Hi HN! Didn't expect this to be on the front page today! I should really release all the optimizations that've been landing lately, the version on github is about twice as fast as what's released on npm.

I wrote it when I was doing prototyping on doing streaming rendering of UIs defined by JSON generated by LLMs. Using constrained generation you can essentially hand the model a JSON serializable type, and it will always give you back a value that obeys that type, but the big models are slow enough that incremental rendering makes a big difference in the UX.

I'm pretty proud of the testing that's gone into this project. It's fairly exhaustively tested. If you can find a value that it parses differently than JSON.parse, or a place where it disobeys the 5+1 invariants documented in the README I'd be impressed (and thankful!).

This API, where you get a series of partial values, is designed to be easy to render with any of the `UI = f(state)` libraries like React or Lit, though you may need to short circuit some memoization or early exiting since whenever possible jsonriver will mutate existing values rather than creating new ones.

For those wondering about the use case, this is very useful when enabling streaming for structured output in LLM responses, such as JSON responses. For my local Raspberry Pi agent I needed something performant, I've been using streaming-json-js [1], but development appears to have been a bit dormant over the past year. I'll definitely take a look at your jsonriver and see how it compares!

[1] https://github.com/karminski/streaming-json-js

If anyone needs to do this in Python I've had success with both ijson and jiter - notes here: https://til.simonwillison.net/json/ijson-stream and https://simonwillison.net/2024/Sep/22/jiter/

I wrote something similar in my last job where we had to parse and query data from huge (50+ GB? I remember they weren't even fitting in my laptop) json files that were stored in an S3 Bucket..

We used the streaming parser to create an index of the file locally {json key: (byte offset, byte size)} and then simply used http range queries to access the data we needed.

Here is the full write up about it:

https://dinesh.cloud/2022/streaming-json-for-fun-and-profit/

And here is the open sourced code:

https://github.com/multiversal-ventures/json-buffet

Oh fun, I wrote a similar library in 2015 for Haskell. There is an annoying gotcha to deal with: there are sequences of valid characters that can be parsed incorrectly if you’re doing incremental chunks, namely if “0.0” is split across two input chunks you can get a token stream with two valid float literals rather than 1! Namely “0” and “.0”, which is just a really annoying wart of json float syntax.

I really like just encoding each object as JSON and then concatinating them with a new line between.

Allows parsing and streaming without any special libraries and allow for an unlimited amount of data (with objects being reasonably sized).

Usually gives these files the .jsonlines suffix when stored on disk.

Allows for batch process without requiring huge amounts of memory.

I wrote a more traditional JSON parser for my microcontooller project. You could iterate over elements and it would return "needs more data" if it was unable to proceed. You could then call it again after fetching more. Then just simple state machines to consume the objects.

The benefit with that was that you didn't need the memory to store the deserialized JSON object in memory.

This seems to be more oriented towards interactivity, which is an interesting use-case I hadn't thought about.

Interesting approach.

I would expect an object JSON stream to be more like a SAX parser though. It's familiar, fast and simple.

Any thougts on not chosing the SAX approach?

"has no dependencies, and uses only standard features of JavaScript so it works in any JS environment."

Then I see a Node style import and npm. When did Node/NPM stop being dependencies and become standardized by JavaScript? Where's my raw es6 module?

I don't get it (and I'd call this cumulative not incremental)

Why not at least wait until the key is complete - what's the use in a partial key?

It's no longer active, but Oboe.js did great stuff for a decade+ in this field! It has some very nice APIs for consuming. https://github.com/jimhigson/oboe.js/

It's less about incrementally parsing objects, and more about picking paths and shapes out from a feed. If you're doing something like array/newline delimited json, it's a great tool for reading things out as they arrive. Also great for example for feed parsing.

This is nice to parse incomplete JSON as they come in.

I did something similar for streaming but built it with a streaming protocol at the frame level wrapping the JSON messages [1]. The streaming protocol has support for both the LF based scheme and the HTTP Content-Length header based scheme. It's for supporting MCP and LSP.

[1] https://github.com/williamw520/zigjr/?tab=readme-ov-file#str...

If you get partial string values that are replaced with longer string values as they are streamed in, how do you know when the value is finished being read and is safe to use?

Oh, this is quite similar to an online parser I'd written a few years ago[1]. I have some worked examples on how to use it with the now-standard Chat Completions API for LLMs to stream and filter structured outputs (aka JSON). This is the underlying technology for a "Copilot" or "AI" application I worked on in my last role.

Like yours, I'm sure, these incremental or online parser libraries are orders of magnitude faster[2] than alternatives for parsing LLM tool calls for the very simple reason that alternative approaches repeatedly parse the entire concatenated response, which requires buffering the entire payload, repeatedly allocating new objects, and for an N token response, you parse the first token N times! All of the "industry standard" approaches here are quadratic, which is going to scale quite poorly as LLMs generate larger and larger responses to meet application needs, and users want low latency outputs.

