Welcome back to another edition of our monthly Positron updates! Each month we share highlights from our latest release and useful resources. Last release we told you about a redesigned welcome page, a first version of Import Data, new Posit Assistant features, expanded Data Connections, package vulnerability scanning, and a more responsive Console. This milestone brings a new ability for Posit Assistant to drive Positron itself, a built-in MCP server so you can bring your own coding agent, Data Connections on by default, faster language features for Quarto, and smoother interpreter and project setup.

Posit Assistant can drive Positron

Posit Assistant can now operate Positron itself, not just the R or Python session inside it. You can ask it to run a Shiny app, open a Parquet file in the Data Explorer, set up a Python environment, browse your data connections, or deploy to Posit Connect. Posit Assistant uses the same source of truth as the Positron UI, so it works with your real interpreters, packages, and connections rather than guessing at them.

In practice, this means Posit Assistant can run and preview Dash, FastAPI, Flask, Gradio, marimo, Streamlit, and Shiny apps with the same app commands you use, see which apps are running along with their status and URL, and stop them. It can show HTML files and URLs in the Viewer pane, and it can read, take screenshots of, and interact with data apps and other content there, which makes it much more useful for checking its own work on an app. It can read your configured and detected Data Connections, browse a connection’s schema, and give you the code to open it. It can also tell which interpreter a project uses, explain why an installed interpreter isn’t showing up, and make that interpreter available, and it knows the commands for deploying to Connect through Posit Publisher.

To try this preview feature, enable the assistant.previewFeatures setting, and tell us what you think in the GitHub discussion.

Bring your own coding agent

If you already have a favorite coding agent other than Posit Assistant, this release lets it work alongside you in Positron. A new built-in Model Context Protocol (MCP) server lets coding agents such as Claude Code, Codex, and Gemini CLI work in your live Python and R sessions. Our goal is to give these agents a set of tools comparable to what Posit Assistant has: agents can list your sessions, run code in the Console, inspect objects without cluttering your history, read recent session history, interrupt a long-running computation, look at the current plot, and search for and run Positron commands. These other agents may not have the data science expertise that Posit Assistant has, but they can still help you write code and explore data.

You stay in the loop on what an agent does. Code that an agent runs shows up in the Console with the agent’s name next to it, so you can always tell agent code from your own. Console tabs also now show a dot when code runs in a console you aren’t looking at, whether that code came from Posit Assistant or an external agent.

To try this experimental feature, enable the ai.mcp.enabled setting. Positron configures Claude Code for you automatically, and the Positron MCP: Add to Coding Agent command sets up the Codex and Gemini CLIs. For any other MCP client, Copy Connection Details gives you a URL and token to connect with.

Data Connections on by default

Data Connections is now the default way to work with databases and data warehouses in Positron. Data Connections connects to databases directly, without needing a Python or R session, and it replaces both the older Connections pane and the Catalog Explorer. If you need the older Connections pane, set dataConnections.enabled to false and reload the window.

This release also extends what you can connect to and how you sign in. Amazon Redshift connections can sign in with AWS Identity and Access Management (IAM), for both Redshift Serverless and provisioned clusters, using credentials from the standard AWS credential provider chain. On Posit Workbench, Snowflake and Databricks connections can use the session’s managed credentials with no credential input at all. Snowflake users get the most new features this month; the Data Connections pane shows Snowflake semantic views with their dimensions, facts, metrics, and relationships, you can browse Snowflake stages as folders and files, and connections in connections.toml show up automatically as detected connections.

Smaller improvements round out the experience. When you open a DuckDB or SQLite file from the Explorer, Positron offers to create a data connection for it. Connect With can now generate ggsql code for SQLite, DuckDB, ODBC, PostgreSQL, and Redshift connections.

The Add Data Connection dialog lists each driver with a short description, and Positron now tells you when a connection fails to open or expand. For slow tables and views, the Data Explorer now shows a progress indicator and placeholders while data arrives, and it loads column summaries in two passes so that missing-value counts appear quickly and sparklines follow later.

Faster language features for Quarto

Positron now provides completions, hover, diagnostics, outline, formatting, and other language features natively for R and Python cells in Quarto and R Markdown documents. Before, the Quarto extension wrote temporary virtual documents to disk to power these features. Positron now uses a virtual notebook in memory instead, which is faster (especially in remote sessions) and solves a whole category of bugs, such as Python imports that did not resolve correctly. It also means Go to Definition, Find References, and Rename work across R cells, finding names that earlier cells define.

The document outline for Quarto and R Markdown documents with many code chunks now updates quickly, and it no longer shows duplicate groups or symbols. Inline output, including HTML tables and the inline Data Explorer, now scales with your editor font size. If you run into problems with the new language features and need to go back to the old behavior, set quarto.embeddedLanguageFeatures.native to false.

Interpreter project setup

Positron is better at setting up the right Python environment for your project. When a workspace has a uv.lock or pixi.lock file but no environment yet, Positron offers to create it with uv sync or pixi install, which is especially handy right after you clone a project. If uv is missing, the New Folder flow can install it for you, and package installs and the Packages pane work with uv right away. When Positron finds a new Python environment in a workspace, it now offers to start a console session in it, and system Python installations now show last in the interpreter picker, below uv, venv, conda, and pyenv environments.

For more control over interpreters in both Python and R, interpreter path settings now support ${workspaceFolder}, so a project can point to an interpreter that lives inside it. The new interpreters.definitions setting lets you define additional R and Python interpreters, each with its own label, environment variables, and startup script. This is useful for workflows like loading environment modules or configuring rJava before a session starts. Set interpreters.discovery to definitionsOnly to show only the interpreters you have defined for a language.

What’s coming next

• Meet Posit at Snowflake World Tour NYC on October 15. Stop by our booth, schedule time with a Posit expert, or catch our theater session on AI-powered data science where your data lives, inside Snowflake.
• Join us at Data Outpost on November 4-5 in San Francisco. I’ll be speaking on “The SQL Agent Harness We Didn’t Mean to Build,” about how the SQL tools we built in Positron for people turned out to already be a harness for AI assistants.

Last month, we gave a posit::conf(2026) keynote on building correct, transparent, and reproducible data agents. The talk brings together many themes from this newsletter, so this week we’re sharing an annotated walkthrough. You can also see the full slide deck here.

Correctness vs. convenience

(Simon)

This morning, your VP pulled up a coding agent and asked a reasonable question:

How is traffic trending for our site?

The agent looked around the workspace, found a table that seemed relevant, wrote a SQL query against it, and returned a polished chart. Daily visits, it said, were down 64% over the last 90 days.

The variation looks a little tight, and the bend is a little sharp. Is there any possible reason we might see that bend other than a genuine change in site traffic?

It turns out that a version of Chrome released 45 days earlier fired the page-view event differently, so it was not aggregated correctly in the database. As people updated Chrome, it appeared that site visits were trending down. This was not even the table the data science team used to track site traffic.

But you are not there! Your VP sees this chart and takes it to the rest of the leadership team. They decide to restructure the organization and spend millions of dollars to reverse the trend.

A month later, somebody asks, “Could you recreate that chart? Let’s see how things are trending now.” The agent cannot find it. Many coding agents delete conversation histories after 30 days, and they often do data analysis in temporary files so they do not clutter the project directory. The source code for the analysis is long gone.

The coding agent offers to recreate the chart. It looks around the workspace, finds a table, and runs some SQL. The number was going up the whole time.

This is a fictional story, but we’e seen situations like this internally at Posit and at many of the organizations we work with. It is becoming normal to vibe-analyze data: ask an agent a question, receive a plausible answer, and move on without understanding how it was produced.

The tension between correctness and convenience is not new

Posit CTO Joe Cheng summarized the current moment this way:

AI has broadened the group of people who can convincingly do data analysis, but many may have no idea what data scientists go through to produce a trustworthy analysis.

Joe said this a month before the keynote, but he could have said it seven years ago. In 2019, the beverage company Conviviality disclosed that a spreadsheet arithmetic error had contributed £5.2 million to a £14 million forecasting mistake. When that “innocent spreadsheeting mistake” came to light, the business sank.

Or Joe could have said it 20 years ago, when a sign error in closed-source protein-modeling software sent a research field down the wrong path. A lab published landmark study after landmark study while other labs struggled to reproduce its findings. Only after another lab found a protein structure that was nearly a mirror image of one that the first lab had published did the first lab audit its source code. They found a negative sign where it did not belong and retracted five papers.

There were threats to correctness in data analysis long before AI. Posit’s stance has never been that people must sacrifice convenience for correctness. While preparing the talk, I found this quote in a blog post old enough to refer to itself as a “weblog”:

JJ was saying that a tool could be both correct and convenient. (By convenient, we do not mean 7-Eleven. We mean straightforward and intuitive.😜)

Can we take the same stance toward AI? Is it possible to make data analysis agents that are both correct and convenient for end users?

Why build AI tools at all?

(Sara)

This pull between correctness and convenience has been on our minds throughout the new, exciting age of LLMs. In 2025 and early 2026, AI started to feel really, really convenient for data analysis. That made us worry about correctness.

In August 2025, when we released Databot, our state-of-the-art exploratory data analysis agent, we did so under what was essentially a warning label. The release post was one of the first things I worked on after joining the AI team, and it gave me the chance to do something I think I am particularly good at: be pessimistic.

We were all really excited about Databot. It felt like flying through your data, gathering insights faster than you thought would ever be possible. It was precisely that convenience that worried us. People might trust it because they were having so much fun, even when it was wrong. So we wrote an entire article detailing basically every way we thought Databot might fail.

If you were at posit::conf(2025), you might remember Joe Cheng describing it as both the most exciting and the most dangerous software he had worked on in his 30-year career.

That raises a fair question, one we contemplate ourselves on a weekly basis: If AI causes all this chaos and makes all these mistakes, why are we doing this? Why involve ourselves at all?

There are many answers. We’ll focus on one.

Curiosity and exploration

Before joining the AI team, I taught data science. One thing that always struck me was what brought people with very different backgrounds into the field. The thing that united many of them was a sense of curiosity. Maybe they were curious about a scientific field, computers, statistics, or how R and Python themselves work. Data rewards that curiosity: There are datasets on almost everything.

But even as an undergraduate who knew some statistics and a little R, I often felt like I was looking at an ocean from above. The dataset was right in front of me, and I knew there was so much going on, but I just couldn’t see it.

As a graduate student, I learned the tidyverse and more about data science. As I got better at using those tools, I finally felt like I had what I needed to ask and answer questions about my data.

The right tools let us see below the surface. They enable that curiosity.

That same curiosity motivates our work now. We are curious about how these models work, how we can build things around them to make them work better, and, maybe most importantly, how we can turn them into tools that open new worlds of analysis. For us, AI is a continuation of work we have always done: finding new ways to explore data.

The models got better

I also wanted to spend some time on what has changed since August 2025, when we released that warning-label image. One major thing happened: The models got better.

About a year before the keynote, we ran an experiment to understand how well models interpret plots. We showed them plots based on a transformed diamonds dataset and asked them to describe what they saw.

The models gave answers like, “There is a strong positive relationship between carat and price.” That sounds plausible until you look at the plot, which shows the opposite. Behind the scenes, we had manipulated the data to reverse a canonical relationship, so larger diamonds were, unintuitively, less expensive.

We wanted to know whether the models could see what was in front of them and interpret plots that contradicted their expectations. A year ago, the answer was largely no.

In November 2025, even the best models of the time were abysmal at this task. They said what they expected to see instead of paying attention to what was actually happening in the plot.

We tried all sorts of interventions to improve the bluffbench scores, and absolutely nothing worked. Then we waited six months.

By May 2026, the major AI companies were releasing models that were noticeably better at the task. By September, the numbers had jumped dramatically: The models could interpret these counterintuitive plots.

The models got better. That does not mean data science is solved.

Models still make mistakes

After that experiment, we asked another question: Can models identify data-quality issues in visualizations? In bluffbench2, we again showed them plots and asked what they saw.

If you look carefully, there’s a suspiciously straight line through the cloud of points. If we really cared about this data, that’s something to investigate: Did something weird happen when the data was created? We wanted to know whether models would do the same.

It turns out they don’t really. Even the leading models were not very good at this task. They struggle to notice subtle data-quality issues.

But there’s another problem. Data analysis is not just you, plus a good model, plus your data. It also depends on context that is not necessarily in your CSV file, warehouse table, or JSON file.

All data is constructed. It all has context. That context might live in a data dictionary, a Markdown file, or a semantic view. Maybe it just lives in the head of a coworker who has been at the organization for 15 years, knows absolutely everything, and has never written a single thing down.

That context is not inherently inaccessible to models. We could give it to them. But it has to be in the right format, it has to be curated, and it has to itself be correct.

So the models have gotten better, but data analysis with AI is not solved. The question then becomes: How do we make AI-involved analysis correct?

How do we make AI for data analysis trustworthy?

(Sara)

Here’s a common thought about AI: It’s untrustworthy, but useful. To make it trustworthy, we need to add in a person.

Here’s a simple version of what that might look like. The agent or model does stuff, probably writes a bunch of code. A person then looks at that code and verifies, approves, or redirects as needed. That feeds back into the agent. This intuitively can feel really appealing. The agent gets to do what it’s good at, the person does what they’re good at, and there’s an expert there to keep everything from going off the rails.

And so it’s kind of tempting to apply this pattern everywhere. Any time the agent might make a mistake, we have a person there to clean it up and make sure nothing bad happens. We call this relatively simplistic view the slap a human on it approach.

In this approach, you don’t really think very much about what the person will actually do. How will they apply their expertise? What is the purpose of their involvement? How will we keep them from technically being in the loop but without actually applying their expertise?

I’m being a bit facetious, but this isn’t that far from how we wrote risk mitigation with Databot a year earlier. We said that Databot and LLMs were not at a point where users could abdicate responsibility. You still needed all of your data skills to catch errors, interpret results in context, and avoid being misled by confident but incorrect claims.

And all of that is still true! But we never said how this was supposed to happen. We assumed that if an expert was there, they would catch the mistakes.

But just adding a person doesn’t really guarantee a better result. Human–AI combinations do not reliably outperform the better of the human or AI working alone.

People also get tired of approving requests, and then they stop reading them. Think about the last time your coding agent showed you a command to approve. Did you carefully read it, or did you hit approve as quickly as possible and feel annoyed that it even bothered to ask?

Human decision-making is not fixed. How much you trust the system, how much you trust the people who built it, the timing of the information, your cognitive load, and how much your coworkers trust the agent can all influence the decision you make. Even if you are an expert who makes the right decision outside an interaction with an agent, adding the agent might change that decision.

So how do we design the entire system to produce correct work? How do we build trust and correctness into the agent itself, and design interactions with users that actually support their expertise?

That maps onto two principles we returned to throughout the talk:

1. Help the agent be correct.
2. Make it less bad when the agent is wrong.

Posit Assistant

(Simon)

Posit Assistant is Posit’s coding and data science agent. It is available in RStudio, Positron, and the terminal. When it is running inside an IDE, it works in the same R or Python session you’re using. You can use whatever model you want from whatever provider you have access to.

For the demo, I returned to one of my first experiences of seeing below the surface with data science. In an introductory statistics class, I analyzed flight data to understand whether I could book tickets that were less likely to be delayed. I used ggplot2 and dplyr, pushing data frames around and visualizing them.

I asked Posit Assistant to find an R package with Houston flight data and retrieve data from the previous year. It found anyflights, read its documentation, and generated the correct call.

Next, I asked it to grab data from the same time the previous year. The agent read the package documentation and so was able to write the code with the right arguments the first time.

We have seen that many coding agents have a superficial relationship with data quality, and we wondered whether we could do better. Posit Assistant proactively recommends cleaning the data and has a dedicated data-cleaning mode. It runs scratch code to look for missing values, outliers, and similar issues. Once it finds them, it surfaces questions to the user in an interactive dialog.

