-dev-http-client-create	Shared HTTP API client patterns
output-dev-create-skeleton	Generate workflow skeleton

Credentials

Skill	Purpose
output-dev-credentials	Full credentials system reference (API, scopes, merging, custom providers)
output-credentials-init	Initialize encrypted credentials files for the first time
output-credentials-edit	View and edit credential values with show/get/edit commands
output-credentials-env-vars	Wire credentials to env vars using the credential: convention

---

CLI Quick Reference

# Development
npx output dev                              # Start dev environment

# List & inspect
npx output workflow list                    # List available workflows

# Execute
npx output workflow run <name> --input '{}'  # Run synchronously (waits)
npx output workflow start <name> --input '{}' # Run async (returns ID)
npx output workflow status <id>              # Check async status
npx output workflow result <id>              # Get async result

# Debug
npx output workflow debug <id>               # Debug failed workflow
npx output workflow debug <id> --json # Machine-readable output

# Rerun from a step (replays up to <stepName>, re-executes everything after)
npx output workflow reset <id> --step <stepName>
npx output workflow reset <id> --step <stepName> --reason "why"

# Eval Testing
npx output workflow test <name>              # Run eval tests against datasets
npx output workflow test <name> --cached     # Use cached output (fast)
npx output workflow test <name> --save       # Run fresh and save results
npx output workflow dataset list <name>      # List datasets for a workflow
npx output workflow dataset generate <name> --input '{}'  # Generate dataset

# Credentials
npx output credentials init                  # Initialize encrypted credentials
npx output credentials edit                  # Edit credentials (decrypts, opens $EDITOR)
npx output credentials show                  # Show decrypted credentials
npx output credentials get <path>            # Get single credential value

---

Naming Conventions

Element	Convention	Example
Workflow folder	snake_case	fact_checker/
Workflow name	snake_case	name: 'fact_checker'
Step functions	camelCase	fetchArticle(), analyzeContent()
Schema names	PascalCase	InputSchema, ArticleData
Prompt files	snake_case@version.prompt	analyze_claim@v1.prompt
Scenario files	snake_case.json	happy_path.json

---

Common Patterns

Workflow Pattern

import { workflow, z } from '@outputai/core';
import { fetchData, processData } from './steps.js';

export const inputSchema = z.object( { url: z.string().url() } );
export const outputSchema = z.object( { result: z.string() } );

export default workflow( {
  name: 'my_workflow',
  description: 'Processes data from URL',
  inputSchema,
  outputSchema,
  fn: async input => {
    const data = await fetchData( input.url );
    const result = await processData( data );
    return { result };
  }
} );

See output-dev-workflow-function for comprehensive patterns.

Step Pattern

import { step, z } from '@outputai/core';
import { httpClient } from '@outputai/http';

export const fetchData = step(
  { name: 'fetchData', inputSchema: z.string(), outputSchema: z.any() },
  async url => {
    const client = httpClient( { prefixUrl: url } );
    const response = await client.get( '' );
    return response.json();
  }
);

See output-dev-step-function for comprehensive patterns.

HTTP Client Pattern (Shared)

Clients live in src/shared/clients/ and are shared across all workflows.

// src/shared/clients/example.ts
import { FatalError, ValidationError } from '@outputai/core';
import { httpClient } from '@outputai/http';
import { credentials } from '@outputai/credentials';

const API_KEY = credentials.require( 'example.api_key' );

const client = httpClient( {
  prefixUrl: 'https://api.example.com',
  headers: { Authorization: `Bearer ${API_KEY}` },
  timeout: 30000,
  retry: { limit: 3, statusCodes: [ 408, 429, 500, 502, 503, 504 ] }
} );

export async function fetchFromExample( query: string ): Promise<ExampleResponse> {

  try {
    const response = await client.get( 'endpoint', { searchParams: { q: query } } );
    return response.json();
  } catch ( error: unknown ) {
    const err = error as { status?: number; message?: string };
    if ( err.status === 401 || err.status === 403 ) {
      throw new FatalError( `Auth failed: ${err.message}` );
    }
    throw new ValidationError( `Request failed: ${err.message}` );
  }
}

Error type guidelines:

• FatalError: 401, 403, 404 (won't succeed on retry)
• ValidationError: 429, 5xx (may succeed on retry)

See output-dev-http-client-create for comprehensive patterns.

Evaluator Pattern

Evaluators return confidence-scored results. Three result types available:

import { evaluator, z, EvaluationBooleanResult, EvaluationNumberResult, EvaluationStringResult } from '@outputai/core';

// Boolean evaluator - pass/fail checks
export const evaluateCompleteness = evaluator( {
  name: 'evaluate_completeness',
  description: 'Check if content meets minimum length',
  inputSchema: z.object( { content: z.string(), minLength: z.number() } ),
  fn: async ( { content, minLength } ) => {
    return new EvaluationBooleanResult( {
      value: content.length >= minLength,
      confidence: 1.0,
      reasoning: `Content has ${content.length} chars (min: ${minLength})`
    } );
  }
} );

See output-dev-evaluator-function for comprehensive patterns.

Prompt File Pattern

Prompts use YAML frontmatter + Liquid.js templating. Location: src/workflows/{name}/prompts/

---
provider: anthropic
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: claude-sonnet-4-6
temperature: 0.7
maxTokens: 4096
---

<system>
You are an expert content analyzer.

{% if context %}
Additional context: {{ context }}
{% endif %}
</system>

<user>
Analyze the following content:

<content>
{{ content }}
</content>

Provide {{ numberOfPoints | default: 3 }} key insights.
</user>

Using in steps:

import { generateText, Output } from '@outputai/llm';
import { z } from '@outputai/core';

// Structured output
const { output } = await generateText( {
  prompt: 'analyze@v1',
  variables: { content: 'Article text...', numberOfPoints: 5 },
  output: Output.object( {
    schema: z.object( { insights: z.array( z.string() ) } )
  } )
} );

// Text output
const { result } = await generateText( {
  prompt: 'summarize@v1',
  variables: { content: 'Article text...' }
} );

Provider & model selection: the SDK supports anthropic, openai, vertex, bedrock, azure, and perplexity (see sdk/llm/src/ai_model.js for the registered list). Don't pin specific model IDs in docs — they drift. To pick a current model, run output-dev-model-selection, which queries the AI Gateway model index live.

See output-dev-prompt-file for comprehensive patterns.

---

Practical Tips

Docker & Services

• Restart worker after adding workflows: docker restart <project>-worker-1
• View worker logs: docker logs -f output-worker-1
• Check services: Use output-services-check skill

Payload Limits

• Temporal: ~2MB per workflow input/output
• gRPC: ~4MB maximum
• For larger data, use file storage and pass references

Debugging Workflow Failures

1. Get the workflow ID from error output
2. Run npx output workflow debug <id> --json
3. Look for: failed step name, error message, input that caused failure
4. Check if issue is determinism, schema validation, or external API