What is Output.ai?

Output is an open-source TypeScript AI development framework for building, testing, and running production AI workflows.It centralizes prompts, traces, configs, and tests in a version-controlled repository so engineering and ML teams can review and iterate workflows using Git.

Built-in tracing and cost tracking record every LLM call and third-party service call for observability, debugging, and cost analysis.Evaluation-driven development provides deterministic checks and LLM-based evaluators, enabling CLI-driven replays and unit-style tests to validate prompt and workflow changes.

Durable execution and automatic retries integrate with Temporal to handle rate limits, long-running tasks, and reliable workflow recovery at scale.Repo-scoped prompt files, coding agent scaffolding, and visual builders reduce context switching and help product teams maintain reproducible prompt engineering practices.

Encrypted, environment-scoped credential management and CLI tooling secure API keys and deployment secrets without exposing raw values.CLI initialization, documentation, and a GitHub-first structure support faster onboarding and reproducible delivery of AI features.

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Output.ai's key features

Open-source TypeScript framework for building, testing, and running production AI workflows
Centralized, repo-scoped management of prompts, traces, configs, and tests under version control (Git)
Built-in tracing and cost tracking that records every LLM call and third-party service call
Evaluation-driven development with deterministic checks, LLM-based evaluators, CLI-driven replays, and unit-style tests
Durable execution and automatic retries integrated with Temporal for rate limits, long-running tasks, and workflow recovery

Output.ai use cases

Create reproducible, auditable AI pipelines in TypeScript by centralizing repo-scoped prompts, configs, traces and tests in Git so teams can track LLM calls, costs and evaluation-driven regressions while managing encrypted credentials for secure collaboration
Build durable, retry-safe long-running AI workflows (e.g., document ingestion, multi-step orchestrations, model fine-tuning) using Temporal-backed execution with automatic retries, LLM call tracing and cost tracking, and repo-scoped prompts to ensure reliable, observable runs
Implement automated CI/CD for AI features by gating merges with evaluation-driven tests, tracing LLM interactions and costs for accountability, storing prompts and configs in the repo, and securely handling secrets with encrypted credential management