Langgraph Testing Evaluation Agent Skill

LangGraph Testing & Evaluation

Practical workflows for validating agent quality with:

• Unit/integration tests
• Trajectory evaluation
• LangSmith dataset evaluations
• A/B-style comparisons between versions

Use this file for high-level flow. Load references/* for detailed implementation.

Start Here

Choose the smallest approach that answers your question:

| Goal | Primary method | Load first |

| --- | --- | --- |

| Validate node logic quickly | Unit tests with mocks | references/unit-testing-patterns.md |

| Validate multi-step agent behavior | Trajectory evaluation | references/trajectory-evaluation.md |

| Track quality over datasets over time | LangSmith evaluation | references/langsmith-evaluation.md |

| Compare old vs new agent versions | A/B comparison | references/ab-testing.md |

Recommended order:

• Unit tests
• Integration/trajectory checks
• Dataset evaluation in LangSmith
• A/B comparison before deployment

Quick Commands

Run from repo root.

Generate test scaffolding

# Python (preferred)
uv run skills/langgraph-testing-evaluation/scripts/generate_test_cases.py my_agent:graph --output tests/ --framework pytest

# JavaScript/TypeScript
node skills/langgraph-testing-evaluation/scripts/generate_test_cases.js ./my-agent.ts:graph --output tests/ --framework vitest

Run trajectory evaluation

# Python: LLM-as-judge
uv run skills/langgraph-testing-evaluation/scripts/run_trajectory_eval.py my_agent:run_agent my_dataset --method llm-judge --model openai:o3-mini

# Python: trajectory match
uv run skills/langgraph-testing-evaluation/scripts/run_trajectory_eval.py my_agent:run_agent dataset.json --method match --trajectory-match-mode strict --reference-trajectory reference.json

# JavaScript/TypeScript
node skills/langgraph-testing-evaluation/scripts/run_trajectory_eval.js ./agent.ts:runAgent my_dataset --method llm-judge --model openai:o3-mini --max-concurrency 4

Run LangSmith dataset evaluation

# Python
uv run skills/langgraph-testing-evaluation/scripts/evaluate_with_langsmith.py my_agent:run_agent my_dataset --evaluators accuracy,latency --max-concurrency 4

# Python (do not upload experiment results)
uv run skills/langgraph-testing-evaluation/scripts/evaluate_with_langsmith.py my_agent:run_agent my_dataset --evaluators accuracy --no-upload

# JavaScript/TypeScript
node skills/langgraph-testing-evaluation/scripts/evaluate_with_langsmith.js ./agent.ts:runAgent my_dataset --evaluators accuracy,latency --max-concurrency 4

Compare two agent versions

# Python
uv run skills/langgraph-testing-evaluation/scripts/compare_agents.py my_agent:v1 my_agent:v2 dataset.json --output comparison_report.json

# JavaScript/TypeScript
node skills/langgraph-testing-evaluation/scripts/compare_agents.js ./v1.ts:run ./v2.ts:run dataset.json --output comparison_report.json

# JavaScript/TypeScript (force local dataset file only)
node skills/langgraph-testing-evaluation/scripts/compare_agents.js ./v1.ts:run ./v2.ts:run dataset.json --no-langsmith

Create mock response configs

# Python
uv run skills/langgraph-testing-evaluation/scripts/mock_llm_responses.py create --type sequence --output mock_config.json

# JavaScript/TypeScript
node skills/langgraph-testing-evaluation/scripts/mock_llm_responses.js create --type sequence --output mock_config.json

Core Workflow

• Define test scope.
• Unit: deterministic logic in one node/function.
• Integration: node interactions and routing.
• End-to-end: complete response quality on realistic inputs.

• Start from deterministic checks.
• Mock LLM/tool IO for speed and repeatability.
• Keep real-model tests as a smaller, explicit suite.

• Build/curate dataset examples.
• Use stable inputs and expected outputs.
• Keep schema simple: inputs and outputs objects (optional metadata).
• Compatibility note: scripts also accept singular keys (input, output) for legacy datasets.

• Run evaluation with explicit gates.
• Use evaluator keys that map to deployment decisions.
• Set thresholds in CI for regression prevention.

