---

name: eval-guide description: "Use when writing eval code, configuring eval infrastructure, creating golden datasets, setting up PromptRegistry, authoring CI eval gates, or working with any eval tool: DeepEval, Ragas, Giskard OSS v3, Promptfoo, Langfuse, Arize Phoenix, adk eval, ADK User Simulation, Vertex GenAI Eval. Covers per-agent accuracy thresholds, CI tier structure (R1-R4), MCP eval suites, golden dataset structure, and PromptRegistry architecture. Also covers pytest harness configuration (asyncio_mode, InMemoryRunner, parametrize-over-golden)." allowed-tools: Bash, Read, Write, Edit metadata: triggers: deepeval, ragas, giskard, promptfoo, langfuse, phoenix, PromptRegistry, golden dataset, evalset, eval-smoke, eval-adk, redteam, MCPUseMetric, Faithfulness, ToolCallAccuracy, HallucinationsV1, tests/golden, dataset_manifest, seed-prompts, R1 gate, R2 nightly, R3 pre-release, R4 canary, ci tier, eval threshold, ArenaGEval, GeminiModel judge, set_default_generator, promptfooconfig.yaml, Scenario, Suite, make eval-all-local, make redteam-promptfoo, make giskard-scan, make mcp-eval-all, make seed-prompts-local, eval-reviewer, per-agent threshold, golden case, asyncio_mode, InMemoryRunner, pytest-asyncio, dataset_manifest.yaml, diff_langfuse_prompts, emergency pack, prompt registry, prompt lifecycle related-skills: google-adk, adk-eval-guide, python-dev, adk-observability-guide domain: agentic-ai role: specialist scope: evaluation output-format: code last-reviewed: "2026-04-28"

Eval Guide

Iron Law

NEVER generate eval tool code from memory. ALWAYS query Context7 MCP for the official API before writing any eval code. Every metric class name, constructor signature, and YAML provider ID must be verified against current official docs — these APIs change between minor versions.

Prefer LangChain-free eval paths. Ragas, Giskard, and Promptfoo all have LangChain-free paths — prefer them to reduce dependency surface and avoid version conflicts.

Consistent LLM judge. Best practice: use the same LLM provider as your main stack for LLM-as-judge to reduce vendor sprawl. For Gemini-based stacks: GeminiModel("gemini-2.5-flash") for DeepEval, Generator(model="google/gemini-3.1-flash") for Giskard, google:gemini-2.5-pro for Promptfoo.

Dispatch eval-reviewer agent after writing any eval code — same mandate as dispatching adk-reviewer after ADK agent code.

Documentation Sources — Query Context7 BEFORE Writing Any Tool Code

Tool	Context7 query	Fallback
DeepEval	deepeval	https://docs.confident-ai.com/docs
Ragas	ragas	https://docs.ragas.io/en/latest
Giskard OSS v3	giskard	https://docs.giskard.ai/en/latest
Promptfoo	promptfoo	https://www.promptfoo.dev/docs
Langfuse	langfuse	https://langfuse.com/docs
Arize Phoenix	arize-phoenix	https://docs.arize.com/phoenix
ADK Eval	google-adk (adk-docs MCP)	https://google.github.io/adk-docs/evaluate
Vertex GenAI Eval	google-cloud-aiplatform	https://cloud.google.com/vertex-ai/generative-ai/docs/evaluate
pytest-asyncio	pytest-asyncio	https://pytest-asyncio.readthedocs.io

