Bloblang playground just got smarter

You know that moment when you're typing:

this.orders.index(0).line_items.index(0).product

…for the third time and thinking, "There has to be a better way"?

Well, now there is.

We've added intelligent autocomplete into the Bloblang playground!

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Since launching the Bloblang playground last year, developers have been using it to prototype everything from simple JSON reshaping to complex event stream transformations. But as Bloblang's role expanded to powering MCP (Model Context Protocol) servers that connect AI agents to your data infrastructure, we realized the playground needed to level up.

This update delivers exactly that. We have smarter autocomplete that understands your data, plus better ways to share, export, and collaborate on real-world mappings.

Autocomplete that actually helps

This isn’t a keyword dump. The new autocomplete understands types, structure, and context, so it suggests what you actually want right when you need it.

Field extraction

The playground now traverses your structured inputs and suggests objects to access.

Given this input:

{
  "orders": [
    {
      "id": "ORD-123",
      "line_items": [
        {"product": "Widget", "price": 10},
        {"product": "Gadget", "price": 20}
      ]
    }
  ]
}

When you type:

 this.orders.

You’ll see suggestions like:

orders.index(0).id
orders.index(0).line_items

The playground shows you exactly how to access data with Bloblang's .index() syntax.

Dynamic metadata detection

Forget hardcoded metadata keys. The playground now discovers metadata from two sources:

From the metadata input box (simulates message metadata):

{
  "kafka_topic": "orders",
  "correlation_id": "req-456"
}

From metadata you set in your mapping:

meta trace_id = "xyz-789"
meta span_id = "abc-123"

Type @ and you’ll see metadata keys from both sources. The playground even detects metadata you defined earlier in the same mapping, so when you type @trace_ after setting meta trace_id = ..., it autocompletes immediately.

Function and method completion with parameter hints

Bloblang has hundreds of functions. Now you don't need to memorize them.

Autocomplete now scopes suggestions by type:

• "user@example.com". results in suggestions for string methods like split(), contains(), lowercase(), and re_replace_all().
• order.created_at.ts_ results in suggestions for timestamp methods like ts_parse(), ts_format(), and ts_unix().

When you select a function, you also get parameter snippets with tab navigation:

.replace("old", "new")  // Tab through each parameter

Select a function or method, tab through parameters, fill them in, and keep moving.

Export, share, and collaborate

The playground has always supported sharing via URL. Now it goes further.

Export configuration (Ctrl/Cmd + E)

Download your entire playground state as JSON. Version control it, share it with teammates, and build a library of reusable transformations.

Import configuration

Import a saved JSON file to restore a previous session. Great for:

• Resuming work across devices
• Testing edge cases with pre-configured inputs
• Onboarding new team members with working examples

Share link (Ctrl/Cmd + S)

The original share feature—now faster. Generate a URL that encodes your full playground state, so your teammates see exactly what you see.

Less, "It works on my machine," more, "Here's the exact config I tested.”

Keyboard shortcuts for power users

If you live on the keyboard, we’ve got you covered.

Press Ctrl/Cmd + / to see the full shortcuts overlay. There's also an execution time display so you can see if that nested .map_each() is killing performance.

Try Bloblang playground

What started as a quick way to test a mapping has become a place to write production Bloblang, debug real data, and ship configs you’ve actually validated. If you’re using Bloblang to power pipelines or MCP servers for AI agents, this playground is now built for that workflow.

Ready to try it?

👉 Open the Bloblang Playground and test the autocomplete yourself.

👉 Import this customer orders example and see how fast you can modify it.

👉 Learn about building MCP servers to connect AI agents to your data infrastructure.

👉 Join our Community Slack to share your Bloblang mappings, ask questions, and learn from other developers.