Workflow orchestration for Inspect AI that enables you to run evaluations at scale with repeatability and maintainability.
As evaluation workflows grow in complexity—running multiple tasks across different models with varying parameters—managing these experiments becomes challenging. Inspect Flow addresses this by providing:
- Declarative Configuration: Define complex evaluations with tasks, models, and parameters in type-safe schemas
- Repeatable & Shareable: Encapsulated definitions of tasks, models, configurations, and Python dependencies ensure experiments can be reliably repeated and shared
- Incremental Execution: Add new models, tasks, or configurations to existing results without re-running completed work
- Parameter Sweeping: Matrix patterns for systematic exploration across tasks, models, and hyperparameters
Inspect Flow is designed for researchers and engineers running systematic AI evaluations who need to scale beyond ad-hoc scripts.
Before using Inspect Flow, you should:
- Have familiarity with Inspect AI
- Have an existing Inspect evaluation or use one from inspect-evals
pip install inspect-flowCreate a config.py file:
from inspect_flow import FlowJob, FlowTask
FlowJob(
log_dir="logs",
tasks=[
FlowTask(
name="inspect_evals/gpqa_diamond",
model="openai/gpt-4o",
),
FlowTask(
name="inspect_evals/mmlu_0_shot",
model="openai/gpt-4o",
),
],
)To run the evaluations, run the following command in your shell. This will create a virtual environment for this job run and install the dependencies. Note that model dependencies (like the openai Python package) are inferred and installed automatically.
flow run config.pyThis will run both tasks and display progress in your terminal.
Often you'll want to evaluate multiple tasks across multiple models. Rather than manually defining every combination, use tasks_matrix to generate all task-model pairs:
from inspect_flow import FlowJob, tasks_matrix
FlowJob(
log_dir="logs",
tasks=tasks_matrix(
task=[
"inspect_evals/gpqa_diamond",
"inspect_evals/mmlu_0_shot",
],
model=[
"openai/gpt-5",
"openai/gpt-5-mini",
],
),
)To preview the expanded config before running it, you can run the following command in your shell to ensure the generated config is the one that you intend to run.
flow config matrix.pyThis command outputs the expanded configuration showing all 4 task-model combinations (2 tasks × 2 models).
log_dir: logs
dependencies:
- inspect-evals
tasks:
- name: inspect_evals/gpqa_diamond
model:
name: openai/gpt-5
- name: inspect_evals/gpqa_diamond
model:
name: openai/gpt-5-mini
- name: inspect_evals/mmlu_0_shot
model:
name: openai/gpt-5
- name: inspect_evals/mmlu_0_shot
model:
name: openai/gpt-5-minitasks_matrix and models_matrix are powerful functions that can operate on multiple levels of nested matrixes which enable sophisticated parameter sweeping. For example, you can explore different reasoning efforts across models with models_matrix, or use configs_matrix for even more concise parameter sweeping.
To run the config:
flow run matrix.pyWhen complete, you'll find a link to the logs at the bottom of the task results summary. To view logs interactively, run:
inspect viewSee the following articles to learn more about using Flow:
- Usage: End-to-end guide for developing and running a Flow job
- Reference: Detailed documentation on the Flow Python API and CLI commands
Visit the documentation site for complete documentation.
To work on development of Inspect Flow, clone the repository and install with the -e flag and [dev, doc] optional dependencies:
git clone https://github.com/meridianlabs-ai/inspect_flow
cd inspect_flow
uv sync
source .venv/bin/activateOptionally install pre-commit hooks via
make hooksRun linting, formatting, and tests via
make check
make test

