Install & Compatibility
Where this runs
tested against v0.8.0 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
py 3.10
✕ build_error
✕ build_error
py 3.11
✕ build_error
✕ build_error
py 3.9
✕ build_error
✕ build_error
732MB installed
● package 732MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
Harbor
✓ import harbor
The primary interaction with the Harbor framework is often through its CLI, but modules can be imported for programmatic access to specific functionalities. The top-level `harbor` package is the entry point for most programmatic uses.
Harbor is primarily designed for command-line interaction to manage and execute agent evaluations. The quickstart typically involves using the `harbor` CLI tool to run evaluations against defined datasets and agents within Dockerized environments. This Python snippet demonstrates how you might invoke a basic `harbor` CLI command programmatically. For a full evaluation, users would define tasks, agents, and environments as described in the official Harbor documentation.
import os
import subprocess
# Note: Harbor is primarily CLI-driven for running evaluations.
# This example demonstrates a basic CLI interaction.
# Ensure Docker is running and 'harbor' is installed.
# Create a dummy task file for evaluation
task_content = "print('Hello from Harbor evaluation!')"
with open('hello_task.py', 'w') as f:
f.write(task_content)
print('Created hello_task.py')
# Run a simple evaluation using the Harbor CLI
# For actual evaluations, you would define an agent and environment.
# This command is a placeholder demonstrating CLI invocation.
# A real quickstart would involve defining a dataset and an agent.
try:
# Example of running a simple command, assuming a 'test' subcommand exists
# or a generic 'run' command without specific agent/dataset is possible.
# The official quickstart uses `harbor run` with datasets/environments.
# This generic call might not be directly runnable without setup.
print('Attempting to run a basic harbor CLI command...')
# As per documentation, a simple quickstart involves `harbor run` on a dataset.
# This is a simplified example. For a full eval, see official docs.
result = subprocess.run(
['harbor', 'run', '--help'], # Or a specific dataset/agent for a real run
capture_output=True, text=True, check=True
)
print("Harbor CLI --help output:\n", result.stdout)
except FileNotFoundError:
print("Error: 'harbor' command not found. Ensure Harbor is installed and in your PATH.")
except subprocess.CalledProcessError as e:
print(f"Error running Harbor CLI: {e}")
print(f"Stdout: {e.stdout}")
print(f"Stderr: {e.stderr}")
finally:
# Clean up dummy task file
if os.path.exists('hello_task.py'):
os.remove('hello_task.py')
print('Cleaned up hello_task.py')
Errors
Common errors & fixes
ModuleNotFoundError: No module named 'harbor'
The `harbor` package was not installed or is not accessible in the current Python environment, or a different package with a similar name (like `harborapi`) was installed instead.
fixEnsure you have correctly installed the agent evaluation framework: `pip install harbor` or `uv tool install harbor`.
docker: command not found
The Docker command-line client is not installed or not in your system's PATH, which is required for Harbor to provision evaluation environments.
fixInstall Docker Desktop (for macOS/Windows) or Docker Engine (for Linux) and ensure it's properly configured and added to your system's PATH. Verify installation with `docker --version`.
Error: 'harbor' command not found. Ensure Harbor is installed and in your PATH.
The `harbor` CLI tool, which is part of the Python package, is not accessible from your shell's PATH, or the installation was incomplete.
fixVerify that `pip install harbor` completed successfully. If using a virtual environment, ensure it's activated. Check your system's PATH configuration to include the directory where Python packages install their scripts (e.g., `~/.local/bin` or a virtual environment's `bin/Scripts` directory).
Upgrade
Version history
0.13.2latest on PyPI · released Jun 11, 2026
Audit
Dependencies
pythonrequiredRequires Python 3.12 or newer for compatibility.
dockerrequiredEvaluations run in sandboxed Docker environments, requiring Docker to be installed and running on the system.