Output is live · free · last verified 24 Oct 2026 · viability monitor
Output
Open-source TypeScript framework where Claude Code scaffolds production AI agent workflows you own.
If your team already writes TypeScript in Claude Code, Output collapses four separate AI SaaS bills into one repo. Prompts in PRs, judges run from the CLI against cached runs, and a per-model cost report at the terminal are concrete wins, not slideware. The catch is that it is designed around Claude Code as the authoring surface — hedge across three agent frameworks and you will fight the conventions.
Verdict generatedVerified 5d ago · liveness 67/100 · cite: rightaichoice.com/tools/output
- AI engineers building multi-step agents in TypeScript who want code, not a canvas
- Teams already using Claude Code as their primary authoring surface
- Platform teams standardizing agent architecture, prompts and evals across projects
- Engineering orgs replacing separate prompt, eval, tracing and hosting SaaS bills
- Non-developers or teams without TypeScript skills
- Teams that want a visual drag-and-drop workflow builder
- Projects standardizing on an authoring agent other than Claude Code
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip Output if your team doesn't write TypeScript or already relies on a visual workflow canvas — the framework is designed around coding agents editing files in your repo, not around clicking through a builder.
Workflow traces are written to your file system or your own S3 bucket, so storage for high-volume agent runs lands on your cloud bill rather than a vendor's.
Output is published as an open-source Apache 2.0 project you install with `npx @outputai/cli init`, so the framework cost sits at the bottom of the agent-tooling stack — the money you actually spend is model and API usage, not a per-seat subscription. That compares favourably with hosted agent platforms and observability vendors that charge per seat or per trace, but you should weigh it against the engineering time for running Temporal and your own trace storage.
In short
Output — Open-source TypeScript framework where Claude Code scaffolds production AI agent workflows you own. Best for AI engineers building multi-step agents in TypeScript who want code, not a canvas, Teams already using Claude Code as their primary authoring surface, Platform teams standardizing agent architecture, prompts and evals across projects. Free to use.
What is Output?
Output — Open-source TypeScript framework where Claude Code scaffolds production AI agent workflows you own. Best for AI engineers building multi-step agents in TypeScript who want code, not a canvas, Teams already using Claude Code as their primary authoring surface, Platform teams standardizing agent architecture, prompts and evals across projects. Free to use.
Output is an Apache 2.0 TypeScript framework for building AI agents and multi-step LLM workflows. Instead of dragging boxes on a canvas, you describe the workflow in plain language and Claude Code scaffolds it into real files: workflow.ts, steps.ts, evaluators.ts, prompts, and test datasets. Everything lives in your repo, reviewable in pull requests. Prompts are stored as versioned .prompt files with YAML front matter naming the provider and model — the docs show anthropic with claude-sonnet-4-6 — so nothing hides in a vendor playground. Evaluation is handled by the @output.ai/evals package, which exports verify, Verdict, judgeVerdict, judgeScore, and judgeLabel so you can mix deterministic assertions with LLM-as-judge rubrics and run them from the CLI against cached production output. Tracing and cost tracking stay on your infrastructure. Every LLM call, step, and per-model cost lands as JSON in your file system or S3, with a CLI cost report that separates model spend from third-party API spend (the vendor page shows tavily and jina line items). Temporal.io runs underneath for durable execution, automatic retries, and workflow history — you write async/await and the framework handles 500s and rate limits. Built at GrowthX for production work with Lovable, Superhuman, Webflow, Augment Code, and Airbyte, Output targets TypeScript engineers who want prompts, evals, traces, and encrypted credentials in one framework rather than four SaaS subscriptions. It is code-first and Claude Code-first, so teams wanting a GUI builder should look elsewhere.
