ES version is available. Content is displayed in original English for accuracy.
But building AI agents that can generate visualizations reliably can be very tricky:
- simple chart specs can be reliable, but generated charts are often of low quality due to reliance on system defaults; - complex chart specs with explicit details can produce good-looking charts, but they are verbose and agents can struggle with reliability
We figured out it is a limitation on the language issue (not just AI capability thing) -- current visualization languages are a bit too low-level for AI agents, requiring them to explicitly make visual decisions that are supposed to be handled by a good compiler. Flint is a visualization intermediate language to address this issue, allow AI agents to solve this last-mile human-agent interaction problem. It provides a simple semantic-type based specification, and contains a layout optimization engine that can produce good-looking charts (filled with derived low-level details) from simple high-level specs. The result is also very human understandable and adaptable. Flint powers data formulator for generating visualizations (another open source project from microsoft https://data-formulator.ai/).
Flint is available open source, and we built a MCP server that you can directly plug flint in your favorite agent app to play with data.

Discussion (59 Comments)Read Original on HackerNews
I don't want to use an agent at all, but i wouldn't mind generating some charts with an easy-to-generate markup language...
The only reason to use this instead of existing, mature ones designed for humans is if you are an AI agent.
A deterministic layer like a compiler or generator of code with some kind of IR that the LLM generates and feeds it with.
I feel we will be seeing this more and more in the near future.
Ok, Microsoft is conflating two different things here: LLMs don't really care about code being low level and verbose, they can read things like Assembly and SPIR-V just fine: visualization is the real issue in that LLMs have no natural understanding of spatial composition through visual comparison because they literally "see" things differently than humans, so the way to get around that is provide them with "visualization" in code form that they can easily reason about and understand, so basically anything that's not deeply nested and has hidden states that they have to reason about.
Also, Flint being stringly typed in JSON is a decision that I don't think I agree with. Looking at the actual spec, this could have just been a normal, human usable TypeScript library, and it would have been 100x better. Using their own example (excuse the formatting):
type SemanticType = "Category" | "YearMonth" | "Profit";
type ChartType = "Heatmap" | "BarChart" | "LineChart" | "ScatterPlot"; // extend as needed
interface ChartEncodings { x: string; y: string; color?: string; size?: string; tooltip?: string; }
interface ChartProperties { colorScheme: string; [key: string]: unknown; // allow other optional properties }
interface ChartSpec { chartType: ChartType; encodings: ChartEncodings; chartProperties: ChartProperties; }
type SemanticTypes = Record<string, SemanticType>;
interface ChartConfig<TData = Record<string, unknown>> { data: TData; semantic_types: SemanticTypes; chart_spec: ChartSpec; }
// The actual typed object literal: const chartConfig: ChartConfig = { data: {}, // replace with your actual data shape/type semantic_types: { game: "Category", period: "YearMonth", newUsers: "Profit", }, chart_spec: { chartType: "Heatmap", encodings: { x: "period", y: "game", color: "newUsers", }, chartProperties: { colorScheme: "redblue", }, }, };
EDIT:
Went and actually looked at the source instead of just eyeballing it from the docs, and it was a lot more complete and sophisticated than my assumed mockup already.
Core complaint (string-keyed JSON vs. a real generic authoring surface) still stands, but the specific types I posted aren't what Flint has. My bad.
For other parts, it's quite common in visualization and diagram etc libraries to have json, since they are easily portable in different rendering contexts.
I mean, it would be great if you guys would have like a "TSON" that is basically "JSON with type declaration and comments" from TS, which I think would just solve a lot of problem straight up. JSON itself is just too restrictive and comes with its own bracket verbosity tax.
N of only a few of us working on an analytics agent, I don't think we've been finding this to be the case. We've been impressed with just how good LLMs (even smaller open weight models) are at using Python and R for visualization. Often any shortcomings go away if we iterate a bit to about ambiguity. Are there any threads of research that could better support this claim or highlight where issues might be?
Or at least, maybe that's the idea?
IME, Claude and ChatGPT do just fine generating ggplot models, but extensive customization can get a bit hairy.
My understanding is that Vega was already an expressive DSL for visualizations and its probably already well spread through LLM training data.
