Timbal AI

Timbal AI: Build AI agents, workflows, and apps in one stack | Product Hunt

Timbal AI

Launching today

Build AI agents, workflows, and apps in one stack

55 followers

Build AI agents, workflows, and apps in one stack

55 followers

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AI Workflow Automation

Timbal helps teams turn AI prototypes into production systems. Build agents and workflows, connect them to your data, design interfaces, deploy, monitor, evaluate, and govern everything from one platform. Instead of assembling separate tools for retrieval, orchestration, UI, observability, and evals, Timbal gives you one core for shipping reliable AI applications.

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Launch tags:Productivity•SaaS•Artificial Intelligence

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What makes your orchestration different from existing tools? A real customer story would answer that quickly.

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21m ago

Maker

@alheri_murya Fair challenge, Alheri. Two parts to the answer.

First, Timbal isn't an agent project sitting on top of someone else's stack. It's a full suite built on our own proprietary Python framework, where orchestration, evals, observability, and the data layer are all native to the same runtime. That matters because the failure modes of agents in production usually live between tools, and we don't have a between.

A concrete example. For a client automating public tender analysis, we run a workflow that calls different specialized agent nodes: one extracts requirements from the tender docs, one checks them against the client's catalog, one drafts the response. Each node runs with ACE (our Action Control Engine) turned on, which enforces the expected behavior of every step. If a node's output doesn't pass, the runtime retries automatically, and if the primary model keeps failing it falls back to a secondary model, all without a human touching it. And when a step genuinely needs a human, you configure human-in-the-loop directly at the framework level: the workflow pauses, routes to a person for review or approval, and resumes with their input. Every retry, fallback, and decision is traced, so when something looks off we can see exactly which step and why.

In most stacks all of that resilience logic is glue code you write and maintain yourself. Here it's just how the runtime works. Happy to go deeper on any piece of it.

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12m ago

Congrats on the launch! 🚀

Been using Timbal and honestly having all the tools I need in one place is what sold me, it makes building AI solutions genuinely simple instead of stitching together five different things. And the monitoring side is a huge plus, actually knowing what your agent is doing instead of guessing.

One question: does ACE ever get in the way when a task needs more flexible reasoning, or can you loosen it per agent/step?

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33m ago

Maker

@carla_granados_soler Thank you so much for this, and for putting Timbal through its paces on the daily 🙌

Great question. ACE isn't one global switch, it's set per step/agent, so you can tighten it where you want strict guardrails (approvals, writes, anything customer facing) and loosen it where you actually want the model to explore, like open ended research or ambiguous classification.

The mechanism is the easy part honestly, the harder part is knowing where to draw that line for a given use case, we're still learning from real usage what the right defaults should be.

Have you hit a specific case where it felt too rigid? Would love to dig into it with you.

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24m ago

How easy is it to migrate an existing project into Timbal. A guided import feature would make adoption much easier.

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16m ago

Maker

@advin_jadis Great question, and honestly the guided import you're describing pretty much exists, it's called Composer.

Two ways to migrate today:

1. Send your code or workflow directly to Composer, our super agent. It can translate workflows from Make, n8n, and other providers straight into Timbal, so you're not rebuilding node by node.

1. Connect your GitHub repo and let Composer manage the migration from there: it reads your existing project and brings it into the Timbal stack.

Either way you end up with a native Timbal workflow, with the eval and observability layer on top from day one, not a wrapper around your old setup.

If you have a specific project in mind (a Make scenario, an n8n flow, a repo), try throwing it at Composer and tell us where it struggles. That feedback is gold for us right now.

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2m ago

I enjoy platforms that remove unnecessary complexity. What level of customization do developers have for interfaces. More template examples could help new users build faster.

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5m ago

Maker

@gaspard_dupuich Thanks Gaspard! Short answer: whatever level they want.

Think of it as a Lovable inside Timbal. You can keep iterating with Composer in natural language until the interface is exactly what you had in mind, and if you want to go deeper you can download the code or connect your GitHub and work on it directly. It's real code you own, not a locked template, so there's no ceiling where the builder stops and you're stuck.

On templates: we already ship with them. They're not exposed as a gallery in the platform yet (coming soon), but Composer already fetches them from the backend and picks the ones that match the intent of your prompt. So in practice you're rarely starting from a blank canvas, even if you never see the template library itself.

Curious what kind of interface you'd build first, that helps us prioritize which templates to surface.

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1m ago

Maker

Hey Product Hunt 👋🏻

I'm Martí, co-founder and CEO of Timbal AI.

In today's AI world, going from 0 to 1 it's easy fast and cheap, going from 1 to 100 is not.

Complex data, legacy software, messy folders and heavy compliance and cybersecurity requirements, that's the enterprise reality.

You can prototype an agent in an afternoon, but then the real work starts. You have to wire together a vector database, an orchestration framework, a UI tool, and an observability layer. Before you know it, you're maintaining a fragmented stack of vendors, and your app still breaks in ways you can't easily trace.

Timbal is the unified stack that closes that gap. Everything you need to go from idea to production lives in one place:

A Database Native to AI: Run vector, keyword, and relational searches in a single query, ensuring your agents are always grounded in your actual data.

Deterministic Workflows: A reliable runtime for agents with built-in observability, traceability, and evals. You will always know exactly why your AI made a specific decision.

Omnichannel Visual Builder: Turn your logic into real apps instantly. Ship directly to the web, WhatsApp, email, or voice.

Our core is open source (Python framework, NPM packages, and TypeScript SDK on GitHub). You can stay in the code, build visually, or seamlessly mix both.

Putting this in front of the PH community is the milestone we’ve been waiting for. We want your brutal feedback—tell us what you'd build, what's missing, and where you think we're wrong.

Let's chat in the comments!

Martí & the Timbal team

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23h ago

Maker

Hey PH! Pedro here, Head of Product at Timbal.

Martí covered the big picture, so I wanted to add a bit on Composer specifically, the piece I spent the most time on for this launch. Most no-code builders get you 80% of the way and then you hit a wall the moment you need real logic, a tricky auth flow, or a data model that doesn't fit the template. Composer lets you drop into actual code right at that point, no rewrite, no migration to a "real" stack later.

If you've ever hit that wall with another builder, I'd love to hear what broke it for you. That's exactly the kind of feedback that shapes what we build next.

Thanks for checking us out today 🙌

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1h ago

how do you manage version control across workflows. a simple visual history could help teams track every important change.

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11m ago