In the demo, the agent found missing values in a relevant column, then found some flights with departure delays of multiple days. I chose to keep both: Those outliers represented what really happened.

At the end of cleaning mode, the agent wrote a persistent file in the project directory to ingest and clean the data. When someone revisits the analysis, they can reproduce those steps.

Posit Assistant also shows plots inside the conversation. It is nice to be able to see them, but we also think it is necessary: Our evaluations show that agents cannot reliably interpret plots. When one carrier appeared unusually delayed, it checked the sample size and found only 32 flights, suggesting that we should not read too much into the pattern.

Once the analysis was complete, I asked the agent to turn it into a persistent Quarto report.

I have one more “little zoomy zoom”: The entire conversation cost five cents.

Part of our mission at Posit is to provide tools to people regardless of their economic means.

Posit Assistant is designed to be token-efficient and can use capable, inexpensive open-weight models such as GLM 5.3 Flash through Posit AI Pass. We spend a lot of time watching the open-weights model space for models on the Pareto frontier: capable of data analysis and orders of magnitude cheaper than frontier models.

Returning to the two principles, part of helping the agent be correct is, quite literally, asking nicely. We say, “Pretty please pay attention to data cleanliness. Pretty please be open to uncertainty when carrying out data analysis.”

We also design interactions around the user’s expertise: Cleaning mode asks about the data-generating process, plots remain visible, and the user shares the agent’s R or Python session.

Posit Assistant assumes that the user is a data scientist, analyst, or someone else who writes code and wants to work in an IDE, close to that code. But what if the user is not a data scientist? How do we help them get correct answers, too?

Introducing commons

(Sara)

Let’s return to our vibe-analyzing VP. I think it’s easy to say, “They just shouldn’t be doing this kind of analysis.” But let’s be on their side for a moment. They had a question, they wanted it answered, and so they’re going to reach for a tool that gives them an answer.

The problem is that they reached for the wrong tool and it gave them the wrong answer.

The data team already has vetted, maintained code that computes site traffic correctly. We call this trusted code. Trusted code might live in a package, Shiny app, Quarto document, report, or dashboard.

Instead of having the model write bespoke code from scratch every time someone asks a question, what if it could use that trusted code?

This is one of the core ideas behind a new open-source package that we’re excited to share with you all today. It’s called commons.

commons helps data scientists build trustworthy data agents for their collaborators. It is an open-source R and Python package built on the ellmer, chatlas, and shinychat stack.

(Simon)

Here is how the VP’s analysis works with a commons agent. When the VP asks, “How is traffic trending for our site?” the first thing the agent does is search for a trusted calculation. If it finds one that answers the question, it invokes that calculation directly.

The calculation returns a plot and a green shield indicating that the answer came from a trusted calculation. commons adds that marker deterministically; the agent itself does not control it.

What makes coding agents so convenient is that they are everything tools. They will write their own R, Python, or SQL code to answer a question. Trusted calculations cannot cover every question, so commons has a fallback path.

When no trusted calculation exists, the agent can use context authored by the data team. commons verifies any citation against that context before displaying its blue citation marker.

If there is neither a trusted calculation nor supporting context to cite, the agent can write code, but commons displays a yellow warning so the user knows to treat the answer cautiously.

Here’s a diagram of the various ways the agent can come up with answers and how the trust relationships are mapped.

So far, we’ve been talking about the VP’s experience with the commons agent. What is it like to work with commons as a data scientist? There are three main loops: build, deploy, and improve.

With data collection enabled, data scientists can review cases where the agent had to write its own code. If those cases share a pattern, they can add trusted code that moves future responses up the trust ladder, making the agent more correct over time.

Returning to our two principles, commons helps the agent be correct by letting it directly use trusted code. It makes mistakes less bad by telling the user when the agent did not directly use trusted code, and by moving responses up the trust ladder over time.

Other use-cases for commons

(Sara)

The VP example is business intelligence, but commons is not only for BI or vice presidents. It’s for any data person who wants to build an agent that gives correct answers to their collaborators.

We’ve made a sample clinical trials agent to highlight this functionality. Its data is simulated. The trusted code is a TLG catalog: code written and trusted by data people to work on this type of data and produce correct answers. You can ask it questions such as, “Plot Kaplan–Meier curves.”

It is still very bad to be wrong

(Sara)

It’s now so easy to ask any kind of agent a question about your data and get a wrong answer that looks completely plausible.

The problem is not just that the answer is wrong, and it’s also not just that you might be unable to reproduce it or have no idea how it arrived at the answer.

These are bad enough, but there’s another problem. If we become accustomed to, say, 15% of our numbers being wrong, we might lose trust in analysis itself and its ability to tell us things about the world, or become used to numbers just being a bit wrong all the time.

But correctness matters, especially in particular industries, and Posit has always built tools to support correct analysis. We do not think that standard should change just because AI is here.

But AI has expanded who can get answers from data. Now anyone can ask a question about their data. That changes how we need to think about the infrastructure for building correct, transparent, and reproducible agents.

With commons, a data team establishes which code and calculations it trusts and provides the organizational context the agent needs. The team can give that agent to someone who might otherwise perform a vibe analysis. That person keeps the convenience of asking questions in natural language, while the answers are grounded in the data team’s trusted work and can be traced and reproduced.

That is where data scientists and analysts come in. This infrastructure must be built by people who understand the data, calculations, and organization. commons helps them build it and make it available to others.

Looking forward

Looking forward, we have been experimenting with something called Canvas.

Canvas is a next-generation agent experience. It assumes that you might not always want to look at the code. That does not mean the code is not there when you need it, but you might not always want to work in an IDE.

This frees up real estate for expansive, new agent-collaboration interfaces. Canvas feels a little aggressively agentic to us, in the way Databot felt a year ago.

Recent past newsletters

• New releases from ellmer, shinychat, and commons
• You probably don’t want to fine-tune

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I came into the internship as a computational biologist. In my PhD research, I work with single-cell sequencing, genomics, and multi-omics data, and with the public databases and identifiers that come with them.

Across different projects, I kept running into the same problems:

• Biological datasets are getting enormous.
• The information needed to interpret them is scattered across many databases.
• Identifiers are surprisingly inconsistent.
• The same API and data-access logic gets rewritten again and again.
• Even when excellent public datasets exist, there can still be a huge gap between the data being available and a researcher being able to use it comfortably.

Those gaps became the theme of my summer.

How do we make increasingly large and complicated biological datasets genuinely usable for researchers?

Making enormous biological datasets explorable

One of the projects I am most excited about is Tahoe Explorer (source).

Tahoe-100M contains more than 100 million single-cell perturbation profiles. That scale is scientifically exciting, but it creates a practical problem: you cannot treat 100 million observations like a normal dataframe inside an interactive application.

I wanted researchers to be able to answer basic questions before committing to a large analysis. Which cell lines are represented? Which drugs were tested? How much coverage exists for the combination I care about? What do we know about the genomic background of those cell lines? And once I find what I need, how do I retrieve only that part of the dataset?

Tahoe Explorer lets researchers move through drugs, cell lines, samples, genes, coverage, and cell-level metadata without first downloading and loading the entire resource. For the larger data, queries are pushed down to DuckDB, which returns only the rows or summaries the current view needs.

I integrated additional information from resources such as DepMap and Cellosaurus, including variant information for the cell lines. That makes it possible to think about perturbation response alongside the genomic context of each cell line.

The Subset builder tab then turns exploration into something reproducible. Users can define the experiment they care about and generate R or Python code for retrieving that subset later.

Another feature of the app is the built-in chat assistant. It can help researchers reason about Tahoe, identify useful filters, interact with supported parts of the app, and finally build those reproducible subset recipes.

What I like most is that the assistant lives inside the app. It can use tools over the data and help operate the workflow. To me, that is a much more interesting direction for AI in scientific software.

The Tahoe app solved one type of scaling problem: accessing and understanding a dataset that is much larger than the application itself. One layer later, the same issue came back, with the visualization itself as the bottleneck.

plotomics and Plotomics Live (source) came out of that bottleneck.

plotomics is a visualization library for computational biology with a shared TypeScript rendering core and wrappers for R and Python. Its 17 components cover embeddings, heatmaps, volcano plots, networks, oncoplots, genomic views, spatial data, survival analysis, and other common biological visualizations. Of those, 15 ship in R, Python, and JavaScript; the two genome browsers are JavaScript and Python only.

For large visualizations, the browser can draw with WebGL or Canvas. Numeric data can also move to the frontend as binary buffers instead of being serialized into huge JSON payloads.

The visualization logic exists once. The R, Python, and JavaScript wrappers all talk to the same rendering engine.

I built Plotomics Live to explore how far that model could go inside Shiny.

The app contains 26 biological visualizations, built from those 17 components with several of them used more than once, across single-cell data, spatial transcriptomics, genomics, networks, perturbation data, and other common workflows. Every one of them renders two ways, and an engine toggle puts the GPU-accelerated React component beside the server-rendered ggplot2 image so you can compare them directly.

One page, for example, interactively renders more than half a million real single-cell observations in the browser.

Plotomics Live is where I worked out what Shiny can look like when R remains the analytical engine and the browser does more of the work it is good at.

Connecting biological information that lives everywhere

Scale was only one recurring problem. The other was fragmentation.

A lot of computational biology does not involve one database or one algorithm. It involves assembling an answer from many different resources.

Variant interpretation is a good example. If I am reviewing a variant, I may want information from ClinVar, gnomAD, Ensembl, UniProt, AlphaFold, Open Targets, dbNSFP, ProtVar, and several other sources.

Variant Reviewer (source) brings those sources together in one reactive workflow.

A researcher can start from a gene, a variant, or both, and the app combines clinical significance, population frequency, prediction scores, gene constraint, protein domains and 3D protein structure.

But building Variant Reviewer surfaced another problem. Every external biological service behaves differently: its own identifiers, its own failure modes, its own rate limits. One might return a clean “not found” response while another throws an error. And one slow or unavailable service should not break the entire application.

Once I started solving those problems in one app, it became apparent that I should not keep solving them separately.

Building the infrastructure once

Five packages grew out of problems that kept appearing across the applications.

biobouncer

biobouncer handles biological identifier validation. Gene symbols change. Ontology identifiers have different namespaces. Variants can be syntactically valid but biologically meaningless. Public databases each have their own identifier conventions.

biobouncer provides a common layer for validating these inputs using pattern checks, cached resources, remote validation, and existence checks. It also has implementations across R, Python, and TypeScript backed by the same conformance data.

If the same identifier moves through an R pipeline, a Python analysis, and a web interface, I want “valid” to mean the same thing everywhere.

biohttp

biohttp handles the repetitive infrastructure around external APIs. Retries, throttling, caching, batching, timeouts, circuit breaking, and structured errors should not need to be rebuilt in every scientific application.

It also makes an important distinction between a service successfully returning no biological result and the service itself failing. That sounds small until an application depends on several external databases at once.

bioclients

bioclients sits on top of that transport layer and provides R clients for 29 biological services. Ensembl, UniProt, gnomAD, Open Targets, ClinVar, AlphaFold, and the rest each get a client that understands the structure and meaning of what they return.

Network requests and response parsing are separated so parsers can be tested against stored responses without requiring a live API.

The separation is intentional:

• biohttp handles how we talk to a service.
• bioclients handles what that biological service returns.

plotomics

plotomics is the exception in this list. I described it above, because it came out of one specific problem before I was thinking about shared infrastructure at all. It belongs here anyway, for the same reason as the rest: the rendering layer only had to be built once.

biocohort

Before APIs and visualization even matter, most omics projects have another problem: keeping the study organized. Subjects, samples, assays, species, and the manual corrections made along the way often end up spread across spreadsheets and scripts.

biocohort provides a validated study representation for keeping those relationships together. The same manifest can describe multiple assay types and organisms, generate pipeline sample sheets, and preserve corrections that would otherwise disappear inside preprocessing code.

This one is particularly close to my own research because these small bookkeeping problems become very expensive once a project grows.

This shift from app-specific code to reusable infrastructure was the biggest change in how I approached the internship. Instead of asking, “How do I make this app work?”, I started asking, “Which part of this problem will the next app also need?”

From retrieving data to synthesizing evidence

Once those pieces became reusable, I could build higher-level research workflows on top of them.

Gene List Builder (source) came from another task I have done manually many times: starting with a disease and assembling a defensible list of associated genes.

The application resolves the disease to an ontology term, queries multiple gene-disease resources, normalizes the results, removes duplicates, and produces a ranked list using transparent and adjustable source weights.

The evidence comes from the underlying databases, the scoring is visible, and the provenance remains available. An optional AI layer can then help with curation after that deterministic evidence-gathering step.

GeneScout (source) takes the same idea further.

It acts as an evidence-review workbench for evaluating candidate genes in a disease context. Its deterministic layer resolves genes, retrieves public evidence, keeps citations attached, scores the evidence, and applies caveats. An optional agent can then synthesize what was retrieved and suggest follow-up directions.

Both apps rest on the design principle I came away from the summer caring about most:

AI can help interpret scientific evidence without needing to be the source of the evidence.

Turning analyses into tools researchers can actually explore

I built several more focused applications around common genomics workflows.

Recount Explorer (source) provides an interactive entry point into recount3, allowing researchers to browse uniformly processed RNA-seq studies, inspect quality metrics, explore expression and PCA, and export data, without writing any code.

DE Explorer (source) makes differential-expression results interactive, moving from a PCA of sample-level structure to a volcano plot, a filterable results table, and a heatmap of the genes you select.

Signature Scoring (source) summarizes expression profiles into pathway-level scores and lets researchers compare those scores across groups before drilling down into the genes driving a pathway.

Drug Perturbation (source) explores connectivity between gene-expression signatures and reference perturbations, including potential mimics and reversers across different cellular contexts. Its reference panel is synthetic, built over the TCGA-BRCA gene space.

Genome Explorer (source) places recurrent variants into an interactive genomic view and lets users move from cohort-level patterns to individual loci.

These five are deliberately narrower than the applications above. An analysis can end in a tool: a thin Shiny layer turns a computational result into something the researcher who understands the biology can actually explore.

Working in open source

I had worked on open-source software before, but working inside Posit’s Open Source group changed how I thought about building research software.

Code review was where a lot of the interesting work happened.

I spent much more time thinking about API design, failure modes, backwards compatibility, and what another developer would experience after I stopped actively working on a project.

That distinction became very clear over the summer:

Making something run once is different from making something other people can depend on.

I got to move well outside the R layer I knew best. Across the summer I worked with R, Python, JavaScript, TypeScript, React, WebGL, Shiny internals, and newer work around shinyreact, which the Shiny team introduced in Introducing shinyreact at the end of September.

Starting the internship at Open Source Work Week in Boston made the whole experience even better. Meeting everyone in person at the beginning made the later remote collaboration feel much more natural.

I am very grateful to Barret Schloerke, Carson Sievert, Joe Cheng, the rest of the Shiny team, and everyone across Posit who provided feedback, answered questions, challenged ideas, or helped me understand another part of the stack.

I learned a lot from being able to build freely while surrounded by people who know these tools from the inside.

Next for these projects

The internship ended, but I do not really think of these projects as finished. They are open source, and many of them solve problems I still encounter in my own research. I hope researchers use them, find things I did not anticipate, and help shape where they go next.

The applications and supporting packages are collected in the Shiny Showcase: Bioinformatics repository, with source code, documentation, examples, and citation information for the individual projects. The live gallery links every app to a running deployment you can open in a browser.

I presented this as a talk in August 2026. The deck covers the same projects in slides, and its source is public.

If you work in computational biology, genomics, or research software, I would genuinely love to hear what is useful and what is missing.

Try an app, use one of the packages, or open an issue if something breaks or could work better. Pull requests are welcome. Or tell me about the research-software problem you keep solving manually.

