The May 2026 releases span conda, conda-build, conda-libmamba-solver, conda-pypi, and conda-rattler-solver! 🎉 You'll find them on the main and conda-forge channels.
For conda, lockfiles are a first-class workflow now, the CLI is lighter on everyday commands, and there is more under the hood for channel metadata and future dependency specs. Step-by-step opt-in for beta previews—Rattler and conda-pypi—is on New features to try.
This post is adapted from a talk I gave at PyConDE & PyData 2026.
In it, I walk through the evolution of Python packaging, from distutils to pip, and later to conda, and how these tools emerged in response to different needs within the Python community.
The ideas in this post are based on my experience as a maintainer in the conda ecosystem and conversations I’ve had with users and contributors over time. A recurring theme in those discussions is the tendency to frame conda and pip as competing tools.
What I’ve found more useful is to view them instead as parts of different ecosystems, shaped by different constraints and priorities. That perspective also helps explain why mixing them can sometimes lead to unexpected behavior in practice.
This post is an attempt to share that perspective in a more structured form.
A vision for conda in 2026 is a package manager that is fast (real-time progress, sharded repodata, lighter shell integration, Rattler integration), trusted (better error messages, smarter confirmation friction, streaming output), and delightful (sensible defaults, helpful suggestions, intentional visuals, accessibility). Beyond the core CLI, the roadmap includes native PyPI wheel support, declarative conda.toml project environments, single-binary installers, and a comprehensive API for IDE and agent integrations.
The March 2026 releases included updates to conda, conda-build, conda-libmamba-solver, conda-content-trust, conda-rattler-solver, conda-standalone, constructor, and conda-pypi! 🎉 All of these have been released to both defaults and conda-forge channels.
Over the last few months, the conda steering council has put significant resources into reducing the standardization debt in the conda ecosystem. This effort culminated in the approval of 10 new CEPs covering the foundational pillars of conda. This is a turning point in our community that is worth celebrating with a blog post!
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! 🎉 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—exploring 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—making 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.