Reference Files

File	When to use
reference/deepeval-patterns.md	MCPUseMetric, GeminiModel, 15 confirmed metric classes, ArenaGEval A/B testing
reference/ragas-patterns.md	ToolCallAccuracy, Faithfulness, ContextPrecision — LangChain-free path only
reference/promptfoo-patterns.md	YAML config, google:gemini-2.5-pro provider, 70+ red-team plugins, MCP security suite
reference/giskard-patterns.md	v3 Scenario/Suite API, LiteLLM Gemini setup, FHA check, RAGET v2-only warning
reference/langfuse-prompts.md	PromptRegistry abstraction, prompt lifecycle, .compile(), emergency pack, drift detection
reference/golden-dataset.md	8-folder structure, dataset_manifest.yaml schema, per-agent case minimums
reference/ci-tiers.md	R1-R4 tier config, pytest marks (@r1/@r2), path-routing rules, 9 CI blockers reference
reference/per-agent-thresholds.md	Per-agent accuracy thresholds for all 14 agents, habitability 100% sub-threshold
reference/mcp-eval-patterns.md	MCP contract suite, tenant isolation test pattern, audit-log verification
reference/pytest-harness.md	asyncio_mode = "auto", conftest.py template, InMemoryRunner, parametrize-over-golden
reference/failure-mode-taxonomy.md	6 failure modes with symptom → eval tool routing table; fix patterns per mode

Process — Before Writing Any Eval Code

1. Identify which tool(s) are needed
2. Query Context7 for that tool's current API — the reference files are starting points, NOT the final authority on API signatures
3. Read the relevant reference file for patterns and gotchas
4. Check reference/per-agent-thresholds.md for the target agent's required thresholds
5. Check reference/golden-dataset.md for dataset structure and minimum case counts
6. Mark every test with @pytest.mark.r1 (PR gate) or @pytest.mark.r2 (nightly) — never unmarked
7. After implementation: dispatch eval-reviewer agent

Make Targets Quick Reference

Target	What it runs	When to use
make eval-smoke	Lint + types + 1-2 eval cases for changed agent + prompt schema check	Every PR (< 30s)
make eval-adk	adk eval against all tests/golden/agents/*/golden.evalset.json	Full ADK eval
make eval-deepeval	uv run pytest -m eval -k deepeval	DeepEval metric runs
make eval-ragas	uv run pytest -m eval -k ragas	RAG metric runs
make redteam-promptfoo	npx promptfoo eval -c tests/eval/promptfoo/	Red-team + regression
make giskard-scan AGENT=<name>	Targeted Giskard scan → reports/giskard/<agent>.html	Security scan
make mcp-eval-all	Contract + auth + behavior + security suites for MCP server	MCP eval
make eval-all-local	All of the above in sequence	Full local validation
make seed-prompts-local	Seeds prompts into local Langfuse with label="development"	Prompt registry setup
make diff-prompts-staging	Detects Git ↔ Langfuse prompt drift	Pre-release check
make phoenix-experiment AGENT=<name>	Phoenix run_experiment() against golden dataset	Trace replay eval
make mcp-inspect	Launches @modelcontextprotocol/inspector against local MCP	Interactive MCP debug
make eval-multiturn AGENT=<name>	ADK User Simulation multi-turn flows	Multi-turn eval
make update-mcp-hashes	Regenerates mcp/.tool-surface-hashes.json	After MCP tool changes

Golden Dataset Minimum Requirements (Day-1)

Agent/Suite	Minimum cases	Location
Primary agent (highest-risk)	≥ 8 Day-1 → ≥ 100 full target	tests/golden/agents/<agent>/
Secondary agents	≥ 8 Day-1 → ≥ 20 full target	tests/golden/agents/<agent>/
Security suite	≥ 1 each: prompt_injection, tenant_isolation, policy_bypass	tests/golden/security/
All other agents	≥ 20 before agent PR merges	tests/golden/agents/<agent>/
RAG agents	≥ 10 faithfulness cases	tests/golden/rag/
MCP contract	≥ 1 per tool	tests/golden/mcp/

Related Skills

• adk-eval-guide — ADK-native eval only (8 ADK criteria, evalset schema, user simulation)
• google-adk — ADK agent construction patterns
• adk-observability-guide — Phoenix OTel integration, span inspection