- Skill ID
- google/agents-cli/google-agents-cli-eval
- Publisher
- Repository
- agents-cli
- Installs
- 11,135
- Files
- 7
- Synced
- Sep 16, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
google/agents-cli/google-agents-cli-evalInstalls these files- references/advanced-commands.md
- references/builtin-tools-eval.md
- references/dataset_schema.md
- references/metrics-guide.md
- references/multimodal-eval.md
- references/user-simulation.md
- SKILL.md
What this skill tells the agent
Agent Evaluation Guide
Requires:agents-cli(uv tool install google-agents-cli) — install uv first if needed.
Scaffolded project? If you used/google-agents-cli-scaffold, you already haveagents-cli eval run(chainsgenerate+grade),tests/eval/datasets/, andtests/eval/eval_config.yaml. Start with executingeval runand iterate from there.
Reference Files
| File | Contents |
|---|---|
references/dataset_schema.md | Canonical EvaluationDataset schema — all field types, JSON examples for single-turn / multi-turn / multi-agent, common mistakes |
references/metrics-guide.md | Complete metrics reference — all built-in metrics, match types, custom metrics, judge model config |
references/user-simulation.md | Dynamic conversation testing — eval dataset synthesize flags, what scenarios are, compatible metrics |
references/builtin-tools-eval.md | google_search and model-internal tools — trajectory behavior, metric compatibility |
references/advanced-commands.md | Opt-in commands: eval analyze, eval optimize, eval submit / eval results |
references/multimodal-eval.md | Multimodal inputs — eval dataset schema, built-in metric limitations, custom evaluator pattern |
The Quality Flywheel
Improving agent quality is iterative. The 4 stages below describe the loop. Each stage has a Default path (you, the coding agent, do the work directly) and an Opt-in CLI command that delegates to the Agent Platform Eval Service for better quality and scale.
1. Prepare Data
Default: Use or edit the scaffolded tests/eval/datasets/basic-dataset.json to define single-turn eval inputs. Start with 1–2 cases.
Opt-in (ADK projects): agents-cli eval dataset synthesize: user-simulate multi-turn datasets when you lack data; its output already includes traces, so Stage 2 collapses to agents-cli eval grade alone. See Eval Commands and references/user-simulation.md.
2. Run the Eval (always run)
Default: agents-cli eval run runs the agent over the dataset and grades the traces, writing results_<ts>.{json,html} to artifacts/grade_results/.
Decoupled form: eval generate then eval grade, for a custom traces location, re-grading without re-running the agent, or traces from synthesize (eval grade alone).
3. Analyze Failures
Default: Open the latest artifacts/grade_results/results_<ts>.html (or .json) and identify failed metrics — see What to fix when scores fail below for the fix table.
Opt-in: agents-cli eval analyze, LLM-based failure clustering; prefer when you have 10+ failing cases and want categorized failure modes. See references/advanced-commands.md.
4. Optimize & Code Fix
Default: Edit the agent — adjust prompts, tool descriptions, instructions, or eval dataset based on the failure analysis. See What to fix when scores fail below for the failure → fix mapping.
Opt-in (ADK projects): agents-cli eval optimize runs ADK GEPA prompt optimization against a target metric (see references/advanced-commands.md). Suitable for prompt-only failures. The optimized prompt appears in the command output; capture it and apply it to the agent. For the full per-iteration trace, set print_detailed_results: true in your optimization config file.
Long-running and expensive. GEPA optimization makes many LLM calls and can take a long time. Do not run it unless the user explicitly asks for prompt optimization. When you do run it, iterate as far as possible with manual fixes first, then run a single final eval optimize — never loop on this command.Running the loop
Iterate stages 2 → 3 → 4 → 2 (with synthesize, re-run Stage 1 each pass, then eval grade). After each fix, run agents-cli eval compare <prev_results>.json <new_results>.json to confirm the target metric improved without regressing others. Expect 5–10+ iterations per case before it passes, which is normal. Only after a case passes should you expand coverage with more eval cases.
When doing 5+ iterations, maintain a task list of which cases are fixed, which are still failing, and what fixes you've tried. Prevents re-attempting the same fix.
Hold cases back. Keep a slice of cases out of the loop and grade them only when you think you're done — otherwise you can't tell a fix that generalizes from one fitted to the cases you iterated against.
Shortcuts That Waste Time
Recognize these rationalizations and push back — they always cost more time than they save:
| Shortcut | Why it fails |
|---|---|
| "I'll lower the bar so it passes" | Lowering the bar hides real failures. If the agent can't meet the bar, fix the agent, don't move the bar. |
| "This eval case is flaky, I'll skip it" | Flaky evals reveal non-determinism in your agent. Fix with temperature=0, rubric-based metrics, or more specific instructions — don't delete the signal. |
| "I just need to fix the eval dataset, not the agent" | If you're always adjusting expected outputs, your agent has a behavior problem. Fix the instructions or tool logic first. |
| "I'll iterate until every case I have passes" | Nothing is left to detect overfitting to your own cases. See Hold cases back above. |
Choosing the Right Metrics
Pick built-in metrics by what you want to measure. Only multi_turn_task_success, multi_turn_trajectory_quality, and multi_turn_tool_use_quality accept multi-turn traces; every other built-in 400s on one. When no built-in fits, write a custom metric (see Evaluation Configuration Schema below).
| Goal | Recommended built-in metrics |
|---|---|
| Did the agent achieve the user's goal? (catch-all for multi-turn agents) | multi_turn_task_success |
| Was the agent's reasoning path logical and efficient? | multi_turn_trajectory_quality |
| Quality of tool / function calling across turns | multi_turn_tool_use_quality |
| Final response quality (no ground-truth reference needed) | final_response_quality |
| Factual grounding (catch hallucinated claims, e.g., RAG agents) | hallucination, or grounding when the case carries a context field |
| Safety policy compliance | safety |
| Match against a golden answer | final_response_match (needs reference on the case) |
| Different pass/fail criteria per case | Put them on the case as rubric_groups and grade with a managed rubric metric. See references/dataset_schema.md (Per-Case Rubrics). |
| Domain-specific check no built-in covers | Write a custom LLMMetric (LLM-judge) or CodeExecutionMetric (deterministic Python). See Evaluation Configuration Schema below. |
Run agents-cli eval metric list to see all available built-ins. For full metric definitions and rubric details, see the Agent Platform metric docs and references/metrics-guide.md.
What to fix when scores fail
After agents-cli eval run completes, inspect the latest artifacts/grade_results/results_<timestamp>.json (or open the .html file) for per-case scores and judge rationales, the input to every fix decision below.
| Failure | What to change |
|---|---|
multi_turn_task_success low | The agent isn't completing the user's goal — fix orchestration, missing tool calls, premature termination, or wrong tool selection |
multi_turn_trajectory_quality low | The agent reaches the goal inefficiently or takes wrong steps — refine planning prompts, tighten instruction order, or remove redundant tool calls |
multi_turn_tool_use_quality low | Fix tool descriptions, parameter docstrings, or agent instructions for tool selection |