A lot of this work started exactly that way. I noticed recurring problems in my own research that felt harder than they should be, and I tried to build the tools I wished I already had.

shinyreact splits a Shiny app along a clean line. The Shiny server does reactive computation. The UI is a React client that you own. shinyreact is the bridge between them, and it ships zero UI components of its own.

You can install it from CRAN or PyPI:

install.packages("shinyreact")

pip install shinyreact

shinyreact is new, and so is the way of building Shiny apps it proposes. We intend to keep the API small, but expect it to evolve as we learn from early adopters.

Shiny + React?

For most Shiny apps, defining the UI in your app.py or app.R file allows you to construct a complete, production-ready app in just a few lines of code.

The trouble starts when the design asks for something Shiny and bslib don’t have: a unique layout, richer interaction, or a component from a non-Bootstrap design system. At that point, Shiny hasn’t had the right tool for the job.

React is that tool, for three reasons:

• Ecosystem. React is the most widely used UI library on the web. Design systems, charts, tables, and maps are all one npm install away.
• The right model. React components are functions of state, which fits Shiny’s reactive model naturally. When the server sends new data, the UI re-renders efficiently.
• AI assistance. LLMs have trained on an enormous amount of React code. Ask a frontier agent for a UI and it will produce better React than it will bespoke Shiny UI. This aligns with Shiny’s goal that app developers should never be required to write low-level HTML or JavaScript themselves.

Later in the post, we’ll discuss a genomics app that renders a 584,000-cell UMAP on the GPU from a plain Shiny server. First, the basics.

Old Faithful with shinyreact

Here is the classic Old Faithful histogram app as a shinyreact app:

library(shiny)
library(shinyreact)

# Set up the page UI using shinyreact
ui <- page_react()

server <- function(input, output, session) {
  x <- faithful$waiting

  breaks <- reactive({
    seq(min(x), max(x), length.out = input$bin_count + 1)
  })

  # Use `reactive_output()` to send data to the client
  output$dist_data <- reactive_output({
    bins <- hist(x, breaks = breaks(), plot = FALSE)
    list(breaks = I(bins$breaks), counts = I(bins$counts))
  })
}

shinyApp(ui, server)

import numpy as np
import pandas as pd
from shiny.express import input
from shinyreact import reactive_output, set_react_page

# Set up the page UI using shinyreact
set_react_page()

x = pd.read_csv("faithful.csv")["waiting"].to_numpy()


@reactive_output
def dist_data():
    breaks = np.linspace(x.min(), x.max(), input.bin_count() + 1)
    counts, _ = np.histogram(x, bins=breaks)
    return {"breaks": breaks.tolist(), "counts": counts.tolist()}

Two things are different from a traditional Shiny app.

1. The UI is one line. page_react() (or set_react_page() in Shiny Express) serves the React client that lives in your app’s www/ directory. There’s no sliderInput() or plotOutput(). The www/ directory contains the static assets for the React app: ui.js and ui.css (when available).
2. The output is data. reactive_output() has no matching UI function. This is a new concept for the Shiny ecosystem! renderPlot() sends an image for plotOutput() to place. reactive_output() sends plain JSON, here the histogram’s breaks and counts. Like any render function, reactive_output() re-executes each time its reactive dependencies change. The server sends facts and React decides how to present them.

Now the client:

// src/ui.tsx (which compiles to www/ui.js)
function App() {
  const [binCount, setBinCount] = useShinyInput("bin_count", 30);
  const bins = useShinyOutputValue("dist_data", null);

  return (
    <main className="layout">
      <label htmlFor="bin_count">Number of bins:</label>
      <input
        id="bin_count"
        type="range"
        min={1}
        max={50}
        value={binCount}
        onChange={(e) => setBinCount(Number(e.target.value))}
      />
      <Histogram bins={bins} />
    </main>
  );
}

If you’ve written React before, this is an ordinary component. Histogram is whatever you like: a hand-written SVG, a charting library, or a component from your design system. The only shinyreact-specific parts are two hooks:

• useShinyInput() works like React’s useState(), except that the value is also sent to the server as input$bin_count (or input.bin_count() in Python). Calling setBinCount() updates the UI and triggers the server’s reactive graph.
• useShinyOutputValue() reads the value of a reactive_output(). When the server recomputes dist_data, the component re-renders with the new data.

Those two hooks cover the vast majority of apps.

IDs and JSON are the contract

The client and server share exactly two things: IDs and JSON values. If you write the client in TypeScript, each hook takes an optional type for its value, such as useShinyInput<number>("bin_count", 30), and your editor will then flag a mismatch that Shiny.setInputValue() never could.

Here is the full round trip for the Old Faithful app:

1. The client calls useShinyInput("bin_count", 30), which sends {"bin_count": 30} to the server.
2. The server reads input$bin_count, runs its reactive graph, and computes dist_data using reactive_output().
3. The client receives the dist_data result as {"dist_data": {"breaks": [...], "counts": [...]}}, and useShinyOutputValue("dist_data") hands it to React.

That narrow boundary is what makes a shinyreact app easy to reason about. The server doesn’t know or care how the histogram is drawn, and the client doesn’t know how the bins are computed. Each side can be reviewed, tested, and rewritten independently, whether a person or an agent wrote it.

When you need more, a few other hooks are available. useShinyOutputStatus() tells you when an output is recalculating so you can show a skeleton, and useShinyMessageHandler() receives one-off messages pushed from the server with send_message(). See the JavaScript API reference for the full list.

You don’t have to write the React yourself

A fair reaction to all of this is, “but I chose Shiny so I wouldn’t have to write JavaScript!”. That’s still the goal. What’s changed is that today’s AI agents are very good at writing React, far better than they are at writing custom Shiny UI, because there is so much more React in the world for them to learn from.

With shinyreact, your job is to own the server, which is where your data and domain logic live, and to describe and review the UI. To make that concrete, both packages ship Agent Skills:

• shinyreact-build-app scaffolds a new shinyreact app from a description.
• shinyreact-convert-app opens an existing Shiny app in a browser, describes what it does in plain English, and then rewrites the UI in React against the same server.

The day-to-day loop is familiar. You edit src/ui.tsx (or ask an agent to), the build step writes www/ui.js, and you reload the running Shiny app to see the change. The server side is unchanged: runApp() or shiny run, exactly as before.

Node.js isn’t required, since a client can be a single www/ui.js file with no build step. However, we do strongly recommend it as it gives you a proper development environment: a build step gets you TypeScript, linting, and formatting.

Keep what you already have

shinyreact doesn’t ask you to throw away the rest of the Shiny ecosystem.

• Existing outputs. renderPlotly(), render.data_frame, and other render functions work as before. Drop a <ShinyOutput id="..." /> into your React tree, and the output’s JavaScript and CSS dependencies are delivered automatically.
• Modules. ShinyModuleProvider namespaces hook IDs to match a server-side module.
• Bookmarking. URL and server bookmarking seed the initial values of useShinyInput().

Testing at every layer

Because the client and server only share IDs and JSON, each layer can be tested on its own. The server is the layer most Shiny developers care about, and it needs no browser at all. Use shiny::testServer() in R, or the new local_server pytest fixture in Shiny for Python 1.8: set inputs and assert on the JSON that comes out.

def test_histogram(local_server):
    local_server.set_inputs(bin_count=10)
    data = local_server.get_output("dist_data")
    assert len(data["counts"]) == 10

The other layers have their own tools:

• The client. Ordinary JavaScript unit tests, with whichever test runner you (or your agent) prefer.
• The wire. wire_tap() for shinytest2 and WireTap for Playwright record the JSON crossing the websocket during a browser test. That gives you an end-to-end assertion on what the client actually sent and what the server actually returned, without reaching into the rendered DOM.
• The behavior. Each example app ships a FEATURES.md: a nested list where every leaf is one checkable claim about the app, written in plain English. A person can read it as a spec, and an agent with a browser can walk it and turn each claim into a deterministic check against the running app.

The testing article covers all of them.

In the wild: Plotomics Live

Over the summer, Shiny intern Samuel Bharti built a collection of bioinformatics Shiny apps. Most of them are plain Shiny and bslib. The one that reached for shinyreact did so because the visualizations demanded it.

Plotomics Live (source, DOI) is a 26-page gallery of GPU-accelerated genomics visualizations, from oncoplots to a one-million-point Xenium spatial view and an interactive 584,000-cell UMAP. Large data skips JSON entirely and moves as compact binary typed arrays straight to the GPU. Because React owns the component, a new selection updates the data in place without re-mounting the visualization or reallocating GPU buffers.

In Samuel’s words:

R stays the analysis engine, React becomes the visualization layer, and shinyreact removes the custom JavaScript bindings, manual message passing, and serialization code that used to sit between them.

What’s next

We’re working on two directions next:

• Embedding React components in existing apps, so you can adopt shinyreact one piece at a time without porting a whole app.
• Wrapping shinyreact in your own package, so you can build a component once and ship it the way bslib ships its components.

Learn more

• Documentation: posit-dev.github.io/shinyreact
• Source and example apps: github.com/posit-dev/shinyreact
• posit::conf(2026) talk slides: Beyond Bootstrap: Building Custom Shiny UI with React

Give shinyreact a try, and please let us know what you build and what breaks.

install.packages("querychat")

pip install -U querychat

This release adds several headline features, including support for multiple tables, data-dict.yml, a full-page chat layout, support for pins, and a new /handoff command. It also builds on shinychat’s recent momentum. As a result, querychat gets chat features like history and file attachments basically for free. querychat_app() provides a quick and useful way to start chatting with data and getting bespoke ggsql visualizations, and it now uses shinychat’s page_chat() for a full chat app experience.

See the R release notes and the Python changelog for the complete list, including a few changes for existing apps if you’re upgrading.

Full-page chat layout

querychat_app() / QueryChat.app() now put the chat front and center (built on shinychat’s page_chat()), leaving more breathing room for things you create within the chat.

library(palmerpenguins)

querychat_app(penguins)

from querychat import QueryChat
from palmerpenguins import load_penguins

qc = QueryChat(load_penguins(), "penguins")
qc.app()

A view of the actual data is always accessible via the data source drawer on the right-hand side. In the case of multiple tables, you’ll see the active table¹, as well as other available tables below it.

The new page() method brings this same full-page chat layout to your own apps. Your users get the chat front and center, and you can still add custom views on other pages, in the drawer, or in the sidebar.

Learn more about building custom apps in R and Python.

qc <- QueryChat$new(penguins, "penguins")
ui <- qc$page("Penguins Explorer")

from querychat.express import QueryChat
from palmerpenguins import load_penguins

qc = QueryChat(load_penguins(), "penguins")
qc.page("Penguins Explorer")

Conversation history

Another major improvement is persistent conversation history (mostly thanks to shinychat). In addition to starting new chats and returning to previous ones, conversations now persist across page reloads and timeouts. As a result, it is now much more difficult to lose your progress.

Also, now that shinychat supports editable messages, canceling responses, file attachments, and more, querychat does too.

Multiple tables

querychat now supports multiple tables in a single chat instance. If those tables reside in a singular source, like a database, you can add them all in one fell swoop with the add_tables() method.

library(querychat)

qc <- QueryChat$new()
qc$add_tables(db, c("customers", "orders", "order_items"))

from querychat import QueryChat

qc = QueryChat()
qc.add_tables(db, ["customers", "orders", "order_items"])

querychat’s query and visualization tools handle joins across these tables, so a single question can span all of them. To write a query like the one below, the LLM first needs to know what’s in each table: column names, types, and value ranges. So it starts by fetching the schema of each table it needs (“Fetch schemas”), then generates the query with that metadata in mind.

In a custom app, the new table() method gives your server code reactive access to any table, including whatever filters the LLM has applied to it. That means you can keep building your own plots and views in Shiny, and your users can drive them just by chatting.

output$order_price <- renderPlot({
  orders_tbl <- qc$table("orders")
  # LLM can perform filter queries on $df()
  orders_df <- orders_tbl$df()
  hist(orders_df$price)
})

@render.plot
def _():
  orders_tbl = qc.table("orders")
  # LLM can perform filter queries on .df()
  orders_df = orders_tbl.df()
  plt.hist(orders_df["price"])

Provide context: data-dict

querychat does its best to gather context from the data itself. When the LLM fetches a table’s schema, it gets whatever metadata querychat can compute from the data. That’s a good start, but in practice it often isn’t enough. Column names can be cryptic, coded values need decoding, and nothing in the data says what “active customer” means to your business or how tables relate. In the example above, the LLM had to infer from column names alone that orders.customer_id points to customers.id.

A data dictionary is how you fill in what the data can’t say about itself. It’s a YAML file that follows the data-dict spec. Alongside plain-English descriptions, it has its own fields for column types, allowed values, keys, and the relationships between tables. This is now the preferred way to describe your data:

tables:
  customers:
    description: One row per customer.
    columns:
      - name: acquisition_channel
        type: enum
        values: [organic, paid_search, social, referral]
        description: How the customer first found us.
  orders:
    description: One row per order.
    columns:
      - name: customer_id
        type: number(id)
        constraints: [foreign_key]
  order_items:
    description: One row per item in an order.
    columns:
      - name: price
        type: number(quantity)
        description: Item price in USD.

relationships:
  - description: Each order belongs to one customer.
    cardinality: many-to-one
    join: orders.customer_id = customers.id
  - description: Each order has one or more items.
    cardinality: many-to-one
    join: order_items.order_id = orders.id

Pass it in as data_dict = "dictionary.yml" (R) / data_dict="dictionary.yml" (Python). When the LLM fetches a table’s schema, any column you’ve documented comes straight from your dictionary, with nothing left to infer. querychat only computes metadata from the data for the columns your dictionary doesn’t cover.

Extract insights: /handoff

Over the course of a conversation, querychat tends to produce a pile of results, some more useful than others. The useful ones deserve to live on in a reproducible artifact that doesn’t depend on the chat app.

That’s the idea behind the new /handoff slash command. It’s available in every querychat app, with no setup required. When a user types /handoff into the chat input, a wizard opens where they select the results that matter, choose an output format (e.g., Quarto, marimo, Shiny, Jupyter), and add any presentation instructions for the LLM to follow when it generates the handoff document.

When the user finishes the wizard, the handoff document’s source code streams into a code editor, where they can revise it by hand or with AI assistance. A download button then gives them a zip bundle with the handoff document, a README file, and the data sources (if they’re small enough).

Chat with pinned data

querychat can now chat with data pinned to a pins board. Pass the board and the pin name, and querychat reads the pin (parquet, CSV, JSON, RDS, and more) and uses its title, description, and tags as the starting data description:

library(pins)
library(querychat)

board <- board_connect()

querychat_app(board, "my_pin")

pip install "querychat[pins]"

import pins
from querychat import QueryChat

board = pins.board_connect()

qc = QueryChat(board, "my_pin")
qc.app()

For more control, such as setting the table name used in SQL, use the new PinSource class directly. Multiple pins, or pins mixed with ordinary data frames, also work together in one chat. Everything is materialized into a shared DuckDB connection behind the scenes, so the LLM can join and filter across all of it. See the data sources guide for R and Python for details.

A few changes for existing apps

This release also includes a handful of breaking changes, mostly around how querychat manages connections and bookmarking now that history is built in. If you’re upgrading, skim the R NEWS or Python CHANGELOG breaking-changes sections before you do.

Learn more

• querychat documentation (R) — full guides on data sources, context, tools, and deployment
• data-dict — the data dictionary spec querychat now reads
• ggsql — the grammar of graphics for SQL that powers querychat’s visualizations
• shinychat (R) — the chat UI toolkit querychat builds on
• chatlas (ellmer) — the underlying LLM tool-calling libraries
• Source on GitHub — issues, discussions, and contributions welcome

Acknowledgements

We thank everyone who contributed to these releases, for opening issues, submitting pull requests, and providing feedback: @gadenbuie, @hadley, @iainwallacebms, @iamYannC, @jnhyeon, @kolabearafk, and @thisisnic.

