condastats is a command-line tool and Python library for querying download statistics of conda packages from the Anaconda public dataset. The project hadn't seen a release since August 2022, so we spent some time updating it to work with current Python and pandas versions, cleaning up the codebase, rewriting the documentation, and adding an interactive browser demo. The result is condastats 0.4.2 -- here's what's new and how to use it.
The January 2026 releases included updates to conda, conda-build, and conda-pack! 馃帀 All of these have been released to both defaults and conda-forge channels.
The Q1 2026 Conda CLI roadmap update highlights faster performance, safer ways to work with PyPI packages, and progress toward more reproducible environments.
Welcome to another quarterly update on what shipped in conda CLI and what we're building next. These posts complement our project board by pulling out the highlights and showing where your feedback matters most.
Part 1 of the "You Can Do That with conda?" series鈥攅xploring unexpected capabilities of conda beyond Python packages.
Conda has long been the driver of data science workflows because of its unique ability to manage the complexities around Python packaging's diverse dependency requirements. It's precisely because of this that conda is also able to handle managing much more than just Python dependencies.
In this tutorial, we'll show the strengths of conda's flexibility and provide a guide on how you can install PostgreSQL for local development environments. Installing PostgreSQL this way offers several advantages: no root or admin permissions are required, the installation is isolated and reproducible, and your database can be version-controlled alongside other project dependencies鈥攎aking it a lighter-weight alternative to container-based solutions like Docker.
Modern AI agents like Claude, Cursor, OpenCode, and Zed can fetch web content, run shell commands, and even install packages. But they lack direct access to the rich, structured metadata embedded in conda packages. This information is essential for solving complex packaging problems. conda-meta-mcp provides that missing link.
Starting in 2026, the community calls for conda and conda-forge have merged in a single timeslot. Instead of alternating weeks, from now on, both communities will share the same space every Wednesday. There are two rotating timeslots:
The first meeting in 2026 will take place on January 7th, at 5PM UTC. For more details consult the calendar.
The meeting minutes will be available in both conda.org and conda-forge.org, in the usual places.
We're excited to announce a new beta feature in conda called sharded repodata. This optimized repodata format makes environment solves faster by reducing the time spent fetching package metadata. Conda-forge is already serving sharded repodata, so you can try it immediately when using conda with conda-forge. In this post, we'll show you how to enable it, explain how the work came together across the ecosystem, and share the performance improvements you can expect in everyday use.
The November 2025 releases included updates to conda, conda-build, and conda-libmamba-solver! 馃帀 All of these have been released to both defaults and conda-forge channels.
Part 3 of our series "Conda Is Not PyPI: Understanding Conda as a User-Space Distribution".
In Part 1, we explained why conda is not just another Python package manager. In Part 2, we placed conda in the broader packaging spectrum, showing how it differs from pip, Docker, and Nix.
Now we turn to what makes conda practical and powerful: reproducibility, automation, layered workflows, and rolling distribution.
Understanding conda's theoretical advantages is one thing. Seeing how they translate into real-world benefits is another. In this final article, we explore how conda's design enables teams to build reliable, maintainable software environments that scale from personal projects to enterprise systems.
We'll cover how conda packages encode provenance, how lockfiles ensure reproducibility across time and teams, and how intelligent layering with pip/npm gives you the best of both worlds.
This is Part 2 of our series "Conda Is Not PyPI: Understanding Conda as a User-Space Distribution".
In Part 1, we explained why conda is not just another Python package manager. Conda packages are distribution units, not libraries. Environments are essentially mini distributions in user-space.