Key features
- Describe a workflow in natural language and let Claude Code scaffold workflow, steps, prompts and tests
- Open-source TypeScript framework released under the Apache 2.0 license
- Prompts stored as versioned .prompt files with YAML front matter naming provider and model
- Repo-native layout: workflow.ts, steps.ts, evaluators.ts, types.ts, prompts/ and tests/
- @output.ai/evals package with verify, Verdict, judgeVerdict, judgeScore and judgeLabel
- LLM-as-judge rubrics with confidence scores mixed with deterministic assertions
- Run evals against cached production output without re-executing (output eval --cached)
- Eval datasets checked into the repo, shown as airbyte.yml and augment_code.yml
- Trace every LLM call, step and cost to your file system or S3 as queryable JSON
- CLI cost reporting that splits per-model LLM spend from third-party API spend
- Temporal.io durable execution with automatic retries and workflow history
- Async/await authoring surface for retries, replays and load handling
- AES-256-GCM encrypted credentials scoped per environment and workflow via CLI
- Type-safe TypeScript throughout with coding-agent-friendly conventions
- One-time project setup via npx @outputai/cli init your_ai_project
Behind the verdict
Where Output earns its keep is the boring part of shipping agents. Retries, replays, and load handling are usually where frameworks thin out; Temporal.io under the hood means you write async/await and get durable execution and workflow history without wiring it yourself. We would reach for this when a TypeScript team is already prompting Claude Code daily and wants conventions rather than a blank canvas. The scaffold teaches the patterns, so a mid-level engineer ships structured code from week one instead of inventing a folder layout. The eval story is the real differentiator. Running `output eval blog_generator --dataset stripe_blog --cached` against cached output means you can change a prompt and validate against dozens of production scenarios without paying for re-execution. Mixing deterministic checks with LLM judges gives you programmatic gates instead of vibes. Where it bites: you are adopting a framework's opinions on directory structure, prompt file format, and credential handling all at once. Teams already deep in LangGraph or CrewAI will find migration friction, and anyone whose authoring agent is not Claude Code loses the main draw. Credentials are AES-256-GCM encrypted and scoped per environment and workflow, and the CLI keeps raw values away from your coding agent. That is a thoughtful detail — sharing .env files across a team is a real security hole, and no external vault subscription is required. The honest comparison: this is a self-owned alternative to prompt-management, eval, and observability SaaS. If you want a vendor SLA, a hosted dashboard, or drag-and-drop, pass. If you want the infrastructure in your git history, the Apache 2.0 license and file-system layout are hard to argue with.
Models under the hood
as of 2026-08-31
Limitations
- Output assumes Claude Code is your authoring surface and TypeScript is your language — there is no GUI, so non-developers cannot use it, and the natural-language-to-code loop is only as good as the coding agent driving it.
- Because prompts, traces and evals are files in your repo, you take on the operational work that a hosted vendor would otherwise absorb: your S3 bucket or filesystem holds the traces, and your CI runs the evals.
- The reliability layer depends on Temporal.io, so durable execution, retries and workflow history come with a Temporal deployment in your stack rather than being self-contained.
- Evaluation results come from LLM-as-judge rubrics with confidence scores, which means a passing gate is a statistical judgement rather than a proof.
as of 2026-10-07
Researching Output? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Is Output still active?
Output is live · free · last verified 24 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Output? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about Output?
What people actually say about Output — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
ResearchedDiscussed by users across 6 sources (Hacker News, YouTube, Product Hunt, App Store, Stack Overflow, Lemmy) · researched Sep 25, 2026.
Weighted by the 96 posts each of 6 sources contributed.
- +Cuts boilerplate for multi-step agent logic in TypeScript projects
- +Tight Claude Code integration means less glue code for existing users
- +Open-source model allows extending and auditing the framework
- +Type-safe modular architecture appeals to senior TypeScript engineers
- +Pre-built agent best practices reduce architectural decision fatigue
- −Locked to the Claude Code ecosystem with no provider switching support
- −Almost no independent community feedback validates real-world reliability
- −Advanced skill level makes it inaccessible to casual or junior developers
- −Keyword collisions with music hardware and medical terms muddy searchability
- −Code-first approach rules out visual or low-code builders entirely
- • Claude Code API usage costs scale with agent complexity and token consumption
- • Time cost of reviewing and refactoring natural-language-generated code for production
- • Lock-in cost if you later need to migrate to a different model provider
How we verified this
Every listed fact about Output— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 24 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Output 12 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed who it suits, features, our verdict, who should skip it, summary, description
- — changed link health
- — changed setup time, who it suits, description, our verdict, features, hidden costs, integrations, limitations, switching in, switching out, models, who should skip it, pricing fit, summary, use cases
- — changed who it suits, features, our verdict, who should skip it, summary, description
- — changed SSL certificate
- — changed who should skip it, switching in, switching out, description, pricing fit, hidden costs, setup time, our verdict, summary, who it suits, features
Showing the 6 most recent of 12 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Output cost in 2026?