Last week, three packages in Posit’s open-source AI stack shipped significant releases. We introduced commons 0.1.0, and ellmer and shinychat both received substantial updates. These releases are part of a broader effort to make it as easy as possible to build modern chat applications in R and Python.

In this newsletter, we’ll take a quick tour of what’s new.

Introducing commons

commons, a new framework for building trustworthy self-service data analysis agents in R and Python, is now on CRAN. Read the full announcement here.

The Python package is currently in a pre-release beta stage, with more features arriving over the next few weeks.

If you’re a data analyst, data scientist, statistical programmer, or other data practitioner, you likely have extensive domain knowledge and a collection of trusted code that you already use in analyses, apps, reports, and packages. The core idea behind commons is that we can leverage this trusted code to improve an agent’s correctness.

A commons agent first searches for a trusted calculation. If it finds one that can answer the user’s question, it can run that vetted code and the answer is deterministically marked as verified.

If it doesn’t find a trusted calculation, the agent searches trusted context before writing custom R, Python, or SQL. The answer is either given a citation or marked as “untrusted,” depending on whether the agent provides a verified citation that supports its approach.

The model doesn’t decide how trustworthy its answer is. commons assigns each label deterministically based on the analysis path taken by the agent.

commons also ships with an agent skill to help you create a commons agent and functions for analyzing your users’ conversations.

shinychat v0.5.0 (R) and v0.7.1 (Python)

shinychat v0.5.0 for R and v0.7.1 for Python bring together more of what you need to build a complete chat application. Several of these shinychat updates also made commons possible!

Read the full blog post here. There are many more updates worth checking out.

page_chat()

Use page_chat() instead of the bslib page_*() functions when you want the chat to be the center of your application. page_chat() creates a full-window, chatbot-oriented layout with support for navigation pages, conversation history, an artifact drawer, and more.

Conversation history

Conversation history is enabled by default when you use chat_server() in R or Chat(client=...) in Python, allowing users to start a new conversation, switch between saved conversations, search them, rename them, and delete them.

Readable tool calls and citations

This shinychat release also includes several improvements for understanding how a model arrived at its response.

One such improvement is readable tool calls. By default, related tool calls are grouped into compact, single-line “activity rows”, keeping them from overwhelming the conversation. You can still inspect the individual tool calls by expanding a row.

shinychat also displays citations returned by providers’ built-in web-search and web-fetch tools.

ellmer 0.5.0

ellmer 0.5.0 is now on CRAN. ellmer makes it easy to work with LLMs from R.

Read the full announcement here. Many of the features made available in this release are also available in recent releases of chatlas, ellmer’s sibling package in Python.

Citations

When a model uses a supported built-in web tool, ellmer now returns and displays the provider-supplied citations. This works with claude_tool_web_search(), claude_tool_web_fetch(), google_tool_web_search(), and openai_tool_web_search(), helping you identify the sources behind the model’s answer.

chat <- chat_openai()
chat$register_tool(openai_tool_web_search())
chat$chat(
  "What is the most recent version of ellmer on CRAN? Look it up."
)
#> The most recent CRAN release of **ellmer** is **version 0.5.0**, published
#> **September 4, 2026**.
#> ([cran.r-project.org](https://cran.r-project.org/package%3Dellmer))[1]
#>
#> Sources
#> [1] CRAN: Package ellmer: https://cran.r-project.org/package%3Dellmer

Tokens and costs

Managing tokens and costs is an important part of working with LLMs.

Ever want to know how many tokens an input will take before sending it? For supported providers, you can now use chat$token_count() to estimate input token use. Instead of actually sending the request to the model, it sends the request to the provider’s token-counting API.

chat <- chat_openai(model = "gpt-5.6-luna")
prompt <- content_pdf_file("example-document.pdf")
chat$token_count(prompt)

#> [1] 252

This estimates only the tokens used by the input and does not predict the number of output tokens, so it won’t represent the total round-trip count.

Companies frequently release new models and change their prices. Use the new function models_update_prices() to download and cache the latest pricing data from the ellmer GitHub repo. Cost estimates reported by token_usage(), Chat$get_cost(), Chat$get_tokens(), and printed Chat objects use this data.

Send files to the model

It’s often useful to send files as part of a chat. You can now send CSV, Markdown, code, and other text-based files to a model with content_document_file() and content_document_url(). For large files or files reused across multiple turns, if you’re using chat_openai(), chat_anthropic(), or chat_google_gemini(), use chat$file_upload() instead. It uploads the file once and returns a reference for $chat(). This avoids repeatedly sending the file and reduces token usage and cost.

Solid improvements for custom agents

Taken together, these releases make it easier to build more complete custom agents. ellmer manages model interactions in R, shinychat provides the user-facing chat interface, and commons adds a framework for data analysis agents that builds upon these two.

We’re hootin’ and hollerin’ to share commons, an R and Python package that helps data scientists build trustworthy data analysis agents.

The package is built on ellmer, chatlas, and shinychat, Posit’s open source LLM stack. You can use whatever model you want from any of the providers supported by those packages with it.

To install the R package, run:

install.packages("commons")

To install the Python package from PyPI, run:

pip install commons

The Python package is currently in a pre-release beta stage, but you can install and play around with it today, with more features arriving over the next few weeks.

Design philosophy

AI agents for data analysis can range from overly cautious and narrowly correct to wildly and confidently incorrect. commons provides a framework for you to design more trustworthy analysis agents by providing them with access to existing trusted code, while still allowing them enough flexibility to answer novel, realistic questions.

If you are a data analyst, data scientist, statistical programmer, or other data practitioner, you likely have a deep understanding of your problem domain and a collection of trusted code you depend on for your analyses and use to create apps, reports, and packages. The core idea behind commons is that we can improve an agent’s correctness by giving it the right access and documentation to run this code that you have already vetted.

When answering questions, commons agents first search through a pool of trusted code. If the agent finds an appropriate piece of trusted code, it can invoke it directly, and its response will be tagged with a green shield icon . If it doesn’t, it will search through relevant context before writing its own SQL, R, or Python. If the agent can find trusted context that justifies its approach, it can provide a citation to it at the end of its answer, which will be deterministically checked by commons. Otherwise, the answer is marked with a small warning label .

Notably, the agent itself does not decide how to label a response. commons instead labels answers deterministically, based on the path the agent takes to get it to its answer.

Get started

To get started with the R package, check out the introductory vignette. The package ships with an agent skill to help you hook your trusted code and context up to the agent.

The Python package (although still in beta) is based on the same ideas, and the resulting apps will look very similar regardless of whether you use R or Python—they literally share the same CSS! Check out the Python package site to learn more.

shinychat is a toolkit for building complete, conversation-centered chat applications with Shiny. The R package pairs with ellmer, and the Python package pairs with chatlas. Install the latest releases from CRAN or PyPI:

install.packages("shinychat")

pip install -U shinychat

We cover a lot in this post, and there’s even more in the releases. See the R release notes and the Python changelog for the complete list of changes, including a few changes for existing apps if you’re upgrading.

Build a chat application

A useful chat application needs more than a text box and a streaming response. Your users need a way to return to an earlier conversation, start a new one, correct a question, compare answers, inspect sources, and see what the model is doing when it calls a tool. They may also need to upload a file, open a preview, or move between the chat and the rest of the application.

shinychat gives you sensible starting points for building that experience. Pair it with ellmer in R or chatlas in Python, and you can get a working chat app running with little setup. The chat application model has three layers:

1. page_chat() gives you a full-window chat app with space for navigation, history, tools, and supporting content.
2. chat_ui() lets you place chat wherever it fits best in your application.
3. chat_server() for R or Chat(client=...) for Python connects your app to an ellmer or chatlas client and enables the integrated chat features.

When you want a fully custom experience or need a model client other than ellmer or chatlas, the lower-level pieces are still available for you to assemble yourself.

Start with page_chat()

When chat is the center of your application, use page_chat(). It gives your users a full-window experience with a chat home, navigation pages, sidebars, toolbars, conversation history, and an artifact drawer. Users can move to a settings or sources page while their conversation keeps working and streaming. The Get started guide for R and the Page chat guide for Python walk through the full layout.

Create a chat app

Build a shinychat application starts similarly in both languages:

library(shiny)
library(shinychat)

ui <- page_chat(title = "Assistant", id = "chat")

server <- function(input, output, session) {
  client <- ellmer::chat_openai(
    system_prompt = "You are a helpful assistant."
  )

  chat_server("chat", client)
}

shinyApp(ui, server)

from chatlas import ChatAnthropic
from shinychat.express import Chat, page_chat

client = ChatAnthropic(system_prompt="You are a helpful assistant.")
chat = Chat(id="chat", client=client)

page_chat(title="Assistant", id="chat")

With just a few lines of code, you’ll have a working chat app backed by a live LLM. Passing a client to chat_server() in R, or to Chat() in Python, does all the hard work for you, fulling connecting your app to the model client and giving you a complete multi-user chat application¹.

For a personal chat UI you can use while you develop locally, pass an ellmer client to chat_app() in R, or a chatlas client to Chat(client=...), and then call .app() in Python.

Welcome users

When you’re app opens, don’t leave your users hanging with an empty chat canvas, gree them with chat_greeting() (R, Python)!

Greetings can be used to explain the application, set expectations, and give users a useful first step before they write their first message. By default, they disappear when the user starts chatting, but you can set persistent = TRUE in R or persistent=True in Python to keep one at the top of the conversation history.

Greetings are written in markdown and can even provide actionable suggestions. Users can click a suggestion to fill the input, ready to edit before sending, or send it immediately.

## Welcome!

What would you like to do?

* <span class="suggestion submit">Summarize my data</span>
* <span class="suggestion">Create a plot</span>
* <span class="suggestion">Explain this code</span>

You don’t have to greet your users with the same message every time, you can use LLMs to generate fresh custom greetings. To learn more, we’ll point you to the chat_greeting() documentation pages (R, Python), but it’s worth noting that dynamic greetings can stream into the chat like any other response.

Return to earlier conversations

One of the biggest features to arrive in this release is conversation history, giving your chat app the ability to save and return to previous conversations. It will also persist the current conversation across page reloads and other disconnects, virtually eliminating the possibility of losing your progress. As usual, when you connect shinychat with an ellmer or chatlas client, conversation history is wired up and enabled for you!

Save conversations

The history drawer lets users:

• Start a new conversation.
• Switch between saved conversations.
• Search conversations.
• Rename a conversation.
• Delete a conversation.
• Return to the conversation that was active when they last opened the app.

shinychat generates a short title once the conversation has enough content. Users can replace that title, and title generation never overwrites a manual rename.

• Rename
• Search

You can history_options() in R or HistoryOptions in Python to configure the conversations that shinychat saves. The main options are:

• restore_mode, which controls which conversation opens when a user returns to the app:
  • "browser" is the default. It returns that browser to its most recent conversation without changing the URL.
  • "url" puts the active conversation ID in the address bar, so users can bookmark or share a specific conversation.
  • "bookmark" restores the conversation with the rest of the app state when your app uses Shiny server bookmarking.
• store controls where shinychat saves conversations. Use "memory" for local development or tests, or "file" to save them on disk.
• title controls how the automated conversation titles are generated.

For example, this configuration stores conversations on disk and puts the active conversation ID in the URL:

history <- history_options(
  restore_mode = "url",
  store = "file"
)

chat_server("chat", client, history = history)

from shinychat import Chat
from shinychat.types import HistoryOptions

history = HistoryOptions(
    restore_mode="url",
    store="file",
)

chat = Chat("chat", client=client, history=history)

On Posit Connect, conversation history is included with the platform and is enabled automatically when you provide a model client. The default configuration uses Connect’s persistent storage and scopes conversations to the authenticated user. That gives every user a private conversation history without an additional history service or per-user setup.

In every restore mode, shinychat keeps the transcript in its configured store instead of putting the full conversation in the URL.

Edit a message and compare answers

Editing a message now creates a new conversation branch. When a user edits and resends an earlier message, shinychat forks the conversation at that point: the original question and its later messages remain on one branch, while the edited question begins another. Users can move between the answers with the branch controls in the message.

• Branch 1
• Branch 2

Branches help when a prompt is almost right or when a model takes an unhelpful direction, and they make comparing answers easy without starting over. And they are part of the saved conversation, so users return to their place in the conversation after a reload.

Add content and controls

When chat is part of a larger application, your users still need access to filters, settings, sources, and results. page_chat() gives you a place to put those alongside the conversation: a drawer for results, toolbars for controls, and offcanvas panels for settings you would rather keep off screen.

Artifact drawer

chat_drawer() gives you a place to show previews, rendered reports, tables, plots, or other bits of Shiny UI next to your chat. Your users can keep the conversation visible while they inspect a result.

See the drawer documentation for R or Python for the full API. The complete application example combines a drawer with the rest of the application layout.

Toolbars

Your app may need a Help button that works on every page, while the chat home needs an action that’s only relevant when you’re looking at the conversation. page_chat() gives each action a home through scoped toolbars, built on the toolbar components that bslib and Shiny shipped earlier this year.

If you want an action to follow users through the whole app — pass it to toolbar_global. Put chat-home actions in toolbar in page_chat(), and give a secondary page its own toolbar through chat_nav_panel(). toolbar_input puts related actions below the message box.

See the R get started guide or the Python Page chat guide for the full toolbar API.

Offcanvas panels

page_chat() pairs offcanvas panels with secondary content, such as an answer-length slider or citation setting, and a toolbar button can open an Answer settings panel from any page:

Complete application

As your app grows, page_chat() can grow around the conversation. You can add secondary pages and a sidebar for filters or other app UI and the application menu keeps those options available on narrow screens.

The following example brings the toolbars, sidebar, navigation, and drawer together.

ui <- page_chat(
  "Research assistant",
  id = "chat",
  toolbar = bslib::toolbar(
    bslib::toolbar_input_button(
      "clear_chat",
      "Clear conversation",
      icon = bsicons::bs_icon("arrow-counterclockwise")
    )
  ),
  toolbar_global = bslib::toolbar(
    bslib::toolbar_input_button(
      "help",
      "Help",
      icon = bsicons::bs_icon("question-circle")
    )
  ),
  sidebar = chat_sidebar(
    tags$p("Use filters to focus the results."),
    history = FALSE
  ),
  pages_navbar = list(
    chat_nav_panel(
      "Sources",
      tags$p("Sources selected during this session appear here."),
      toolbar = bslib::toolbar(
        bslib::toolbar_input_button(
          "refresh_sources",
          "Refresh",
          icon = bsicons::bs_icon("arrow-repeat")
        )
      )
    )
  ),
  drawer = chat_drawer(
    tags$p("Select a result to inspect it here."),
    title = "Latest result",
    open = FALSE
  )
)

from faicons import icon_svg
from shiny import ui
from shinychat import chat_drawer, chat_nav_panel, chat_sidebar
from shinychat.express import page_chat

page_chat(
    "Research assistant",
    id="chat",
    toolbar=ui.toolbar(
        ui.toolbar_input_button(
            id="clear_chat",
            label="Clear conversation",
            icon=icon_svg("arrow-counterclockwise"),
        )
    ),
    toolbar_global=ui.toolbar(
        ui.toolbar_input_button(
            id="help",
            label="Help",
            icon=icon_svg("question-circle"),
        )
    ),
    sidebar=chat_sidebar(
        ui.p("Use filters to focus the results."),
        history=False,
    ),
    pages_navbar=[
        chat_nav_panel(
            "Sources",
            ui.p("Sources selected during this session appear here."),
            toolbar=ui.toolbar(
                ui.toolbar_input_button(
                    id="refresh_sources",
                    label="Refresh",
                    icon=icon_svg("arrow-repeat"),
                )
            ),
        )
    ],
    drawer=chat_drawer(
        ui.p("Select a result to inspect it here."),
        title="Latest result",
        open=False,
    ),
)

Show how the model reached an answer

Understanding how an LLM arrived at an answer is just as — if not more — important than getting the answer from the model. A response can include ordinary text, thinking content, web activity, citations, tool calls, tool results, and custom UI. shinychat works hard to make the model’s work visible and presents each part in a way that helps users understand the answer and what produced it.

