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Bonacci Studio
Agentic, Visual Data Engineering and Data Science Platform
Most AI data tools stop at generating a snippet you still have to run yourself. Bonacci Studio's agent has real tool access: it connects to your database, inspects schemas, writes PySpark or SQL, executes it on Spark, streams logs back, and debugs its own failures. Also included: a visual drag-and-drop DAG builder, Kafka streaming, Apache Camel API integration, RAG knowledge bases, and native MCP support. Bring your own model - Claude, GPT, Gemini, Groq, or fully local via Ollama
Hey Product Hunt 👋
I've spent years building data pipelines, and the same thing kept happening: I'd ask an
LLM for help, get a beautiful PySpark script, and then spend the next 40 minutes finding out it hallucinated three column names that don't exist in my schema.
The model wasn't the problem. The model just couldn't see anything.
So I built Bonacci Studio around one idea: give the agent real access, not a description
of reality. When you ask it to build a daily revenue rollup, it doesn't guess - it calls
inspect_schema("orders"), gets back 14 real columns and 2.4M rows, writes the PySpark against what's actually there, runs it on Spark, and streams the logs into your browser. If it fails, it reads its own stack trace and fixes it.
What's in it:
• Agentic engine - DB tools, SSH tools, code-gen, all in a real tool-calling loop
• Visual DAG builder - drag-and-drop for when you'd rather not type
• Three engines under the hood - Spark (batch), Kafka (streaming), Camel (APIs)
• Native MCP - client and server, so the agent extends to any MCP tool, and your
other agents can drive Studio
• Bring your own model - Claude, GPT, Gemini, Groq, or fully local with Ollama
• RAG knowledge bases so the agent knows your internal docs, not just your schema
It's built on Apache Spark, Kafka and Camel - proven infrastructure for data heavy loads.
The name derived from 'Fibonacci'. Pipelines grow the way nature does, one connection building on the last.
Free tier is real, not a trial - bring your own model key, connect a source and a target,
and actually run pipelines. No card. $29/mo if you want scheduling and more room.
I'm here all day and genuinely want the honest feedback.
the self-debugging loop where it streams logs back and fixes its own PySpark failures is genuinely impressive, not just a wrapper that pretends to be an agent. most teams would have shipped the snippet generator and called it done.
honestly the auto-debug loop is wild, like watching it actually catch its own PySpark errors and recover without me babysitting the run. that kind of self-correcting execution is what most "AI data tools" skip entirely.
honestly the self debugging loop sounds super useful, but it would be great to see some kind of cost or token usage tracker built in so you know what each agent run is actually spending, especially when you switch between claude and the local ollama setup
honestly the tool access is a huge step up, but it'd be really nice to see some kind of cost tracking per agent run. when the thing is spinning up PySpark and debugging itself across multiple iterations, you can easily blow through api credits without realizing it. even a rough token or spend estimate in the run history would help a lot
The agent debugging its own failures is honestly such a nice touch. One thing though, would be great if you could save and version the generated PySpark or SQL as actual files in a connected Git repo right from the studio, basically turning the agent runs into proper commit history you can review or roll back later.
About Bonacci Studio on Product Hunt
“Agentic, Visual Data Engineering and Data Science Platform”
Bonacci Studio was submitted on Product Hunt and earned 10 upvotes and 11 comments, placing #123 on the daily leaderboard. Most AI data tools stop at generating a snippet you still have to run yourself. Bonacci Studio's agent has real tool access: it connects to your database, inspects schemas, writes PySpark or SQL, executes it on Spark, streams logs back, and debugs its own failures. Also included: a visual drag-and-drop DAG builder, Kafka streaming, Apache Camel API integration, RAG knowledge bases, and native MCP support. Bring your own model - Claude, GPT, Gemini, Groq, or fully local via Ollama
Bonacci Studio was featured in Developer Tools (516.2k followers), Artificial Intelligence (474.3k followers) and Data & Analytics (5.7k followers) on Product Hunt. Together, these topics include over 189.2k products, making this a competitive space to launch in.
Who hunted Bonacci Studio ?
Bonacci Studio was hunted by Mallesh Madapathi. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Mallesh Madapathi