One of the most useful features of this approach is filtering LLM tool calls on the server and passing through a subset of the parse events to the client. This makes it relatively easy to put moderation, metadata capture, and other requirements in a single tool call, while still providing low latency streaming UI. It also avoids the problem with many moderation APIs where for cost or speed reasons, one might delegate to a smaller, cheaper model to generate output in a side-channel of the normal output stream. This not only doesn't scale, but it also means the more powerful model is unaware of these requirements, or you end up with a "flash of unapproved content" due to moderation delays, etc.

I found that it was extremely helpful to work at the level of parse events, but recognize that building partial values is also important, so I'm working on something similar in Rust[3], but taking a more holistic view and building more of an "AI SDK" akin to Vercel's, but written in Rust.

[1] https://github.com/aaronfriel/fn-stream

[2] https://github.com/vercel/ai/pull/1883

[3] https://github.com/aaronfriel/jsonmodem

(These are my own opinions, not those of my employer, etc. etc.)

I wrote something similar that can also produce JSON incrementally from other streaming data sources. It combines a streaming JSON parser with streaming strings and a streaming regex engine.

Concretely, it means I can call an LLM, wrap its output stream in a streaming string, and treat it like a regular string. No need for print loops, it’s all handled behind the scenes. I can chain transformations (joining strings, splitting them with regexes, capturing substrings, etc.) and serialize the results into JSON progressively, building lazy sequences or maps on the fly.

The benefit is that I can start processing and emitting structured data immediately, without waiting for the LLM’s full response. Filtered output can be shown to users as it arrives, with near-zero added latency (aside from regex lookaheads).

I did something like this for Python [1]. The application I worked on at the time had a feature allowing users to import and export their data as a JSON document, and users often had enough data to make this cumbersome, especially with serialization and deserialization overhead. My implementation can also generate JSON documents as they stream out, from Python generators. The incremental JSON parsing was a little difficult to use, but incremental generation was an immediate win. We generated JSON documents from database results row-by-row and streamed the output to the web server, never producing the entire document in memory.

[1] https://github.com/chrchr/flojay

You could also use JSON Merge Patch (RFC 7396) for a similar use case.

(The downside of JSON Merge Patch is it doesn't support concatenating string values, so you must send a value like `{"msg": "Hello World"}` as one message, you can't join `{"msg": "Hello"}` with `{"msg": " World")`.)

[1] https://github.com/pierreinglebert/json-merge-patch

I recently also wrote a streaming JSON parser in PHP. In case anyone is interested, I would love to get your feedback. It’s designed to work independently or with the rest of our system.

https://github.com/Qbix/Platform/blob/main/platform/classes/...

I couldn’t find a library like this in PHP, but realized for my use case I could easily hack something together. Algorithm is simply:

- trim off all trailing delimiters: },"

- then add on a fixed suffix: "]}

- then try parsing as a standard json. Ignore results if fails to parse.

This works since the schema I’m parsing had a fairly simple structure where everything of interest was at a specific depth in the hierarchy and values were all strings.

This is awesome work! I had Claude port this to Python to consume LLM output in parallel for massive latency hiding.

Install with `uv install jsonriver`

https://github.com/chrisschnabl/jsonriver-py

Maybe I'm wrong but it seems like you would only want to parse partial values for objects and arrays, but not strings or numbers. Objects and arrays can be unbounded so it makes sense to process what you can, when you can, whereas a string or number usually is not.

Awesome. You know what would be EVEN COOLER?

Given a schema and a JSON message prefix, parse the complete message but substitute missing field values with Promise objects. Likewise, represent lists as lazy sequences. Add a pubsub system.

Thanks for sharing!

Roughly how does it compare with https://github.com/promplate/partial-json-parser-js ?

> If you gave this to jsonriver one byte at a time it would yield this sequence of values:

Does it create a new value each time, or just mutate the existing one and keep yielding it?

This looks really nice. I think I could find a use for this.

The title made me think of Star Trek DS9 and Nog talking about The Great Material Continuum.

“Nog: The river will provide”

Nice. You should pair it with Immer + React for a nice UI demo. This will hype hard if the value is properly demonstrated.

Interesting that it streams a string object value prior to being complete, but not the object key.

Finally, a way to make JSON as fluid as our UI needs to be.

So SAX, but for json?

Plus ça change, et plus c'est la même chose.

Noteworthy: Contributions by Claude

I can't imagine a usecase. Ok, you receive incremental updates, which could be useful, but how to find out that json object is actually received in full already?