Keep tool calls readable

Tool calls are now shown as compact activity rows instead of letting them take over the conversation, refining the tool-call cards shinychat introduced last year. By default, related calls are grouped together into a single row, and users can still expand a group, open an individual call, and inspect the request and result when they need more detail.

Opening an individual call shows the request and the result in a card:

Grouping keeps the answer readable, and the request and result stay one click away. To customize grouping or register tools, see Tool UI in shinychat for R, Tools in Shiny for Python, tool/function calling in ellmer, or tool calling in chatlas.

Show citations for web search and fetch

Many LLM providers offer built-in web search and web fetch tools that let your agent search the web, and their APIs return citations when the model uses that content in a reply. shinychat now displays those citations automatically.

For example, here’s how to register Claude’s tools with an ellmer or chatlas client:

library(ellmer)

client <- chat_anthropic()
client$register_tool(claude_tool_web_search())
client$register_tool(claude_tool_web_fetch())

from chatlas import ChatAnthropic, tool_web_fetch, tool_web_search

client = ChatAnthropic(
    kwargs={
        "default_headers": {
            "anthropic-beta": "web-fetch-2025-09-10"
        }
    }
)
client.register_tool(tool_web_search())
client.register_tool(tool_web_fetch())

When this client is used with chat_server(), citations are connected directly to the portions of the assistant’s response that they support.

Custom retrieval applications, like the RAG systems you can build with ragnar or raghilda, can use the same citation UI by prompting the assistant to use a <shiny-aside> tag to attach a source to a claim.

Stream responses and show thinking

With chat_server() in R or Chat(client=...) in Python, shinychat streams responses and shows supported thinking content in a collapsible panel. Users can cancel a slow response with the stop button or the Escape key, and the partial response stays in the conversation.

Add files and shortcuts

Attach files

File attachments are now supported in shinychat! Your users can send images, PDFs, and text files through a file picker, drag and drop, or paste, and shinychat sends each file to the model alongside the user’s message. When you use chat_server() in R or Chat(client=...) in Python, your app gets that support for free.

Add slash commands

You can now register chat shortcuts, or slash commands, with chat$slash_command() in R or @chat.slash_command() in Python. The command palette appears when users type /, and they serve as a way to trigger server-side code, inject context or additional prompting, or even just take an action in your app, all from the chat input.

Check out the shinychat for R or shinychat for Python documentation for details.

More shinychat-powered apps

The next release of querychat will bring these chat features to data applications, including conversation history, attachments, tool displays, and citations. It will introduce a page-first querychat_app() workflow and a new page() API for adding querychat to an existing Shiny page.

btw 1.5.0 already uses shinychat 0.5.0 to give btw_app() a complete coding assistant for your R projects. It adds conversation history, a page_chat() layout, and slash commands to an assistant that can use your R session, project files, and package documentation.

A few changes for existing apps

Existing chat_ui() applications remain supported when chat shares a page with other top-level content. When the conversation should fill the application instead, choose page_chat() and use it as the outermost page container; nesting it inside another page layout breaks the full-window layout and history experience.

In R, chat_mod_ui() and chat_mod_server() are soft-deprecated in favor of pairing chat_ui() and chat_server() by ID. In both languages, a startup message no longer seeds a conversation when history is enabled; use a greeting or append messages through the chat object instead.

The release also protects users from unsafe model-authored Markdown, shows an error when a response fails before streaming starts, and preserves tool results, citations, attachments, and other rich content when users return to a conversation.

With page_chat(), chat_server() or Chat(client=...), and the history options, you can now give your users a complete chat application: saved conversations they can return to, messages they can edit into new branches, greetings and suggestions to start from, and responses with visible tool calls, citations, and thinking.

Read the shinychat for R documentation or the shinychat for Python documentation to explore the examples. For the complete list of changes, see the R release notes and the Python changelog.

Acknowledgements

We thank everyone who contributed to these releases, for opening issues, submitting pull requests, and providing feedback: @bastianolea, @bianchenhao, @christophsax, @cpsievert, @crissthiandi, @elnelson575, @gadenbuie, @Harshit28j, @JamesHWade, @jcheng5, @jlxAtNovozymes, @jnhyeon, @jose-c-milliman, @kaipingyang, @lucasrod16, @markmcd, @nbenn, @parmsam, @schloerke, @shea-parkes, @simonpcouch, @slupczynskim, @thisisnic, @wlandau, and @xx02al.

You can install the latest version from CRAN with

install.packages("ellmer")

This blog post covers the major changes in this release: a lifecycle update, updates to how you can work with files with ellmer, new ways to update model price data, returning citations when using web search tools, and new hooks for developers building on ellmer’s tool loop.

The full list of changes can be found in the release notes.

Lifecycle

chat_github() and models_github() are now defunct, since GitHub Models has been retired.

We’ve tightened up what a tool can return: a string, an atomic vector, a JSON string, or a Content object. Returning anything else, like a data frame or a list, now gives a deprecation warning. Previously ellmer converted these to JSON for you, but any problem with the conversion surfaced long after your function had finished, and it was easy to forget that the model can only read the result, not compute with it. For a data frame, convert it yourself:

get_weather <- tool(
  function(cities) {
    df <- weather_api(cities)
    jsonlite::toJSON(df, dataframe = "columns")
  },
  ...
)

New features

Sending files to the model

There are now two ways to give the model a file. New content_document_file() and content_document_url() send text-based documents like CSV, Markdown, and code files, just as content_pdf_file() and content_image_file() already do for PDFs and images. The contents go inline with your message, so this works with every provider:

penguins <- tempfile(fileext = ".csv")
readr::write_csv(
  data.frame(
    penguin = c(
      "Waddlesworth", "Flipper McGee", "Turbo Tuxedo",
      "Captain Blubber", "Sir Slidesalot"
    ),
    race_time_seconds = c(43.2, 38.7, 31.9, 45.1, 36.4)
  ),
  penguins
)

chat <- chat_anthropic()
chat$chat("Who won the race?", content_document_file(penguins))
#> Based on the race times, **Turbo Tuxedo** won the race with the fastest time of
#> **31.9 seconds**.

Inline contents are re-sent with every turn, which adds up over a long conversation, especially with a large file. For those cases, chat$file_upload() sends the file to the provider once and returns a reference you pass to $chat() instead. Because the file isn’t re-sent with every message, this also reduces your token usage and costs:

chat <- chat_google_gemini()
race <- chat$file_upload(penguins)
chat$chat("Who won the race?", race)
#> **Turbo Tuxedo** won the race with the fastest time of **31.9 seconds**.
chat$chat("And who came last?")
#> **Captain Blubber** came in last with the slowest time of **45.1 seconds**.

You can manage your uploads with chat$file_list(), $file_get(), $file_download(), and $file_delete(). File management works with chat_openai(), chat_anthropic(), and chat_google_gemini(), and replaces the now-deprecated claude_file_upload() and google_upload().

Citations

When a model answers using a built-in web search or fetch tool, the provider usually reports which sources back the answer. ellmer 0.5.0 captures citations from Claude, Google, and OpenAI.

chat <- chat_anthropic()
chat$register_tool(claude_tool_web_search())
chat$chat("What are the current stable versions of R and Python? Look them up, one line each, no commentary.")
#> R: R version 4.6.1 (Happy Hop) has been released on 2026-06-24[1]
#>
#> Python: Python 3.14.7 / 5 August 2026[2]
#>
#> Sources
#> [1] R: The R Project for Statistical Computing: https://www.r-project.org/
#> [2] Python (programming language):
#> https://en.wikipedia.org/wiki/Python_(programming_language)

Citations are also kept in the chat history and included in streamed output, so apps built on ellmer can display them as well.

Counting tokens and keeping prices current

Two additions make it easier to know what a conversation will cost before you commit to it. Chat$token_count() asks the provider how many tokens some input would use, without actually sending it to the model.

chat <- chat_anthropic()
prompt <- "Tell me a joke about an R programmer"
chat$token_count(prompt)
#> [1] 19

Token counting is currently supported by chat_anthropic(), chat_openai(), chat_google_gemini(), chat_google_vertex(), and chat_posit().

Providers change their prices more often than we release ellmer. You can now update in between releases with models_update_prices(), which downloads the latest pricing data from GitHub and caches it locally.

Other improvements

• Default models have been updated across providers: chat_anthropic(), chat_aws_bedrock(), chat_databricks(), chat_posit(), and chat_snowflake() now use Claude Sonnet 5; chat_openai() and chat_openrouter() use GPT 5.6 Terra; and chat_google_gemini() and chat_google_vertex() use Gemini 3.7 Flash. We update the default models regularly, so if you’d prefer to pin your code to a specific model, you should specify it using the model parameter.
• chat_aws_bedrock() now supports Bedrock Mantle, the newer endpoint that serves models like Claude Mythos and the GPT-5 family through the Anthropic Messages and OpenAI Responses APIs, rather than only the Converse API. ellmer picks the right API from the model name, so this should just work. If you’re using a model it doesn’t recognize, you can set the new api argument yourself.
• You can now stream structured output. Chat$stream() and $stream_async() gain a type argument, which works the same way as in $chat_structured(), for providers that support it.

Developer updates

Two new features help developers building on ellmer’s tool loop.

Inside a tool, tool_context() returns the request that triggered it and the conversation so far, so a tool can make decisions, not just the model. Here, a query tool stops after three calls:

run_query <- tool(
  function(sql) {
    # count_tool_results() is a stand-in for your own helper
    if (count_tool_results(tool_context()$turns) >= 3) {
      tool_reject("Query budget used up. Answer with what you have.")
    }
    jsonlite::toJSON(DBI::dbGetQuery(con, sql), dataframe = "columns")
  },
  name = "run_query",
  description = "Run a SQL query",
  arguments = list(sql = type_string())
)

Chat also gains $on_request_start() and $on_request_end(), which fire before and after every request to the model, including each round of the tool loop. For example, to time each request:

chat <- chat_anthropic()
chat$register_tool(run_query)

started <- NULL
chat$on_request_start(\(turns) started <<- Sys.time())
chat$on_request_end(\(turn) message("Request took ", round(Sys.time() - started, 1), "s"))

chat$chat("Find the mean of every column in mtcars, one query at a time.")
#> Request took 3.6s
#> ◯ [tool call] run_query(sql = "SELECT * FROM mtcars LIMIT 5")
#> ● #> [{"mpg":21,"cyl":6,"disp":160,"hp":110,"drat":3.9,"wt":2.62,"qsec":16.46,…
#> Now I'll compute the mean of each column one query at a time, as requested.
#> Request took 2.7s
#> ◯ [tool call] run_query(sql = "SELECT AVG(mpg) AS mean_mpg FROM mtcars")
#> ● #> [{"mean_mpg":20.0906}]
#> Request took 1.9s
#> ◯ [tool call] run_query(sql = "SELECT AVG(cyl) AS mean_cyl FROM mtcars")
#> ● #> [{"mean_cyl":6.1875}]
#> Request took 2s
#> ◯ [tool call] run_query(sql = "SELECT AVG(disp) AS mean_disp FROM mtcars")
#> ■ #> Error: Tool call rejected. Query budget used up. Answer with what you
#> have.
#> It looks like the query budget has been used up, so I can only report the means
#> I was able to compute before being cut off:
#>
#> | Column | Mean |
#> |--------|------|
#> | mpg | 20.0906 |
#> | cyl | 6.1875 |
#> Request took 3.4s

$on_request_start() also receives the turns about to be sent, so an agent can compact its history with chat$set_turns() before the context window fills up.

Acknowledgements

A big thanks to the 73 people who helped make this release possible by filing issues, contributing code, and asking questions: @1beb, @abiyug, @aclink88, @AdaemmerP, @alesanGreat, @Apollo7777777, @arnavchauhan7, @arunrajes, @atheriel, @awunderground, @bakaburg1, @bastianolea, @bshor, @cerebrixos, @CoryMcCartan, @cpsievert, @D-M4rk, @dareneiri, @debruine, @diegomsg, @diegoperoni, @dipterix, @earthcli, @etiennebacher, @feddelegrand7, @FrancescoMonti-source, @frankiethull, @gadenbuie, @hadley, @hectorgray, @hopessugar, @hswerdfe, @JamesHWade, @jamesinottawa, @jcheng5, @jcrodriguez1989, @jeroenjanssens, @JosiahParry, @jrosell, @kaipingyang, @karawoo, @kbenoit, @kchou496, @klin333, @kolabearafk, @ksr-zguo, @lazasaurus-ai, @lionel-, @MLiedgens, @n8layman, @nbenn, @neil-bray, @nrineausanofi, @ntentes, @omorante, @petzi53, @rajabzadehalidip, @rempsyc, @Sade154, @sarahsdao, @scjohannes, @simonpcouch, @Sirhubi007, @SokolovAnatoliy, @sounkou-bioinfo, @stefanlinner, @t-kalinowski, @Tazinho, @thisisnic, @thoov08, @trangdata, @WvdH-Novus3, and @xmarquez.

Welcome back to another edition of our monthly Positron updates! Each month we share highlights from our latest release and useful resources. Last release we told you about new Data Connections sources, a round of polish for inline output in Quarto documents, and help installing missing packages. This milestone brings a redesigned welcome page, a first version of Import Data, new Posit Assistant features, expanded Data Connections, package vulnerability scanning, and a more responsive Console.

Welcome page refresh and interpreter setup

We redesigned the Positron welcome page. It now leads with an environment setup card that checks whether Python and R are ready to use, with actions to help resolve any problem it finds. The Positron badge and name now come with a Help button that opens the Help pane, and a banner links to the walkthroughs, including a new “Get Started with Positron” walkthrough that covers the Positron panes, keyboard shortcuts, built-in extensions, and Git.

The environment setup theme continues into interpreter selection itself. When you select a Python managed by your operating system or a package manager, Positron now offers to create a virtual environment for your workspace instead of installing packages into that shared interpreter. With no folder open, the environment Positron creates now lives at ~/.virtualenvs/positron rather than ~/.venv, so it stays in the interpreter picker after a restart, and Positron asks before creating an environment in your home directory. We also removed the confusing startup notification that reported no interpreters were found while linking to documentation saying no setup was needed, and interpreters installed under /opt/python are now labeled Global instead of Unknown in the interpreter picker.

Import Data

Before we started work this month, importing data via the Positron UI was our most upvoted feature request and this release now delivers a first version. When you view a CSV or TSV file in the Data Explorer, a new Import Data button in the action bar opens a dialog. The dialog shows the code to load that file into a data frame. You can copy the code, or click Import to run it in the console, starting a session if one is not already running.

Import Data supports CSV and TSV files in Python with pandas and in R with the readr package, as well as Excel workbooks and Parquet files (with readxl and nanoparquet in R). The generated code can reproduce the filters and sorts you have applied in the Data Explorer, and it names the file by a workspace-relative path when the file is inside your workspace, so the code is easier to share and rerun. You can open the dialog from the Data Explorer, the File menu, the Variables pane, or the File Explorer context menu.

Posit Assistant

This release brings a new Agent Layout that opens Posit Assistant in the editor area with a compact Session pane, giving you more visibility into the agent’s actions as it works alongside your code. Configuring language model providers also gets a redesign. Our new Configure LLM Providers modal groups providers by connection state, so you can see at a glance which providers are ready to use. If you have trouble with the new dialog and need to switch back, set assistant.newProviderModal to false.