• Compare versions before rollout.
• Run same dataset on both versions.
• Check both quality and latency.

• Diagnose failures from traces/experiments.
• Inspect low-scoring examples.
• Split failures by pattern (routing, tool usage, hallucination, latency spikes).

Current References (Load On Demand)

references/unit-testing-patterns.md

Load when:

• You need node-level and routing test patterns.
• You need pytest/vitest/Jest integration patterns.
• You need robust mocking and flaky-test reduction.

references/trajectory-evaluation.md

Load when:

• You need trajectory match evaluation (strict, unordered, subset, superset).
• You need LLM-as-judge trajectory scoring.
• You need LangSmith experiment comparison for trajectory results.

references/langsmith-evaluation.md

Load when:

• You need dataset creation/management in LangSmith.
• You need evaluator signatures and experiment runs in Python/TS.
• You need CI-friendly workflows with quality thresholds.

references/ab-testing.md

Load when:

• You need offline A/B comparison methodology.
• You need significance testing and interpretation.
• You need production traffic split strategy and guardrails.

Assets

assets/templates/test_template.py

• Runnable Python pytest template aligned with current LangGraph testing patterns.
• Includes:
• Compiled-graph invocation with thread_id
• Single-node testing via compiled_graph.nodes[...]
• Integration-test placeholder

assets/datasets/sample_dataset.json

• Deterministic seed dataset for LangSmith ingestion.
• Uses examples: [{ inputs, outputs, metadata }] format.

assets/examples/README.md

• Documentation-only index for current asset usage.
• Notes where runnable assets live today.

Script Interface Summary

scripts/generate_test_cases.py / .js

Use for fast test scaffolding.

Inputs:

• Graph module path
• Python: my_module:graph or my_module.graph
• JS/TS: ./file.ts:graph

Outputs:

• Framework-specific starter tests in target directory.

scripts/run_trajectory_eval.py / .js

Use for trajectory scoring with either:

• --method match
• --method llm-judge

Supports:

• Local dataset files (.json)
• LangSmith dataset names
• Optional reference trajectory file with --reference-trajectory
• Match modes: strict, unordered, subset, superset

Local-only mode:

• --no-langsmith in both Python and JavaScript scripts (requires local JSON dataset file)

scripts/evaluate_with_langsmith.py / .js

Use for dataset-based evaluation runs and experiment tracking.

Supports:

• Existing dataset by name
• Dataset creation from JSON examples file
• Multiple evaluators (--evaluators accuracy,latency,...)
• Concurrency control (--max-concurrency)

Python-only:

• --no-upload to run without uploading experiment results

scripts/compare_agents.py / .js

Use for offline version comparisons:

• Shared dataset input
• Success/latency summaries
• JSON report output for CI artifacts
• Local JSON datasets or LangSmith datasets (JS supports --no-langsmith to disable remote loading)

scripts/mock_llm_responses.py / .js

Use for deterministic test doubles:

• single
• sequence
• conditional

Decision Rules

If behavior is deterministic and local:

• Use unit tests first.

If behavior depends on tool sequence/routing:

• Add trajectory evaluation.

If behavior depends on realistic distribution quality:

• Run LangSmith dataset evaluation.

If approving a replacement model/prompt/graph:

• Run A/B comparison and check both quality and latency.

Common Failure Patterns

Flaky tests

• Cause: real-model nondeterminism in unit scope.
• Fix: mock LLM/tool calls for unit tests; reserve real-model tests for separate integration marks.

High trajectory variance

• Cause: overly strict matching for workflows with equivalent paths.
• Fix: switch match mode (unordered, subset, or superset) where appropriate.

Regressions hidden by averages

• Cause: only aggregate score monitored.
• Fix: inspect per-example failures and segment by category metadata.

Latency regressions with same quality

• Cause: no explicit latency gate.
• Fix: include latency evaluator and CI threshold.

Minimal Best Practices

• Keep fast deterministic tests as the largest share.
• Version datasets and keep them stable.
• Track both correctness and latency.
• Add explicit go/no-go thresholds in CI.
• Compare candidate vs baseline before production rollout.
• Investigate failures with trace-level evidence, not only aggregate scores.