Flint is a higher-level abstraction, with simpler much shorter spec, and the compiler derives low-level decisions so that charts are looking good.
So: flint lets agent write short program that achieving good looking charts that had to be done with lengthy program in the past.
I don't quite get what the goal of this is other than abstracting away a little bit of the complexity at the expense of flexibility. To me, the promise of LLMs is the opposite, I can get flexibility and customisation without the cost of complexity.
The intention here is that Flint is a simpler abstraction to get basic setups right and any followup edits can be done on top of the first compiled outputs (thus not limiting expressiveness). It also makes it easier for user to manipulate (like swapping axes, click to change something, which can be very hard if LLM generates a complex chart spec upfront).
But for many basic stuff your intuition is completely right.
I'm not sure if Flint is the right tool for me. I'd like to have a tool that expresses code in visual form for me. For example, right now I need to reverse engineer some code for debugging purposes.
I already found out there are three tasks:
Visually it's easily expressed: 3 bubbles lined up with 2 connections between the neighboring ones.Which ML tools suited best for that?
But when building it in a tool that serve end users, we are starting to see that a 80% success rate in generating good looking charts can become a big issue. We experienced this when building some data analysis system. So the reliability, expressiveness, and costs (in terms of time and tokens) are hard to achieve all together with directly generating matplotlib, vega-lite etc.
So we essentially designed the langauge as a trade-off across the three, by moving some decisions to the compiler to reduce generation cost while maintain good expressivenss.
Isnt graphviz there for the same reason?
Edit: I see it is using JSON as the declaration language, I am OK with llms being "good at json" but a syntax also consumable by humans it is not!
Btw, Flint is intentionally designed to allow agent skip low-level params like scale, axe, zero, step size etc (which are extremely crucial for "GOOD-looking") and they are dynamically optimized by the compiler. So AI agents can have a easier time.
Plant, Mermaid, Graphviz are all declarative textual representations designed for human authoring, JSON is made for tools. Its not a criticism just a statement that if interop across agent and human was intended this is not the simplest option.
MCP setup: https://microsoft.github.io/flint-chart/#/mcp
and then that spec would be rendered either to a Bubble TUI via NTCharts or to HTML/SVG via ECharts. That Echarts HTML could be naturally served by a Golang http service.
But Flint goes much deeper with semantic layers and settings optimizations. Perhaps a NTChart, or whatever terminal chart, could be a rendering target? I'll add it to the list to explore...
https://github.com/NimbleMarkets/ntcharts/blob/spec/spec/REA...
Also, I find NTChart very fun, maybe we should add NT chart to the list of compilation backend for Flint so it works in the library. Putting a reminder here: https://github.com/microsoft/flint-chart/issues/45
I’ve been building https://smalldocs.org for this exact reason. It’s an office suite for AI agents - but my main use case is giving a cli based LLM the canvas to express itself - charts, mermaid diagrams, etc. I’ve extended it a bit further to be a format for all types of work so the agent can embed slides and spreadsheets in a document.
Sample document: https://smalldocs.org/blogs/what-is-a-smalldoc
Source: https://github.com/espressoplease/smalldocs
I'm terrible at diagrams, so I gave GPT very generic descriptions of one of our project, to convert in to that mermaid style, then for Lucid I pasted it in there, and had a visualization of what I needed. Worked out nicely.
They can do a lot of cool things! Mermaid gallery here: https://smalldocs.org/s/xZrc-lNW1kbXpoIuU3l_ky#k=-0ehGe2B-hR...
Functions extremely well and the result is a very clear (and consitent) human-readable "output layer." Cool idea, fun to see people converging on similar concepts in the space.
I find that besides training better models, designing new language for agents is also a super viable paths to improve their performance!
but enterprise
In some composite chart examples, the good-looking echart spec is like 5x longer than the simple Flint one!
Agents, npm, typescript, MCP. All buzzwords are there. Will anyone look at the slop charts? Of course not, the tokens are the goal.
MSFT stock is at 2024 levels. Maybe someone should produce a flint chart and present the agentic work to Nadella. No one buys this AI slop any more.
Make something people want.