PyCharm has added the Agent Environment Coordinator skill to help AI agents use the correct Python interpreter and runtime environment associated with a project, rather than automatically resorting to the system interpreter or installing packages in the wrong location. JetBrains says this issue had been causing broken configurations and polluted environments that required manual fixes by developers.
JetBrains tested six AI models across 28 different programming tasks. The average success rate reached 68% when the agents could not access the project’s actual environment. After this information was made available to them through the skill in PyCharm, the average rose to 98%, without modifying the system’s Python installation.
The Problem Addressed by the Skill
Large language model agents often ignore the virtual environment assigned to a project and use the system interpreter instead. A single machine may contain several interpreters, while projects use different tools to manage their environments, such as uv, Poetry, pip with venv, or conda. The agent typically has no direct way to see these settings configured in PyCharm.
In a typical scenario, the agent might run the command pip install httpx using the wrong interpreter, causing the package to be installed globally. The program then fails, and the environment used on the system becomes polluted. PyCharm already knows the interpreter associated with the project and the tool managing the environment, so JetBrains designed a way for the agent to request this information.
How Agent Environment Coordinator Works
The skill allows the agent to call two functions from PyCharm. get_python_environment returns the correct interpreter for the file or code module being worked on, along with its path and the tool responsible for managing it, whether uv, Poetry, pip with venv, or conda.
If no environment has been configured yet, configure_python_interpreter uses PyCharm’s existing setup mechanism to create an appropriate interpreter, including the same mechanism that suggests creating a .venv directory. As a result, the new interpreter also becomes visible within the integrated development environment.
JetBrains emphasizes that the skill does not intercept or rewrite commands. It only returns environment information, after which the agent decides whether to use it and how to incorporate it into the command. This gives the agent the missing context through PyCharm’s existing mechanisms, without the skill executing commands on its behalf.
Test Results
The test set included everyday tasks related to Python environments, such as running tests, installing a library, inspecting dependencies, and resolving a version conflict. Each task required the agent to select the correct interpreter to execute a command. The reward was also reduced when the agent polluted the system environment, so the high success rate reflects not merely completing the task but also cleaner execution.
JetBrains ran the complete task set three times for each model, with and without the skill, using Harbor, and then calculated the average results. The announced improvements included the following models:
- Sonnet 5: from 73% to 100%.
- Opus 5: from 94% to 100%.
- Codex/GPT-5.6: from 80% to 100%.
JetBrains believes the results indicate that the primary problem was a lack of context, not the models’ inability to complete the tasks. The models did not change, but they stopped guessing which interpreter was required, which explains why the model with the weakest initial result benefited the most. Penalizing the test for polluting the system environment also means that the higher scores were associated with reducing execution residue, not merely increasing the number of successful tasks.
Availability
The skill can be tried in PyCharm 2026.2.1 by opening an AI chat and asking it to install a package or run a command inside the project. Agent Environment Coordinator is one of the skills built into PyCharm. Skills can be browsed and managed from within the development environment, and the library can be expanded through external registries such as public GitHub repositories or by importing skills prepared for Claude Code or Codex.