Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add soba-labs/langchain-agent-skills --skill langgraph-testing-evaluationgit clone --depth 1 https://github.com/soba-labs/langchain-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/soba-labs/langchain-agent-skills/langgraph-testing-evaluation)<a href="https://agentmods.dev/skills/soba-labs/langchain-agent-skills/langgraph-testing-evaluation"><img src="https://agentmods.dev/badge/skills/soba-labs/langchain-agent-skills/langgraph-testing-evaluation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/soba-labs/langchain-agent-skills/langgraph-testing-evaluation"><img src="https://agentmods.dev/badge/skills/soba-labs/langchain-agent-skills/langgraph-testing-evaluation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00099 | $0.02000 |
| Opus 5 | $0.00049 | $0.01000 |
| Sonnet 5 | $0.00020 | $0.00400 |
| Haiku 4.5 | $0.00010 | $0.00200 |
Grade A, and why
langgraph-testing-evaluation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What ships with it
17 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/datasets/sample_dataset.json 1.9 KB
- assets/examples/README.md 1.4 KB
- assets/templates/test_template.py 2.6 KB runs code
- references/ab-testing.md 15 KB
- references/langsmith-evaluation.md 10 KB
- references/trajectory-evaluation.md 10 KB
- references/unit-testing-patterns.md 12 KB
- scripts/compare_agents.js 8.3 KB runs code
- scripts/compare_agents.py 11 KB runs code
- scripts/evaluate_with_langsmith.js 7.4 KB runs code
- scripts/evaluate_with_langsmith.py 12 KB runs code
- scripts/generate_test_cases.js 9.0 KB runs code
- scripts/generate_test_cases.py 11 KB runs code
- scripts/mock_llm_responses.js 6.1 KB runs code
- scripts/mock_llm_responses.py 11 KB runs code
- scripts/run_trajectory_eval.js 3.6 KB runs code
- scripts/run_trajectory_eval.py 11 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 259 lines · 99 tokens per session scan A 2d4818b80193
langgraph-testing-evaluation is a skill published in the GitHub repository soba-labs/langchain-agent-skills (106 stars, last pushed 23d ago), licensed MIT. It adds 99 tokens to every session and 2,000 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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