Output: Free to use. Pricing last verified 24 Oct 2026.
Where the pricing makes sense
The company stage and team size where Output's pricing actually pencils out — and where peers do it cheaper.
Output is published as an open-source Apache 2.0 project you install with `npx @outputai/cli init`, so the framework cost sits at the bottom of the agent-tooling stack — the money you actually spend is model and API usage, not a per-seat subscription. That compares favourably with hosted agent platforms and observability vendors that charge per seat or per trace, but you should weigh it against the engineering time for running Temporal and your own trace storage.
Has Output's pricing changed?
No plan price for Output has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Output?
Pick Output if you are AI engineers building multi-step agents in TypeScript who want code, not a canvas, Teams already using Claude Code as their primary authoring surface, Platform teams standardizing agent architecture, prompts and evals across projects, Engineering orgs replacing separate prompt, eval, tracing and hosting SaaS bills.
Look elsewhere if you are Non-developers or teams without TypeScript skills, Teams that want a visual drag-and-drop workflow builder, Projects standardizing on an authoring agent other than Claude Code, Teams that need a hosted platform with vendor SLAs rather than self-owned infrastructure.
Skip it if Skip Output if your team doesn't write TypeScript or already relies on a visual workflow canvas — the framework is designed around coding agents editing files in your repo, not around clicking through a builder.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Output actually fits — and what changes day-one when you adopt it.
Runs `npx @outputai/cli init sales-research`, then asks Claude Code to create a workflow that researches a company and generates a sales brief; Claude scaffolds workflow.ts, steps.ts, prompts and realistic test scenarios into the repo.
Outcome: A runnable workflow with `output run sales-research acme-scenario`, prompts already version-controlled as .prompt files and readable in the first pull request.
Copies the framework's scaffolded layout — workflow.ts, steps.ts, evaluators.ts, types.ts, prompts/ and tests/ with eval datasets — into each team's project so every agent has the same structure.
Outcome: Consistent project structure across projects, with the framework's conventions teaching the patterns rather than an internal wiki page.
Reads a trace with `output workflow cost lead_enrichment`, sees claude-sonnet-4-6 at three calls for $0.0842 and gpt-4o-mini at one call for $0.0012, then asks Claude Code to swap the expensive step to a cheaper model and rerun evals against cached output.
Outcome: Per-model cost changes made and validated offline without re-executing production runs, with Temporal.io handling retries on the live workflow.
Use cases
- Build a multi-step sales-research agent that reads a company and emits a brief
- Parse call transcripts into structured sales follow-up drafts with prompts stored in the repo
- Automate code review workflows with LLM-judge evaluators and quality gates in CI
- Create a support agent that calls tools to look up orders, with retries handled by Temporal
- Prototype a text transformation and enrichment pipeline with per-step cost visibility
- Standardize agent project structure across several TypeScript repositories
- Run offline evals against cached production runs before shipping a prompt change
- Track model and API spend per workflow from the command line instead of a dashboard
Setup time & first value
How long it actually takes to get something useful out of Output — broken out by persona, not the marketing-page minute.
For a TypeScript developer already in Claude Code, first value is minutes: run `npx @outputai/cli init your_ai_project`, describe the workflow you want, and Claude Code scaffolds the steps, prompts and test scenarios. Reaching a workflow with real evals in CI is a day or two of tuning datasets and rubrics. Standing up Temporal for durable execution in production is the longer piece — budget a
Switching to or from Output
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From hand-rolled TypeScript agent glue: run the Output CLI to scaffold the workflow, then move existing prompt strings into versioned .prompt files.
