gemini-cli/evals/README.md

Behavioral Evals

Behavioral evaluations (evals) are tests designed to validate the agent's behavior in response to specific prompts. They serve as a critical feedback loop for changes to system prompts, tool definitions, and other model-steering mechanisms.

Why Behavioral Evals?

Unlike traditional integration tests which verify that the system functions correctly (e.g., "does the file writer actually write to disk?"), behavioral evals verify that the model chooses to take the correct action (e.g., "does the model decide to write to disk when asked to save code?").

They are also distinct from broad industry benchmarks (like SWE-bench). While benchmarks measure general capabilities across complex challenges, our behavioral evals focus on specific, granular behaviors relevant to the Gemini CLI's features.

Key Characteristics

• Feedback Loop: They help us understand how changes to prompts or tools affect the model's decision-making.
  • Did a change to the system prompt make the model less likely to use tool X?
  • Did a new tool definition confuse the model?
• Regression Testing: They prevent regressions in model steering.
• Non-Determinism: Unlike unit tests, LLM behavior can be non-deterministic. We distinguish between behaviors that should be robust (ALWAYS_PASSES) and those that are generally reliable but might occasionally vary (USUALLY_PASSES).

Creating an Evaluation

Evaluations are located in the evals directory. Each evaluation is a Vitest test file that uses the evalTest function from evals/test-helper.ts.

evalTest

The evalTest function is a helper that runs a single evaluation case. It takes two arguments:

1. policy: The consistency expectation for this test ('ALWAYS_PASSES' or 'USUALLY_PASSES').
2. evalCase: An object defining the test case.

Policies

Policies control how strictly a test is validated. Tests should generally use the ALWAYS_PASSES policy to offer the strictest guarantees.

USUALLY_PASSES exists to enable assertion of less consistent or aspirational behaviors.

• ALWAYS_PASSES: Tests expected to pass 100% of the time. These are typically trivial and test basic functionality. These run in every CI.
• USUALLY_PASSES: Tests expected to pass most of the time but may have some flakiness due to non-deterministic behaviors. These are run nightly and used to track the health of the product from build to build.

EvalCase Properties

• name: The name of the evaluation case.
• prompt: The prompt to send to the model.
• params: An optional object with parameters to pass to the test rig (e.g., settings).
• assert: An async function that takes the test rig and the result of the run and asserts that the result is correct.
• log: An optional boolean that, if set to true, will log the tool calls to a file in the evals/logs directory.

Example

import { describe, expect } from 'vitest';
import { evalTest } from './test-helper.js';

describe('my_feature', () => {
  evalTest('ALWAYS_PASSES', {
    name: 'should do something',
    prompt: 'do it',
    assert: async (rig, result) => {
      // assertions
    },
  });
});

Running Evaluations

First, build the bundled Gemini CLI. You must do this after every code change.

npm run build
npm run bundle

Always Passing Evals

To run the evaluations that are expected to always pass (CI safe):

npm run test:always_passing_evals

All Evals

To run all evaluations, including those that may be flaky ("usually passes"):

npm run test:all_evals

This command sets the RUN_EVALS environment variable to 1, which enables the USUALLY_PASSES tests.

Reporting

Results for evaluations are available on GitHub Actions:

• CI Evals: Included in the E2E (Chained) workflow. These must pass 100% for every PR.
• Nightly Evals: Run daily via the Evals: Nightly workflow. These track the long-term health and stability of model steering.

Nightly Report Format

The nightly workflow executes the full evaluation suite multiple times (currently 3 attempts) to account for non-determinism. These results are aggregated into a Nightly Summary attached to the workflow run.

How to interpret the report:

• Pass Rate (%): Each cell represents the percentage of successful runs for a specific test in that workflow instance.
• History: The table shows the pass rates for the last 10 nightly runs, allowing you to identify if a model's behavior is trending towards instability.
• Total Pass Rate: An aggregate metric of all evaluations run in that batch.

A significant drop in the pass rate for a USUALLY_PASSES test—even if it doesn't drop to 0%—often indicates that a recent change to a system prompt or tool definition has made the model's behavior less reliable.

Fixing Evaluations

If an evaluation is failing or has a regressed pass rate, you can use the /fix-behavioral-eval command within Gemini CLI to help investigate and fix the issue.

/fix-behavioral-eval

This command is designed to automate the investigation and fixing process for failing evaluations. It will:

1. Investigate: Fetch the latest results from the nightly workflow using the gh CLI, identify the failing test, and review test trajectory logs in evals/logs.
2. Fix: Suggest and apply targeted fixes to the prompt or tool definitions. It prioritizes minimal changes to prompt.ts, tool instructions, and modules that contribute to the prompt. It generally tries to avoid changing the test itself.
3. Verify: Re-run the test 3 times across multiple models (e.g., Gemini 3.0, Gemini 3 Flash, Gemini 2.5 Pro) to ensure stability and calculate a success rate.
4. Report: Provide a summary of the success rate for each model and details on the applied fixes.

To use it, run:

gemini /fix-behavioral-eval

You can also provide a link to a specific GitHub Action run or the name of a specific test to focus the investigation:

gemini /fix-behavioral-eval https://github.com/google-gemini/gemini-cli/actions/runs/123456789

When investigating failures manually, you can also enable verbose agent logs by setting the GEMINI_DEBUG_LOG_FILE environment variable.

It's highly recommended to manually review and/or ask the agent to iterate on any prompt changes, even if they pass all evals. The prompt should prefer positive traits ('do X') and resort to negative traits ('do not do X') only when unable to accomplish the goal with positive traits. Gemini is quite good at instrospecting on its prompt when asked the right questions.