You can now configure multiple custom providers, each with its own name, type, endpoint, credential, and model list; the previous single “Custom Provider” option is now called “OpenAI Compatible” to better match what it actually does. Amazon Bedrock users get a smoother experience as well. An expired AWS SSO session can be renewed right from the provider modal instead of requiring aws sso login in a terminal, and the AWS profile and region can now be set in the configuration dialog rather than only through environment variables or a hand-edited providers.json. Speaking of which, providers.json now accepts comments, and your comments survive edits that Positron makes to the file.

Data Connections

The Data Connections preview keeps growing. A new ODBC data connection driver lets you browse any database with an installed ODBC driver in the Connections pane and open it in the Data Explorer. Data sources already configured on your machine appear automatically. Databricks gains OAuth sign-in on desktop and reads DATABRICKS_TOKEN, DATABRICKS_HOST, and DATABRICKS_CONFIG_FILE credentials managed by Posit Workbench automatically. A Disconnect option in the context menu closes a connection and any Data Explorers opened from it, and each data connection driver now gets its own log output channel.

Smaller improvements round out the preview. You can now set dataConnections.enabled per workspace, so a repository can turn on the Connections pane for anyone who opens it. The tree is shallower so table and column names get more of the panel’s width, and a new dataConnections.tree.indent setting controls the indentation. DuckDB connections now default to read-only, so the Connections pane and a Python or R session can have the same database open at once; when a lock conflict does happen, the error now explains that another session has locked the database.

Package security vulnerabilities

The Packages pane now shows known security vulnerabilities (Common Vulnerabilities and Exposures scoring) for installed Python and R packages, so you can see at a glance whether something in your environment has a known CVE. The data comes from your environment’s own Posit Package Manager repository when it has one, and from the public Posit instance otherwise, so what you see reflects the same package source your organization already governs.

A more responsive Console

Console code submission is now faster, always shows visual feedback, and can be canceled while a completeness check is in flight; the Console no longer waits indefinitely with no feedback when a kernel is slow or unreachable. The Console breaks multi-statement input into complete expressions and executes them statement by statement for languages that support it. A new console.promptWhenIncomplete setting runs submitted code immediately without a completeness check.

A few more Console fixes are worth knowing about. The Console no longer takes focus at runtime startup when you are working in another view or editor, Interrupt stays visible after switching between busy consoles, and session names now ellipsize to fit as the console tab list narrows.

What’s coming next

• posit::conf(2026) is next week! Our team will have several sessions on Positron, and there is still time to register to join us virtually from anywhere in the world.
• Meet Posit at CDAO Government on September 22-23 in Washington, D.C. Stop by our booth to talk data modernization and where agentic AI fits in government.

Recently, there’s been a lot of talk about fine-tuning models. The reasoning usually goes something like this:

• There’s some task that needs to be done regularly.
• Today’s frontier models can do it quite reliably, but it’s expensive and the bills are starting to rack up.
• Cheaper models can’t quite do the task reliably, but a fine-tuned variant of a small, open-weights model might be able to just as cheaply.
• Therefore, you should fine-tune models for tasks that your organization does.

I’ve seen a number of blog posts cited in support of this idea.

It makes sense that fine-tuning is appealing! AI is getting expensive, many players in the space cannot make the privacy guarantees we’d hope, and just as you start to rely on one proprietary model, it’s phased out in favor of a newer release. That said, my reaction is that this approach seems more engineering-intensive and, likely, more expensive than alternative approaches. I’ll try to make that case here.

What is fine-tuning?

Before we go further, it’s probably worth quickly outlining what I mean by fine-tuning. In short, fine-tuning means training an existing model further to improve its performance on a particular task.

Large language models contain huge arrays of parameters. Input, usually in the form of text, is turned into arrays of numbers, and those arrays get multiplied a bunch of times to form the output, which is then typically converted back into text. Those multiplications are quite computationally intensive, and the array of parameters is itself quite large. For example, the smallest models that are beginning to be able to reliably complete basic agentic work contain 8 billion parameters (in short, “8B”).

Fine-tuning changes a model’s behavior, either by updating the existing parameters, or by training a smaller set of additional parameters that work alongside the originals. The goal of fine-tuning is, broadly, to increase the performance of a model on a given task while minimizing the impact on the model’s ability to do other tasks.

Fine-tuning is hard

It is very, very difficult to successfully fine-tune a model. I don’t claim that it’s impossible, but it is a substantial engineering effort, requiring the careful attention of dedicated scientists with access to today’s frontier models across several weeks, just for the first edition of the model. To show why, let’s revisit each step of that process.

What model should you start with? In short, you want the smallest possible model that has the ability to learn to do the task reliably while retaining sufficient general intelligence. It is very hard to predict when or how capabilities will emerge during fine-tuning without first fine-tuning, meaning that the choice will need to be revisited several times once you’ve made a first go at the remaining steps.

What data gets used for training? Once you’ve chosen a model, you need to find some training data to fine-tune it with. What should you use? Ideally, you would use the real data that represents the task and its inputs. However, in order to train on that real data, you typically need the user’s consent, and it’s likely that you won’t have that consent.

And then, when you do have user consent and choose to train on that data, you now have a mandate not to overfit. This is because if the model you’re fine-tuning internalizes the data you’re training on and is able to (even hazily) recollect it, you’ve now exposed that data to any users of the model.

The other approach, then, is synthetic data, created by asking a model to generate a bunch of scenarios that resemble the real inputs and then demonstrate how to carry out the task in those scenarios. But this type of synthetic data has its own problems: notably, the models creating the data have their own set of tics that make the data unrepresentative of real-world data. The Elias in the Lighthouse effect is a notable example of this phenomenon. A wide variety of models will, when asked to write a story, write about a lighthouse keeper named Elias Thorne. On its own, the story of Elias Thorne might be informative training data. However, 1,000 stories about a lighthouse keeper Elias Thorne are not.

How do you nudge the weights? Let’s assume you do have a diverse, representative set of training data to work with. You’ll now need to decide how, mechanistically, to change the behavior of the model—do you need a full fine-tune, or will a LoRA be sufficient?

Next, you’ll need to tune a set of 5-10 hyperparameters. Notably, because they are hyperparameters, there is no generally good “magic number” for them, and instead you’ll need to try a bunch of values and see what works. You’d make some guesses, see what happens, then generate some hypotheses on what a given change to those parameters might do, then try out a different number and see if it has the desired effect. Every time you’re figuring out what to try next, you’ll be reading a ton of test cases and trying to observe general failure modes exhibited in this intermediate draft of your fine-tuned model. That might change the initial choice of model you’re fine-tuning, and it might change which subsets of the training data you’re exposing to the training process.

How do you measure success? You need a reliable way to tell whether each version of your model is actually improving. That is especially tricky for free-text tasks, because two answers can be equally good even when they differ syntactically. Because of this, you’ll probably want an LLM-as-a-judge system, where another model compares the responses to a target and grades according to a rubric you’ve supplied. LLM-as-a-judge systems are themselves quite hard to design correctly, and you’ll be reading a bunch of that judge’s grading transcripts, too.

It’s also worth mentioning that getting your fine-tuned model to score well on your own benchmark is not the hard part. Many of these fine-tuning writeups include a plot along these lines:

When I see that a single-digit-billion-parameter fine-tuned model scores better than Fable 5.1 or GPT 5.6 Sol on an evaluation, I interpret that as evidence that the evaluation is not meaningful. In my own experience, it is not (comparatively) hard to get a small model to score very well on any given benchmark. What’s much more difficult is preserving the broad intelligence of a model while driving the evaluation score up. A meaningful evaluation can do both at once, measuring task performance under a realistic, broad distribution of possible task configurations. It is very hard to author meaningful evaluations, especially for models as capable as those that exist today.

But let’s say you’ve made it this far! You found a good model to start from, a diverse set of training data that you’ve obtained consent to train on, and a meaningful way to measure progress. Now, it’s time to put it in production.

Fine-tuning is expensive

Fine-tuning can seem appealing because the actual fine-tuning part of the process can be relatively inexpensive, likely on the order of a few dollars to a few hundred dollars.¹

However, actually deploying the model to do the task you trained it to do is likely to be substantially more expensive.

…because hosting is expensive

Hosting a model yourself is more expensive than using a similarly capable model hosted by someone else unless you have extraordinary volume. Frontier-model providers like Anthropic and OpenAI serve extraordinarily large volumes, keeping their compute almost always near max capacity. It’s likely that the same won’t be true for your fine-tuned, self-hosted model.

As an example, let’s say I host Gemma 4 26B A4B on a single H100. I can rent a high-availability H100 GPU instance at $0.0833 a minute, or $44,000 a year. Assuming a coding-agent-like workload,² that would buy about 96 billion Sonnet 5 tokens, and Sonnet 5 is a much more capable model. To break even, this fine-tuned model would need to serve about 182,000 tokens per minute, 24/7, throughout the year.

It might be possible to serve that many tokens. The hard part is finding enough demand for one task to keep the model busy, and that demand needs to be relatively smooth. If the model sits idle, you’re still paying for it. If demand spikes, you either rent a second GPU or accept worse performance for users.

…and you must host

The other suggestion here is that, if the model is small enough, users who need to do the task can just download the weights and run it on their laptop or a dedicated workstation.

The effectiveness of this argument depends on the kind of task to be done. However, it’s worth considering the potential for this to be a very unpleasant user experience compared to just using a model that someone else hosts. For example, let’s say my colleague does some task for an hour every week and there’s some model small enough to run on my laptop that’s capable of doing the task. In order to use the model for that task, my colleague would need to download the 4GB or 8GB or 100GB or whatever of weights. That user would also need to be capable of configuring the serving of the model, and they’d need to make that happen every time they started the task. If that model was accessed through a tool that the user was using regularly anyway—Claude Code pointed at a local ollama model, for instance—they’d need to remember to switch the model over in the application’s settings. If that model was accessed through a different interface, the user would need to use a different interface than they normally use LLMs with for that task specifically. Neither of these are good UX.

Even if the software for locally serving models got much more pleasant than it currently is, it’s hard to compete with the experience of pay-as-you-go for the Everything Tool that wraps the Everything Model.

Your fine-tune will quickly fall behind

Let’s say you successfully fine-tune a model based on some fictional model Kuen 3. Then, the following week, Kuen 3.5 is released. 3 of the 100 most capable LLM scientists on this planet worked on it. It’s almost as good as your fine-tune on that specific task, and it’s also broadly capable at a very broad array of tasks.

Fine-tuning is not a boat that is lifted by the rising tide of broader AI progress—in order to take advantage of the Kuen 3.5 release with your fine-tune, you’d need to restart that process of choosing parameters and observing fine-tuning runs. The underlying architecture of the model may have changed, and thus the approaches that you used to fine-tune Kuen 3 might not work for Kuen 3.5 on your first try.

What you should do instead

Instead, the boat that is lifted by a rising tide in this context is plain old prompt engineering. (POPE, as Joe Cheng calls it.) Choose a model that’s available to you, that fits your price point, and seems broadly capable across a wide variety of public benchmarks. (Extra points if the vibes on the model from people you trust are good, and extra points if someone else is serving it.)

Then, put together a short prompt telling the model how to do the task, perhaps inside of some coding agent harness like Claude Code (or, hey, Posit Assistant), and see what it does. Adjust the prompt to tell it how to do things correctly that it tends to trip up on, and iterate from there. If you’ve already put together an evaluation as part of your fine-tuning process, you could even reuse that! My colleague Sara and I have written about prompt engineering in the past if you’re interested in learning more, once about the more specific task of POPE for teaching LLMs about R packages but in relatively generalizable ways, and once focused on deciding between the popular ways to deliver context to coding agents.

Recent past newsletters

• How to choose a model
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To install the newest release, run the following in R:

install.packages("vitals")

The package includes two new helpers, claude_code() and codex(), which allow you to compare your own ellmer-built agents with leading coding agents. This release also ships another new helper, vitals_log_read(), which supports reading log files back into tibbles, including columns of resumable ellmer Chats. Finally, the release includes several performance improvements; log files are much smaller, and the log viewer that reads them is now substantively faster.

To read the full list of changes, see the changelog.

Agent solvers

vitals is a port of Inspect, a well-adopted Python framework for LLM eval from Posit’s own JJ Allaire. One of the concepts that vitals borrows from Inspect is the concept of a “solver,” or the LLM-powered system that sets out to solve some task. The simplest solver is just the LLM itself, with no system prompt or tools, like what you’d get from running chat_anthropic() from ellmer. Solvers can gain all sorts of prompts and tools, which allows vitals users to test the effect of a change in their prompt or the addition of a new tool.

In the last year or so, the dominant interface to solvers in Inspect has become “agent solvers”: interfaces to the popular coding agents Claude Code and Codex. You call the helper claude_code() or codex(), and Inspect will proxy traffic through the real coding agent harness.

vitals now has first-class support for these two helpers, allowing users to compare their own agents built with ellmer to popular coding agents like Claude Code and Codex. You provide the set of tasks and grading guidance, and vitals will take care of the communication with Inspect.

Read eval logs back into ellmer Chats

One of the big annoyances I’ve had in my own usage of vitals is log storage. So that users can use Inspect’s log viewer directly, we write evaluation logs to a JSON format that Inspect can read.^[1] However, I often want to write R code against the original R objects—ellmer Chats especially—that the logs were generated from. Loading in the ellmer Chats would especially be helpful for inspecting (ha!) the conversation histories in the same interface that users of the ellmer application would see.

Because of this, I’ve often saved both the JSON logs and .rda logs, the latter of which contain the ellmer Chats. These files are large on their own, and it feels even more silly passing around duplicates of them.

The new release of vitals introduces vitals_log_read(), which reads an eval log file back into a tibble of samples. (It’s almost exactly what you’d get if you ran the get_samples() method on a vitals Task object.) That tibble includes reconstructed solver (and, for model-graded scorers, scorer) chats as ellmer Chat objects. For some providers, the chats will even be resumable; you can load in a solver from a JSON file into an R session and ask that solver a question yourself.

Performance improvements

The long and the short of this section is just to say that:

1. Logs will take up less storage space than they did before. Roughly, logs written with the new vitals version will be 4x smaller than before, and the magnitude of savings increases with the complexity of the log.
2. We now display logs (with vitals_view()) much more quickly. The log viewer should feel very snappy for almost all uses of the package.

I’m really excited to have this release on CRAN! Take it for a spin and let me know if you run into issues on the package repository.

Orbital’s whole pitch is that a trained model becomes SQL, so a database can run predictions on its own, with no Python process anywhere near it. Until 0.6.0, “trained model” meant scikit-learn: pipelines, trees, linear models, all .fit() in Python and then turned into a SELECT statement. It never covered what a lot of teams are actually training now: PyTorch models, not scikit-learn pipelines.

Orbital 0.6.0 closes that gap. A torch.nn.Sequential network, trained exactly the way you already train it, now compiles to the same kind of SQL a linear regression would.

No ONNX Runtime. No model server. No separate inference service to keep alive next to the database. Just a query.

Why does that work at all, for a framework Orbital was never written for? Because of a decision made long before PyTorch was ever on the table.

Why this isn’t a bolt-on

Orbital was never really a scikit-learn tool. Underneath, it converts a scikit-learn pipeline to ONNX (Open Neural Network Exchange, a standard graph format for trained models) using the skl2onnx library, then walks that graph node by node to produce SQL. Scikit-learn was always one hop removed from what Orbital actually translates.

Adding PyTorch meant taking the same hop from a different starting point. torch.onnx.export turns a torch.nn.Sequential model into that same kind of graph. Feed it into the translator that already existed, and the translator doesn’t know or care whether the graph came from PyTorch or scikit-learn.

The proof is in how little new code that took. The entire engine for running a feed-forward network in SQL is three small classes: a Gemm translator for Linear layers, ReLU, Sigmoid. Everything else (the translator base class, the optimizer, the per-dialect SQL compiler) already existed, built earlier for trees and linear models.

