langchain-tests
Standard tests for LangChain implementations
Decision gist · record as of 2026-08-14
Yes, if you are developing a LangChain integration (chat model, retriever, or other component). It provides the canonical test suite and fixtures the LangChain ecosystem expects, with low install friction and active maintenance. Not relevant for end users of LangChain who are not building integrations. Pin the version in your CI to avoid unexpected test additions breaking your pipeline.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; integrations must inherit from langchain-core base classes.
- Low friction install with a pure-Python wheel.
- Active maintenance (last commit 2026-08-14) and recent release (2026-05-21).
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution in both open-source and proprietary projects with minimal restrictions.
last release 2026-05-21 (85 days) · last repo commit 2026-08-14 · 144,266 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,969,745 downloads/mo, #3,400 on PyPI
Alternatives
Verify before relying
pip install langchain-tests
from langchain_tests.unit_tests import ChatModelUnitTests
from langchain_core.language_models import BaseChatModel
class TestMyModel(ChatModelUnitTests):
@pytest.fixture
def chat_model_class(self) -> Type[BaseChatModel]:
return MyCustomChatModel- Whether test coverage includes all LangChain integration types beyond chat models (e.g., retrievers, agents).
- Performance impact of running the full test suite on typical CI pipelines.
- Compatibility guarantees across langchain-core versions when pinning is not used.
What it is and what it does
langchain-tests is a testing library that provides standardized test base classes and fixtures for validating LangChain integrations. It defines unit and integration test templates (e.g., ChatModelUnitTests, ChatModelIntegrationTests) that integration authors inherit from to ensure their implementations conform to LangChain's standard interfaces. The library bundles pytest plugins (pytest-asyncio, pytest-benchmark, pytest-recording, pytest-socket, vcrpy) and assertion tools (syrupy) to support deterministic, isolated testing.
Integration packages use langchain-tests by creating test classes that inherit from the appropriate base and configure fixtures like chat_model_class and optional parameters such as tool-calling and structured-output capabilities. The library is actively maintained by the LangChain team and released frequently; the documentation explicitly recommends pinning to a specific version in CI to prevent unexpected test failures when new tests are introduced.
Use it for
- Validate a custom chat model integration against LangChain's standard interface before publishing.
- Ensure a third-party LLM provider integration passes unit and integration tests in CI/CD.
- Add deterministic, recorded HTTP tests to an integration using pytest-recording and vcrpy.
- Benchmark performance of a chat model integration with pytest-benchmark fixtures.
- Test tool-calling and structured-output capabilities of a chat model implementation.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are developing a LangChain integration (chat model, retriever, or other component).
It provides the canonical test suite and fixtures the LangChain ecosystem expects, with low install friction and active maintenance. Not relevant for end users of LangChain who are not building integrations. Pin the version in your CI to avoid unexpected test additions breaking your pipeline.
Install
langchain-tests on PyPI
Before you install
Low friction install with a pure-Python wheel. Active maintenance (last commit 2026-08-14) and recent release (2026-05-21). Documentation recommends pinning versions to avoid CI breakage when new tests are added.
Requires Python 3.10 or later; integrations must inherit from langchain-core base classes.
License in practice
MIT license permits unrestricted use, modification, and distribution in both open-source and proprietary projects with minimal restrictions.
Quickstart
pip install langchain-tests
from langchain_tests.unit_tests import ChatModelUnitTests
from langchain_core.language_models import BaseChatModel
class TestMyModel(ChatModelUnitTests):
@pytest.fixture
def chat_model_class(self) -> Type[BaseChatModel]:
return MyCustomChatModelVerify before relying
- Whether test coverage includes all LangChain integration types beyond chat models (e.g., retrievers, agents).
- Performance impact of running the full test suite on typical CI pipelines.
- Compatibility guarantees across langchain-core versions when pinning is not used.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0.0,>=3.10.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 11 packageshttpxlangchain-corenumpypytest-asynciopytest-benchmarkpytest-codspeedpytest-recordingpytest-socketpytestsyrupyvcrpy |
| Maintenance | Actively maintained 85 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,969,745 / month, #3,400 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Software Development :: Libraries :: Python ModulesTopic :: Software Development :: Testing |
Evidence: langchain_tests-1.1.9-py3-none-any.whl
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See also langchain-openai · langchain-groq · langchain-deepseek · langchain-ollama · langchain-google-community · langchain-perplexity · langchain-xai · langchain-google-genai · langchain-classic · langchain-aws