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 Python dependencies.

In the first part of a multi-part blog post series, we'll show the strengths of conda's flexibility and provide a tutorial on how you can install [PostgreSQL][postgresql-org] for local development environments.

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:

• 2PM UTC
• 5PM UTC

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! 🎉 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.

Part 1 of our series "Conda Is Not PyPI: Understanding Conda as a User-Space Distribution".

This is the first article in a three-part series exploring the fundamental differences between conda and PyPI, and why understanding these differences matters for your development workflow. Conda is not just another Python package manager—it's a multi-language, user-space distribution system. In this series, we'll unpack what that means, explore where conda fits in the broader packaging landscape (alongside pip, Docker, and Nix), and show you how to think about conda's role in your toolchain.

Part 1 (this article) clarifies why conda is a distribution, not a package registry, and what that distinction means in practice.

The Q4 2025 conda CLI roadmap highlights improvements coming soon, including sharded repodata support for faster installs, and work to improve integration with PyPI ecosystem.

The conda steering council has decided to archive the conda Discourse forum. Users should refer to our Zulip chat instance instead.

The September 2025 releases included updates to conda and conda-build! 🎉 Both have been released to defaults and conda-forge channels.