When I first thought of support for PyTorch, I put it this way: “the underlying translation works on ONNX graphs… the same value proposition orbital already provides for scikit-learn models can apply as it is to pytorch networks exported to ONNX.” That sentence turned out to be the whole implementation plan.

If you want the fuller picture of how a graph becomes SQL, parser, then translator, then optimizer, the architecture docs walk through all three stages in order.

That architecture is also why I keep calling this multiple frameworks, not two frameworks. Relu, Sigmoid, Tanh, and Softmax are single translators, not one per framework. Scikit-learn’s MLPClassifier and MLPRegressor reach them through MatMul and Add, PyTorch’s nn.Sequential reaches the exact same translators through Gemm. One implementation, two entry points, and no reason it has to stop at two.

Scikit-learn and PyTorch are both real and shipping today, which is enough on its own to call this “multiple frameworks.” But the dependency story is already moving that direction: issue #113 proposes turning scikit-learn itself into an optional dependency, the same way PyTorch already is, so the core stops assuming any particular framework at all.

Making it actually usable

Neural networks are layered, and every neuron in layer two reads every output of layer one. If Orbital just inlines those outputs at each place they’re read, instead of naming them once, that repetition compounds from one layer to the next. Two layers doubles the inlined text. Five layers is a different order of magnitude.

That’s not theoretical: the issue that tracked the fix measured scikit-learn’s own default MLPClassifier(hidden_layer_sizes=(100,)), the first thing anyone reaches for, at 53MB of generated SQL and roughly 894 seconds just to generate it. A hundred neurons in one hidden layer, and the query was already unusable.

PyTorch’s Gemm translator never had this problem. It has called preserve(), materializing its output as a real SQL column, since the day it was written. Scikit-learn’s MLPClassifier and MLPRegressor compile through different ONNX ops though: MatMul then Add, because that’s what skl2onnx emits, not Gemm. Neither of those translators called preserve() at all.

The fix, Optimizer.preserve_referenced_outputs(), runs after every single node in the translation loop, for every translator, not just MatMul and Add, and checks how many times that node’s output is actually referenced downstream. Referenced more than once, it gets materialized as a named column. Referenced once or not at all, it stays inlined, no extra column, no extra noise.

None of this is new machinery either. Tree ensembles already lean on the same trick: preserve() materializes per-tree votes, or the whole ensemble’s aggregated vote so it isn’t re-emitted everywhere it’s read, as real SQL columns. preserve_referenced_outputs() generalizes that same idea automatically, for every translator, whether it’s part of an ordinary pipeline or a neural network.

Here’s what that fix was worth, measured on three shapes while it was being built:

Generation time collapsed just as hard: the 20→64→64→1 network above went from about 185 seconds to about 3.6 seconds. Running the resulting SQL got faster too, if less dramatically: on 200,000 rows, an MLP(32,32) query dropped from 0.43s to 0.35s, and an MLP(100,100) from 3.58s to 3.31s.

Same hyperparameters. Same defaults everyone actually reaches for. The difference is entirely in how the SQL gets built, not in what the network computes.

What it can do today

Neural network support in 0.6.0 covers binary classification, multiclass classification, and regression, for both scikit-learn and PyTorch. Five new or updated examples in the repo prove it out: pytorch_fraud_detector.py, pytorch_maintenance_classifier.py, pytorch_demand_regressor.py, pipeline_mlp_classifier.py, pipeline_mlp_regressor.py.

The one worth walking through is the fraud detector, since it’s the shape most teams actually need: a handful of numeric features, a binary “is this fraud” output, trained the same way this kind of model always is.

FEATURES = {
    "amount": orbital.types.DoubleColumnType(),
    "hour": orbital.types.DoubleColumnType(),
    "v1": orbital.types.DoubleColumnType(),
    "v2": orbital.types.DoubleColumnType(),
}

model = torch.nn.Sequential(
    torch.nn.Linear(len(FEATURES), 16),
    torch.nn.ReLU(),
    torch.nn.Linear(16, 8),
    torch.nn.ReLU(),
    torch.nn.Linear(8, 1),
    torch.nn.Sigmoid(),
)

# ... train model normally: Adam, BCELoss, a plain training loop ...

orbital_pipeline = orbital.parse_pytorch_model(model, FEATURES)

From that one orbital_pipeline, two engines:

duckdb_sql = orbital.export_sql("transactions", orbital_pipeline, dialect="duckdb")
postgres_sql = orbital.export_sql("transactions", orbital_pipeline, dialect="postgres")

I ran both, against a real DuckDB and a real Postgres, not just read the generated text. Same four test transactions, three ways to compute a prediction (PyTorch itself, the DuckDB query, the Postgres query), and all three agree to within 1e-5. Both queries land around 10KB, not the tens of megabytes a naive translation would have produced before 0.6.0’s optimizer fix.

That works because Sigmoid and ReLU both compile to plain arithmetic: EXP, a division, a CASE WHEN. Every SQL engine has those. It’s not an accident which activation this example uses, either: Tanh compiles to a native TANH() call instead, and not every dialect implements that the same way, so it’s the one activation in Orbital’s NN support with an actual portability caveat attached.

DuckDB and Postgres are two of the three dialects Orbital actively tests in CI. SQLite is the third. But that’s a testing choice, not an architecture boundary: Orbital doesn’t write dialect-specific SQL at all. Translation ends at ibis. export_sql just hands the finished expression to whichever of ibis’s own backend compilers matches the dialect you ask for, and ibis ships about twenty of those: Snowflake, BigQuery, Trino, MySQL, and so on.

Limits, honestly

What Orbital 0.6.0 handles is feed-forward, fully connected networks: stacks of Linear layers with ReLU, Sigmoid, Tanh, or Softmax in between. That already covers real use cases people put into production: fraud scoring, churn, demand forecasting, risk models. None of those need a CNN or a transformer.

What it doesn’t do yet is exactly what that shape excludes: no convolutions, no recurrence, no attention, no embedding layers for categorical features. If your model needs any of those, Orbital isn’t there yet.

SQL size still grows with the network. A few hidden layers of 64 to 128 neurons land comfortably in the KB range. Wider or deeper than that, it’s worth checking the generated SQL against whatever statement-size limit your engine has, before you deploy it.

That headroom exists at all thanks to Optimizer.preserve_referenced_outputs(), and that mechanism helps every translator in Orbital, not just neural networks. It’s a big enough story on its own that I’ll get back to in a future post.

Try it

pip install orbital[pytorch]

From there, the getting-started guide walks through this same fraud-detector shape end to end, and the examples directory has five more, covering classification and regression in both frameworks.

Orbital speaks PyTorch now. Scikit-learn too. Same query either way.

There are more frameworks already in the works. I won’t name them here, only that the architecture was built for exactly this.

How do you know which model to use and when? Often, it’s not a question of which model is best, but of which model suits your task and needs at a given time. You might switch between models for different projects (package development vs. data analysis), or even within a single project (planning vs. implementation). Different tasks require a different mix of cost, token usage, speed, intelligence, and capabilities.

If you want our most durable, high-level advice: start with the most expensive model you have access to from either OpenAI or Anthropic.¹ Then, once you have a sense of the “ceiling,” try less expensive models and see how they compare. Developing a feel for what’s possible with LLMs will help you make better decisions about trade-offs.

That said, we’ll try to tackle this question more thoroughly in this post.

The model landscape

Currently, Anthropic and OpenAI set the bar for AI capabilities. Google is often discussed as a third frontier lab, though its current model lineup is less competitive.

Anthropic and OpenAI each release a “family” of models: Claude and GPT, respectively. The most capable models in both families are also the slowest and most expensive. Conversely, the least capable models are the cheapest and quickest. Other labs tend to follow this same pattern, releasing a set of models with different trade-offs along the cost-performance curve.

Models within a given family tend to share a similar shape of intelligence, with related capabilities (relative to model size), shortcomings, and idiosyncrasies. For example, Claude Fable 5, Claude Opus 5, and Claude Sonnet 5 often use the same turns of phrase and are quite good at writing code and debugging it. Models from different families can have different shapes of intelligence even when their benchmark scores and prices are very similar. For example, GPT-5.6 Terra and Claude Sonnet 5 are priced similarly and comparably capable at agentic coding, but Terra doesn’t “see” data visualizations as well as Sonnet, while Sonnet doesn’t communicate as clearly as Terra.

Other labs release models that score nearly as high as models from Anthropic and OpenAI on benchmarks. However, these evaluation scores can be deceiving. Labs can now train models to optimize for benchmarks (“benchmaxxing”). These models score well on benchmark-shaped tasks, which tend to be highly autonomous and “tricky,” but can fail to generalize to real-world tasks, which often involve more ambiguous requests. Kimi K3 and GLM 5.2 offer a counterexample: they score well on benchmarks, and we’ve also found them exceptionally well-rounded and intuitive. We recently introduced both models to Posit AI.

In previous newsletters and blog posts, we’ve shared results from targeted evaluations. Here, instead, we offer an approximate and entirely vibes-based comparison of these model families’ characteristics.

For data science applications broadly, you can loosely think of the relevant score as the average of the agentic coding and vision scores we’ve assigned here. Beyond writing R and Python code, models need to be able to interpret plots accurately and faithfully.

Find the model that fits your task

Much of model choice is constrained by the models you have access to. Your organization may only allow a certain provider, or you might not want to pay multiple (possibly expensive!) subscriptions just to have access to all the top models.

If you do have your pick, however, here are some quick guidelines, partially shaped by what models are currently available through Posit AI.

The best open-weights model, especially for data analysis: Kimi K3.

As Simon wrote in the Kimi K3 and GLM 5.2 in Posit AI announcement post, “Kimi K3 is currently the most capable open weights model out there. In our internal testing, it feels somewhere between Opus 5 and Fable 5, and is notably well-rounded compared to other open weights releases.”

It also ranks near the top in bluffbench2 (13.46%, compared with 16.35% for the tied top scorers, Gemini 3.5 Flash and Claude Fable 5), which evaluates models’ abilities to spot subtle data quality issues in visualizations.²

A highly autonomous model for a complex coding or data task when cost and speed aren’t a concern: Claude Opus 5, Claude Fable 5, or GPT-5.6 Sol.

These are the top-of-the-line models from Anthropic and OpenAI. They are expensive and relatively slow, but can be worth using for ambitious or highly autonomous work.

A strong open-weights model for coding when you don’t need vision: GLM 5.2 (from Z.ai).

“GLM 5.2 excels at agentic coding and less so at data analysis tasks.” It is much less expensive than the proprietary models it resembles for coding tasks, but it does not natively support image inputs.

A middle-tier generalist for coding or data analysis: Claude Sonnet 5 or GPT-5.6 Terra. Both are capable across coding and routine data analysis, support vision, and are less expensive than their respective labs’ higher-tier models.

Good plot or image interpretation: One of the Gemini 3.x Flash models (3.7 was released on August 13).

This model series is particularly strong at vision and has performed well on bluffbench (3.5 Flash) and bluffbench2 (3.5 and 3.6 Flash).

Fast answers from an Anthropic model for a task that is not particularly complex: Claude Haiku 4.5.

Fast answers from an open-weights model for a task that is not particularly complex: Gemma 4.

Assorted notes from August 2026

In late summer 2026, a few developments feel notable, but, as with much in the AI world, who knows how long these observations will hold.

• Google Gemini currently does not have any models that perform near the frontier. With the releases of Gemini 2.5 Pro (June 2025) and the Gemini 3 series (November 2025), Google seemed positioned as a third frontier lab. However, it’s been quite a while since they released a frontier model, and they’re now meaningfully behind. As of the time of writing, Google says Gemini 3.5 Pro is still testing with partners. Meanwhile, the company has begun training Gemini 4 and says it is excited by the progress.

• For a year or so, it seemed like Anthropic was solidly ahead of OpenAI in agentic coding. However, since the Claude 4.6 releases, it has become less clear that Anthropic is meaningfully ahead of OpenAI. For one, Anthropic’s current high-end models use newer tokenizers that, according to Anthropic, produce roughly 35% more tokens for the same text than their predecessors. This means the same listed price per token does not necessarily translate to the same cost for comparable text. Further, in our experience, the Claude series has become increasingly token-hungry and difficult to communicate with. At the same time, OpenAI’s models have a notably clear, concise communication style compared with the Claude 5 series. Anthropic is still likely ahead on autonomous, long-horizon coding, but OpenAI no longer feels behind for day-to-day software engineering and data science.³

• There are now a number of balanced, well-rounded open-weights models relatively close to the closed-weights frontier. Kimi K3 and GLM 5.2, in particular, combine strong capabilities with a more pleasant, intuitive feel than their predecessors. While earlier open-weights models were just as close to the frontier in benchmark scores, some newer releases are notably more well-rounded and respond to prompts about as effectively as proprietary models. These releases are also priced at a steep discount compared with the proprietary models they most resemble.

Recent past newsletters

• Keep track of your data exploration with Posit Assistant’s EDA log
• Which models are best at spotting subtle data quality problems?

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Welcome back to another edition of our monthly Positron updates! Each month we share highlights from our latest release and useful resources. Last release we told you about several major features that came out of preview to general availability, including the new notebook editor. This milestone we are excited to share new functionality for SQL support, reproducible authoring, helping you know when packages are missing, and more.

Data Connections

Data Connections is our new way to work with SQL and database-like resources in Positron, from local files and database servers to cloud data warehouses. It is currently available as a preview feature, and you can enable it with the dataConnections.enabled setting. This release more than doubles the number of data sources you can reach. Amazon Redshift, Snowflake, Databricks, and Posit Connect pins join the existing DuckDB, PostgreSQL, and SQLite support.

The panel itself is more capable as well. Refresh and Refresh All reload the tree while preserving what you have expanded, briefly highlighting the rows that were reloaded. Open connections now show an indicator. Collapsing a connection in the UI keeps you connected to your data source, while anything you’ve previewed with the Data Explorer stays still open. When you remove a connection, Positron asks for confirmation and reports how many Data Explorers will close with it.

Data Connections is still an experimental preview, and your feedback continues to shape it. Tell us which databases and warehouses you need, and anything confusing, missing, or broken, in the Data Connections discussion.

Inline output for Quarto

Inline output for .qmd documents came out of preview last release, and this release brings you a substantial round of polish for this way of working. Be aware that the Quarto settings have moved into a dedicated quarto.* namespace with its own group in the Settings editor. The previous positron.quarto.* keys still work but are deprecated, and Positron will prompt you to update your settings.

Before a kernel starts, the kernel status names the interpreter it will start and offers an explicit Start Kernel action. When a cell fails, Fix and Explain buttons send the error to Posit Assistant, matching the Positron notebook experience. The editor also scrolls to reveal output as it is produced, which you can turn off with the new quarto.inlineOutput.autoScroll setting.

Output renders more faithfully as well. The editor gutter now shows which statement is currently executing and per-statement progress. Positron draws images at your display’s pixel ratio, so plots are sharp on retina screens, and Python figures now respect the fig-width and fig-height cell options.

HTML widgets no longer stick in the editor corner when you scroll past them, or trap scrolling instead of letting the document scroll. HTML widgets no longer render as raw HTML after a reload, and collapsed output no longer springs back open when its cell re-runs. Running code in a Quarto document also pins the editor tab now, so Positron does not silently close the document and its session when you open another file.

AI model providers

Positron now reads AI model provider configuration from a single providers.json file rather than a scattered set of settings. The new release will migrate your existing configuration automatically when you start it, and deprecates the authentication.* and positron.assistant.provider.*.enable settings in favor of it. Two new commands give you direct access: Open AI Provider Settings (JSON) opens providers.json from the Command Palette, and Migrate Provider Settings to providers.json runs the migration on demand.

Install missing packages

Positron now notices when your code refers to a package you do not have installed and offers to install it for you. The prompt appears for packages referenced by your scripts and notebooks in both R and Python.

