[Conda] Error: CondaDependencyError — failed to resolve package dependencies — How to Fix It

Summary

The CondaDependencyError: failed to resolve package dependencies occurs when Conda’s solver cannot find a set of compatible package versions that meet all requirements. It often arises from conflicting version constraints, mixed channels (like defaults and conda-forge), or outdated metadata. The solution is to relax constraints, update Conda, or broaden your available repositories.

Context

Conda uses a dependency solver to ensure every installed package is compatible with the others in your environment. When two or more dependencies have incompatible version requirements — or when metadata is outdated — the solver fails. This issue is especially common when using strict version pins, old YAML environment files, or mixing channels with divergent dependency trees.

Probable Cause

  • Strict version pins such as python=3.9.0 or numpy=1.21.0 blocking compatible builds.
  • Mixing the defaults and conda-forge channels without priority control.
  • Outdated or corrupted Conda dependency metadata.
  • Platform-specific packages included in a cross-platform YAML (e.g., pywin32 on Linux).
  • Removed or deprecated builds no longer available in repositories.

Quick Fix

Apply the following steps to resolve dependency conflicts:

  1. Update Conda and its dependency metadata:
conda update conda
conda update --all
  1. Add the conda-forge channel and relax channel priority:
conda config --add channels conda-forge
conda config --set channel_priority flexible
  1. Relax version pins in your command or YAML file:
# Instead of:
python=3.9.0
numpy=1.21.0

# Use:
python=3.9
numpy
  1. Install packages incrementally to identify the conflict:
conda install numpy
conda install pandas
conda install scipy

If one of these fails, that’s the conflicting package.

  1. Try solving with Mamba for faster and clearer diagnostics:
conda install -n base -c conda-forge mamba
mamba install <package>
  1. If the environment is being created from YAML, remove OS-specific dependencies such as pywin32 or libgcc-ng.

Full Example

Example error:

CondaDependencyError: failed to resolve package dependencies
UnsatisfiableError: The following specifications were found to be incompatible
 - python=3.9.0
 - tensorflow

Fix:

conda update conda
conda config --add channels conda-forge
conda config --set channel_priority flexible
conda create -n ml_env python=3.9 tensorflow

If this still fails, try a minimal environment first:

conda create -n base_test python=3.9 numpy

Pitfalls & Debug

  • Symptom: “Unsatisfiable dependencies” → Fix: Relax strict pins and use flexible channel priority.
  • Symptom: YAML-based environment creation fails → Fix: Remove OS-specific or legacy packages.
  • Symptom: Repeated solver timeouts → Fix: Use mamba instead of Conda for faster solving.
  • Symptom: PackagesNotFoundError → Fix: Check channel availability using conda search <package>.

Validation & Next Steps

After adjustments, confirm that Conda resolves dependencies correctly: 

conda search python
conda info

Recreate or install your environment cleanly: 

conda create -n newenv python=3.11 pandas numpy

If successful, the solver will complete without dependency errors and your environment will initialize normally.

Sources

Anaconda Documentation — Package specifications and solver
conda-forge community — Tips for resolving dependency conflicts
Mamba documentation — high-performance Conda solver
Stack Overflow — troubleshooting UnsatisfiableError and solver issues

Labels: Tool/Conda, OS/Windows-macOS-Linux, Topic/Dependency-Solver