- →From a hosted prompt playground: export prompts into .prompt files with provider and model front matter so they are reviewed in PRs.
- →From a third-party observability vendor: point trace output at your own file system or S3 bucket and read runs as JSON.
- →From ad-hoc scripts: define steps.ts and evaluators.ts so retries and results follow the framework's conventions.
- →From another agent framework: port step logic into the scaffolded structure and rewrite tests as evals with deterministic checks plus LLM judges.
- ↗To a hosted agent platform: replace the CLI run loop and eval commands with the platform's runtime, and accept prompts moving out of your repo.
- ↗To a visual workflow builder: re-create each step as a canvas node, losing the .prompt file review workflow.
- ↗To a hand-rolled stack: keep the .prompt files and eval datasets, and reimplement orchestration and tracing yourself.
- ↗To another code-first framework: port workflow.ts and steps.ts into the new framework's primitives and rebuild the eval harness.
- ↗To a managed tracing vendor: forward trace JSON to the vendor instead of your file system or S3 bucket.
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Output”, and we withheld 6: 6 could not be judged, because “Output” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Output.
Official links
What are the best Output alternatives?
The closest alternatives to Output we list are Mastra, omnigent and Microsoft Agent Framework. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Mastra | Freemium | Pricing not published | — |
| omnigent | Free | Free | — |
| Microsoft Agent Framework | Paid | Pricing not published | — |
Alternatives to Output
View all
Mastra
Open-source TypeScript agent framework with durable workflows, memory, and observability built into one stack.
omnigent
Open-source meta-harness that runs Claude Code, Codex, Pi and your own agents under one policy, sandbox and cost layer
Microsoft Agent Framework
Microsoft Agent Framework is an open-source SDK for building agentic AI with workflows, checkpoints, and the GA Agent Harness runtime.
Tools that pair well with Output
Common stack mates teams adopt alongside Output, with the specific reason each pairing earns its keep.
Mastra
Open-source TypeScript agent framework with durable workflows, memory, and observability built into one stack.
omnigent
Open-source meta-harness that runs Claude Code, Codex, Pi and your own agents under one policy, sandbox and cost layer
Microsoft Agent Framework
Microsoft Agent Framework is an open-source SDK for building agentic AI with workflows, checkpoints, and the GA Agent Harness runtime.
Output vs its closest rival
Output's closest head-to-head is with Presto Voice — Presto Voice and Output serve completely different markets. Presto Voice is a specialized drive-thru voice AI for QSR chains, offering tangible ROI through upselling and automation. Output is an open-source framework for developers building custom AI agents with Claude Code. Choose based on your domain: restaurant operations or AI engineering. Verdict generated
Head-to-head comparisons
Output vs Presto Voice
Presto Voice and Output serve completely different markets. Presto Voice is a specialized drive-thru voice AI for QSR chains, offering tangible ROI through upselling and automation. Output is an open-source framework for developers building custom AI agents with Claude Code. Choose based on your domain: restaurant operations or AI engineering.
GeneratedOutput vs Locus Robotics
If you run a warehouse needing physical automation to boost picking productivity, Locus Robotics' AMRs and Locus Array deliver proven results (2-3x gains) via a RaaS model. For developers building AI workflows with Claude Code, Output is a free, open-source framework that accelerates prototyping. These tools serve entirely different domains—choose based on whether your problem is physical logistics or software AI orchestration.
GeneratedOutput vs Truleo
Truleo and Output serve entirely different worlds: Truleo is a specialized, paid intelligence platform for law enforcement that connects siloed data and automates case lead generation. Output is a free, open-source framework for developers creating AI agents with TypeScript. Choose Truleo if you're in law enforcement needing to cut report writing from 40 to 7 minutes. Choose Output if you're a developer prototyping AI workflows with Claude Code.
GeneratedFrequently asked questions
Best-of guides
Used Output? Help shape our editorial sentiment research.