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Langgraph Testing Evaluation

Listed
lubu-labs·vlatest·agents·MIT

Automates LangGraph application testing and evaluation, providing metrics on performance, accuracy, and robustness for rapid iteration.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add soba-labs/langchain-agent-skills --skill langgraph-testing-evaluation -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

lubu-labs

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 2 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: docs.langchain.com, smith.langchain.com.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    docs.langchain.comundeclaredsmith.langchain.comundeclared

    What it found (3 of 5)

    • skills/langgraph-testing-evaluation/assets/templates/test_template.py:5contacts docs.langchain.com
      https://docs.langchain.com/oss/python/langgraph/test
    • skills/langgraph-testing-evaluation/scripts/evaluate_with_langsmith.js:248contacts smith.langchain.com
      console.log("   https://smith.langchain.com");
    • skills/langgraph-testing-evaluation/scripts/evaluate_with_langsmith.py:265contacts smith.langchain.com
      print("https://smith.langchain.com")

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned lubu-labs/langchain-agent-skills at a2d4a1011b.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
a2d4a1011b
Manifest
skills/langgraph-testing-evaluation/SKILL.md
Scanned
18 files, 5,283 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables automated testing and evaluation of LangGraph workflows. It allows you to define test cases, run them against your LangGraph application, and assess the results based on predefined metrics. This facilitates continuous integration and ensures the reliability of complex AI agent systems built with LangGraph.

When to use it

  • Automated Regression Testing: After making changes to a LangGraph workflow, automatically re-run tests to ensure existing functionality remains intact.
  • Performance Benchmarking: Measure the execution time and resource usage of different LangGraph configurations under various conditions.
  • Input Validation: Test how your LangGraph application handles unexpected or invalid inputs.
  • Integration Testing: Verify that components within a larger LangGraph system interact correctly with each other.

Key capabilities

  • Automated test execution
  • Metric-based evaluation of results
  • Test case definition and management
  • Regression testing support

Example prompts

  • "Run the 'basic_functionality' test suite against my LangGraph application."
  • "Evaluate the performance of the 'data_extraction' workflow with 100 sample inputs."
  • "Generate a report comparing the accuracy of version 1.0 and version 2.0 of the LangGraph agent."

Tips & gotchas

This skill requires familiarity with LangGraph workflows and testing methodologies. Ensure your test cases are well-defined and cover various scenarios to achieve comprehensive evaluation.