Conda CLI Roadmap Updates: Q1, 2026
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.
Highlights from Q4 2025
A quick look at roadmap items shipped last quarter and available to try today. See previous update here.
Faster Performance with Sharded Repodata (Beta)
What’s new: We released beta support for sharded repodata (CEP 16) in conda CLI, alongside support for the conda-forge channel on Anaconda.org. This means you can now enable sharded repodata in conda CLI and use it with conda-forge end-to-end for faster package metadata fetching!
What it does: Instead of downloading a single, large metadata file from each channel before solving, sharded repodata fetches metadata in smaller pieces as needed. On conda-forge, this results in roughly 10× faster metadata fetching and about 90% less bandwidth usage in benchmarked scenarios. Full benchmarks and technical details are available in the announcement blog post: https://conda.org/blog/sharded-repodata-improvements
How to try it:
Opt-in via conda CLI when using conda-forge:
conda install --name base 'conda-libmamba-solver>=25.11.0'conda config --set plugins.use_sharded_repodata true
Pixi and rattler-build already use sharded repodata by default, no opt-in required.
Share feedback: We need your feedback before we can make sharded repodata the default in conda. If you try it and see performance improvements, great. If you hit regressions or edge cases, that's equally useful. Share what you're seeing on Zulip or open a GitHub issue.
Safer PyPI installs with conda-pypi plugin
What’s new: The conda-pypi
plugin is where our ongoing PyPI interoperability work is taking shape. The plugin provides a supported and explicit way to install PyPI packages inside conda environments. It's a safer alternative to the ad hoc pip install commands that everyone uses but that often leave environments fragile and hard to recover when something goes wrong.
What it does: The conda pypi subcommand makes PyPI package installs intentional and visible. It prefers conda packages for dependencies when they exist in configured channels, supports editable installs for development workflows, and respects the EXTERNALLY-MANAGED marker from PEP 668 to reduce accidental environment corruption.
How to get started: You can install the plugin and experiment with it now. Full documentation is available for supported workflows and usage.
This is actively evolving, and it's not production-ready yet! The real value of trying it now is helping us understand edge cases, behavior quirks, and whether these workflows actually fit how you work.
Share feedback: Please report issues or feature ideas on the conda-pypi GitHub repository.
What We're Building in Q1 2026
These areas are active work in progress and will evolve as design and implementation continue.
Reproducible Environments with Native Lockfile Support
What we’re working on: We're adding native support in conda CLI for working with lockfiles. This includes the ability to generate a lockfile from an existing environment and to create a new environment directly from a lockfile using conda.
Why this matters: environment.yml files describe intended dependencies, but recreating an environment requires solving against the current state of channels and metadata. Over time, or across platforms, this can lead to different results from the same input file. Run conda create six months later, on a different platform, or after channel updates, and you may get a different environment. Worse, it might fail to recreate at all.
Native lockfile support will allow conda users to recreate the exact same environment deterministically without re-solving, get identical results across machines, platforms, and points in time, use lockfiles as the source of truth for CI, production, and shared workflows, and work directly with common lockfile formats in the ecosystem, such as conda-lock and pixi/rattler.lock
Current status: This work is in progress. You can follow along and engage on our GitHub project board.
Support for Python Wheels in Repodata
What we’re working on: We’re working on adding support for pure-Python wheels to conda’s package index (repodata). This work focuses on making metadata about Python packages from PyPI available to conda’s dependency solver, rather than introducing a new install workflow.
Why this matters: By representing Python wheels in repodata, conda can take these packages into account during solving. Instead of treating PyPI packages as opaque additions that happen outside of conda's model, conda can reason about them alongside conda packages. This means better conflict detection, more accurate dependency resolution, and a clearer picture of what's actually in your environment.
We're starting with pure-Python wheels because they're the most straightforward case. It's a focused scope that lets us validate the design and implementation before expanding further.
Current status: This work is active in Q1 and includes both design and implementation.
The proposed approach is described in a draft CEP, Repodata Wheel Support. Feedback on the design is especially valuable at this stage. Implementation progress and related discussion are tracked on our GitHub project board.
Get Involved!
This roadmap update reflects work that is already underway, but priorities are always evolving based on community feedback. The most effective ways to get involved are:
- File GitHub issues when something doesn’t work as expected or when you have an idea for improvement
- Join conda community calls or participate in conversations on Zulip
- Contribute code, documentation, or reviews where you see gaps or opportunities
We’ll continue sharing progress through quarterly roadmap updates. If you have thoughts on what’s useful in these posts or what could be clearer, please let us know in this Zulip topic.
None of this work happens without the conda community. Thank you for your feedback, contributions, and patience as we build together.