A library() or import call for something missing becomes a single click instead of an error you have to go resolve by hand.

Performance and memory

We continue to invest in the memory footprint, performance, and reliability of Positron. Several components now load only when they are actually needed, and turning off ai.enabled now means Positron never loads some heavy AI-related components at all. We fixed a cluster of long-standing reliability problems around session restarts and lifecycles.

Startup and editing are faster as well. R and Python kernels start much faster on Windows systems with aggressive antivirus software. The interpreter session picker appears immediately instead of waiting for interpreter discovery to finish. We also fixed slow typing, formatting, and saving in long R and Python files.

What’s coming next

• Join our upcoming webinar on August 26 to learn about AI governance in Posit Workbench.
• We are looking forward to posit::conf(2026) next month, where our team will have several sessions on Positron. Register now to join us in person in Houston or virtually from anywhere in the world.

Kimi K3 is currently the most capable open weights model out there. In our internal testing, it feels somewhere between Opus 5 and Fable 5, and is notably well-rounded compared to other open weights releases. It’s priced at the same price-per-token as Claude Sonnet 5.¹

GLM 5.2 excels at agentic coding and less so at data analysis tasks. Because the model is not vision-capable, it cannot ‘see’ plots and thus feels less capable for data science work. Its per-token pricing is also similar to Haiku 4.5, but feels something like Opus 4.6 or Sonnet 5 for agentic coding tasks.

Pricing

The models in Posit AI today, at a glance:

The cost savings are greater than the per-token pricing differences alone might suggest. For one, the Claude 5 series models use a tokenizer that results in substantially more tokens (~35%) per word than Kimi K3’s or GLM 5.2’s tokenizer. Also, users are not billed at a higher rate for Cache writes than ’normal’ input tokens; all input tokens are written to the cache by default, but we can’t make a guarantee that you’ll hit the cache after any specific delay between requests. In practice, we’ve seen that the cache efficiency of conversations with these deployments is slightly lower than with Claude models.

Get started

To get started, open up Posit Assistant and update when prompted! Then, select your model of choice under the Posit AI model provider. If you’re not already a Posit AI subscriber, you can learn more here.

It’s worth saying that we suspect these models will rotate somewhat regularly in the service; as the months go by, we plan to introduce support for new models and deprecate others.

The log summarizes findings for different areas of exploration and keeps track of next steps. To use the log, run the /eda-log slash command after you’ve started the EDA process.

Why we made this

Exploratory data analysis, the open-ended orientation to your data that often comes before anything else, can be a branching, nonlinear process. There are many questions you can ask of your data, and new areas of inquiry can open with each question you ask. Because of this, it is often hard to keep track of what you’ve looked into, where that code lives, and what you want to explore next.

Historically, the EDA process was limited by how quickly you could write code and interpret the output. Coding agents like Posit Assistant lift the first of those constraints. They can carry out EDA far faster than you can on your own, which can exacerbate the issue of keeping track of what you’ve explored.

This speed also introduces a new problem: the point of EDA is typically for you, the human, to understand your data, but coding agents can produce output faster than you can absorb it. If the agent completes an analysis but you haven’t understood the insights in the data, the exploration process hasn’t really happened.

Posit Assistant already has various features that tackle this problem, including an exploratory mode of interaction where it runs shorter turns and stops more frequently to involve the user.

The EDA log is another lightweight tool for the same goal. It keeps a running summary of what you and Posit Assistant have explored, helping your understanding keep pace with the agent’s and giving you a clearer picture of what’s already been done.

Details

Here’s what the EDA log looks like in action:

At a high level:

• When you run /eda-log, Posit Assistant will create a log for the exploration done in the conversation so far. The log then opens in the editor area in Positron.
• The underlying logs are stored as YAML files in .posit/assistant/eda-logs/, next to where plans are stored.
• Posit Assistant is instructed to loosely keep the log up to date as the conversation progresses, but you can also manually trigger an update at any time with the “Refresh” button.
• Clicking the arrow next to an area scrolls you back to the spot in the conversation where that insight originated, so you can revisit the code and context that produced it.
• Suggested next steps appear as clickable text. Clicking one sends it to Posit Assistant as your next message.
• The creation of an EDA log is always user-triggered. Posit Assistant will never create one on its own.
• The feature is currently only in Positron, but will come to RStudio soon.

Recent past newsletters

• Which models are best at spotting data quality problems?
• How to choose between AGENTS.md, skills, and MCP servers

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Huh. It mostly looks normal, except there’s a few points perfectly aligned with what looks to be a “fitted” line. You dig into it a bit more, and realize that the rows from one study site have their cholesterol values imputed. You set them to NA and go along your way.

Would today’s frontier LLMs catch such an oddity? We designed an LLM evaluation to help us answer this question. As it turns out, LLMs mostly miss these sorts of artifacts:

During exploratory or open-ended data analysis, Posit assistant “only runs a few bits of code at a time, then summarizes what it found and suggests next steps”. This is motivated by our stance that, for now, a data scientist should mostly keep pace with and understand what the agent is doing when analyzing data. This stance was initially informed by our observation that last year’s frontier models tended to see what they expected to see when visualizing data. While LLMs have since become much better at interpreting counterintuitive plots, bluffbench2 shows they still lag behind human data scientists in interpreting data visualizations. As such, we are still cautious on the prospect of highly autonomous data agents.

How the eval works

The eval harness is a relatively generic coding agent harness, similar to that of Claude Code or Posit Assistant. The agent has a tool to run R code in a persistent REPL and some vague prompting about data analysis:

You are an AI assistant embedded in the user’s data science IDE. You can read and modify files in the user’s workspace and execute R code in their active session, including rendering plots. Prioritize correctness and clear communication…

In each sample, the agent first carries out a few “lull” turns, making a couple plots and tables unrelated to the eval. Short user messages like “load in the csv in this folder” are decorated with “System Reminders” and other noise like that injected by popular agent harnesses.

After a few turns, the agent is asked to produce a data visualization that includes a subtle visual artifact that could feasibly result from a real data-generating process. The artifacts span a range of realistic data quality issues: stuck sensors, bad joins, points imputed onto a line, swapped columns, pseudoreplication, differing units, etc.

plot bmi vs cholesterol

Tool: Run R code

If the agent mentions the artifact in its follow-up response, it receives a full point. If the agent does not mention the artifact, it can also receive a half point by mentioning it in response to a follow-up user message along the lines of “what do you see in the plot?” If the agent never mentions the artifact, it is graded as incorrect.

Designing the eval

Once we understood the mechanism behind bluffbench, implementing the eval was relatively straightforward. bluffbench demonstrates the degree to which an LLM will ignore evidence shown in a plot in favor of its expectations. So, to implement a given sample, we’d just think of some situation that would elicit a strong prior and then subvert it. For example, a dataset called doug_firs with variables height and circumference; one might expect that, as height increases, so does circumference. So, instead, we did a transformation under the hood that made the relationship parabolic.

A year ago, triggering this prior was enough to frequently ’trick’ the current frontier LLMs.

Slipping a plotted artifact past today’s LLMs is much harder. Any human could ace bluffbench, but only an attentive data analyst would excel at bluffbench2.

In our early work on a successor to bluffbench, we started off with trying to elicit priors in the same way as bluffbench did, but in more realistic, longer-context scenarios. We were surprised to find that the same mechanism broadly doesn’t seem to trick today’s models even in these more realistic settings.¹ We then tried a ‘reverse bluffbench’, where we let the model being evaluated in on the trick, asking it to carry out the transformation itself and then look at the plotted result which was tampered with to show the original relationship. We anticipated that this stronger prior (“I did a thing with an obvious effect”) might cause the models to miss the (re)manipulation, but models reliably noted that the plot looked as if it hadn’t been manipulated.

As such, there isn’t a similar ’trick’ in bluffbench2 per se. The transcripts read like relatively normal data analysis sessions and the plotted artifacts are designed to plausibly result from real data-generating processes. Instead, the eval elicits 1) the ‘shape’ of LLMs’ vision being different than humans’ and 2) the model’s tendencies to perform progress, simulating a data analysis moving along smoothly.

Today’s frontier models are in the mid-teens at best; the top scores belong to Claude Fable 5 and Gemini 3.5 Flash at 16%. That said, we’d caution folks from interpreting the current scores on this eval as ‘LLMs don’t see plots well.’ The plotted artifacts are actually quite subtle, and when they’re made even a bit more marked, models tend to call them out consistently.

For example, the previous version of the scatterplot had a slightly more dense cluster of points:

• Previous
• Current

Opus 4.8 (medium) consistently got this sample right in the previous iteration.

Exploring the eval’s results

At least for now, there’s a loosely linear relationship between the cost to run the eval and the resulting score:

One of the most interesting learnings from examining the logs is a behavioral one. Even though we never request that LLMs introduce modeled results to plots, like fitted lines and confidence intervals with geom_smooth(method = "lm", se = TRUE), they sometimes do so anyway. For example:

In general, adding modeled results to data visualizations without first looking at data is bad practice; it makes it hard to see the data itself. In the eval, adding a modeled result like this seems to substantially lower the chances that the model will notice the plotted artifact:

More bluffbench

If you’d like to learn more about the bluffbench set of evals, take a look at these past posts:

• Introducing bluffbench: Writeup of the the original eval.
• LLMs interpret plots well, until expectations interfere: In-depth post on why models at the time didn’t perform well on bluffbench, as well as various interventions we tried to improve performance.
• LLMs are getting much better at interpreting counterintuitive plots: In spring 2026, bluffbench scores suddenly jumped as models improved.
• It’s (still) very bad to be wrong: Slides from our recent SciPy 2026 talk on building agents for correct, transparent, and reproducible data analysis in light of the bluffbench results.

If you work across Python and R or juggle multiple projects with different dependency needs, environment management is probably one of the most challenging parts of your workflow. Positron comes with several out-of-the-box ways to help simplify environment management. Here are a few tips to streamline your workflow.

1: Understand how Positron discovers your environment

Positron does not just look at your system PATH. For Python, it actively discovers venv, uv, pyenv, and conda environments. If Positron is not seeing your project’s virtual environment, you can add custom search locations via the python.interpreters.include setting, or trigger a manual rescan with Interpreter: Discover All Interpreters. Learn more about Python discovery in the Positron documentation.

For R, discovery works differently. Positron consults various sources to build the list of R interpreters. These include your PATH, R root folders based on specific operating systems, well-known executable locations, and on Windows the registry. You can customize your R discovery through a few settings including positron.r.customRootFolders and positron.r.customBinaries. Learn more about R discovery in the Positron documentation.

2. Use the Interpreter Selector

To begin your first session, click “Start Session” in the top right corner and select your preferred R or Python interpreter. Positron can run multiple R and Python interpreter sessions at once, but only one is ever the active session at a given moment. The Interpreter Selector always shows you the active session and its status (idle, busy, or shut down) and you can use it to switch between or start additional sessions.

3. Install Python with uv

If Positron does not find a usable Python on your machine, it will offer to install Python via uv to help streamline your setup. If you prefer to manage the installation yourself, you can disable uv with the python.allowUvPythonInstall setting. Check out our blog post exploring on-demand Python installation in Positron or explore configurations in our Python installation documentation.

4. Manage packages with the Packages Pane

The Packages Pane in Positron lets you manage the packages installed in your active session. Whether you use pip, uv, conda, pak, base R, or renv, you can browse installed packages and search package repositories. You can also track outdated packages and install, update, or uninstall packages without leaving Positron or writing any code.

5. View package documentation

If you need to learn more about a specific package, the Packages Pane has buttons to navigate to the source documentation or link to the package’s website. You can also pull up the Help Pane for any reference in the Console using the ? operator, including both packages and functions.

6. Start projects with New Folder from Template

The New Folder from Template flow helps you start new projects faster. Instead of running multiple setup commands you can make a few selections and Positron helps you set up an environment directory, version control, directory structure, and an interpreter instance. Learn more about the Python and R templates available in the documentation.

7. Make use of the Command Palette

We recommend taking advantage of the following commands when managing your environments:

• Interpreter: Discover All Interpreters: Find new interpreters
• Interpreter: Select Session: Select a running interpreter session
• Python: Create Environment: Create a new virtual environment

8. Let Posit Assistant help manage your environment

Posit Assistant has knowledge of your R and Python session including the language, version, names, and types of variables in your environment. You can prompt Posit Assistant to set up environments and troubleshoot issues you run into.

Have an idea for how we can improve environment management in Positron? We would love to hear from you in a discussion on GitHub.

Welcome back to another edition of our monthly Positron updates! Each month we share highlights from our latest release and useful resources. Last release we told you that several major features were on track to leave preview in July, and the time is now here! The new Notebook editor, the Packages pane, and Posit Assistant are all now generally available.

Positron Notebook Editor

Positron’s new Notebook editor is now the default experience for Jupyter (.ipynb) files. This release brings a long list of additions, including split-pane editing, cell tag management, and executing a line or selection within a cell with Cmd/Ctrl+Shift+Enter. You can export notebooks to Quarto, Python, or R, and inline PDF rendering makes it easier to work with generated output. If you are coming from JupyterLab, you will find familiar keyboard shortcuts, and we have improved output fidelity for Mermaid diagrams, htmlwidgets, and ipywidgets.

Posit has built on and for the Jupyter ecosystem for years now, and we are excited to have recently joined the Jupyter Foundation.

Packages pane

The Packages pane has also come out of preview. It gives you a live view of the R and Python packages installed in your active session, so you can search, install, update, and remove packages, and jump to their documentation, without leaving Positron.

This release also makes the pane more capable. Clicking a package opens a detail editor with its overview, metadata, and actions, similar to the Extensions pane. Installing a package searches results as you type, and each row has a button that opens the package’s website. Update indicators appear immediately on a new session from a cached snapshot, and Update All Packages reports exactly what changed. Package operations keep your environment consistent too, resolving against a workspace requirements.txt for Python and updating the renv.lock snapshot for R.

Posit Assistant

Posit Assistant, our unified, data-science-focused approach to AI assistance, is now out of preview and generally available; Posit Assistant fully replaces the legacy Positron Assistant. We know the names are similar, and Joe Cheng recently walked through a bit of the story behind how our AI agent tools have evolved.

This release also gives you finer control over AI in Positron. The new ai.enabled setting turns off every Positron AI feature at once, and administrators can enforce it; notebook.ai.enabled does the same for notebooks specifically. The set of language model providers keeps growing, with DeepSeek joining as an experimental provider and Microsoft Foundry reaching general availability. The configuration modal now shows every provider by default with preview and experimental badges.

Data Explorer

The Data Explorer can now open Excel workbooks directly, with no code required, without first needing to load it via Python or R. Sort, filter, and profile columns, switch between worksheets, and toggle whether the first row holds column names. An Open in Excel button opens the workbook in your native spreadsheet application.

This release broadens what you can open in the Data Explorer overall. Backed by a native DuckDB engine, it now also previews compressed CSV, TSV, and Parquet files. Also, a new Open in Data Explorer code action, from the editor lightbulb or Cmd+., opens the data frame under your cursor in R, Python, and Quarto files, so you can jump straight from your code to exploring your data.

R language intelligence

Last release we introduced within-file symbol resolution for R; this release expands it across files. Go to Definition, Find References, and Rename Symbol now work across the packages and scripts in your workspace, and diagnostics and workspace symbols react to external file changes so your language intelligence stays in sync as your project evolves.

What’s coming next

• New in preview this release, Data Connections lets you browse the schemas, tables, views, and indexes of a database, open tables in the Data Explorer, and generate connection code. It currently supports DuckDB, PostgreSQL, and SQLite. Learn how to try it out and tell us what you think in the Data Connections discussion post: which databases and warehouses you need, whether the connection setup is clear, and anything confusing, missing, or broken.
• We are looking forward to posit::conf(2026) in September, where our team will have several sessions on Positron. Register now to join us in person in Houston or virtually from anywhere in the world.