Zenprojectgit Claude Code Plugins Plus Skills Langchain Local Dev Loop

LangChain Local Dev Loop (Python)

Overview

An engineer writes the most natural assertion possible:

def test_summarize():
    out = chain.invoke({"text": "..."})
    assert out.content == "expected summary"

It passes locally against Claude at temperature=0. It fails in CI on the third run with a one-token delta in the output. That is P05: Anthropic's temperature=0 is not greedy — it still samples. Tests against live Claude are not deterministic, period.

So the engineer swaps in FakeListChatModel(responses=["expected summary"]) and the assertion passes. Then the downstream callback that logs cost blows up in CI with KeyError: 'token_usage' — because FakeListChatModel does not emit response_metadata["token_usage"] (P43). Production code reads that key, so either the fake has to synthesize it or the test has to skip the callback.

Meanwhile, the first integration test under VCR records a cassette that ships Authorization: Bearer sk-ant-api03-... in the repo (P44). PR review catches it; the reviewer revokes the key; the dev loop is hosed for an afternoon.

And none of this matters if pytest cannot even collect the suite because import langchain_community emits a DeprecationWarning that -W error promotes to failure (P45).

This skill installs the four layers that make the whole loop fast and safe: FakeListChatModel / FakeListLLM with a metadata-emitting subclass (fixes P43); VCR with filter_headers plus a pre-commit hook (fixes P44); pytest filterwarnings policy in pyproject.toml (fixes P45); and an env-var-gated integration marker so the default pytest run never touches live APIs.

Speed targets: unit tests with FakeListChatModel run in < 100ms per test; VCR-replayed integration tests run in 500ms – 2s per test; live integration tests (the RUN_INTEGRATION=1 gate) run only in nightly or manual workflows.

Pin: langchain-core 1.0.x, langgraph 1.0.x, pytest current, vcrpy current. Pain-catalog anchors: P05, P43, P44, P45.

Prerequisites

• Python 3.10+
• pip install langchain-core>=1.0,<2.0 langgraph>=1.0,<2.0 pytest vcrpy pytest-recording
• For integration tests: at least one provider key (ANTHROPIC_API_KEY, etc.)
• Project uses pyproject.toml (PEP 621) for pytest config

Instructions

Step 1 — Deterministic unit tests with FakeListChatModel

Use FakeListChatModel from langchain_core.language_models.fake for chat chains and FakeListLLM for legacy completion LLMs. Responses cycle through the list.

from langchain_core.language_models.fake import FakeListChatModel
from langchain_core.prompts import ChatPromptTemplate

def test_classifier_picks_positive():
    fake = FakeListChatModel(responses=["positive"])
    prompt = ChatPromptTemplate.from_messages([("user", "Classify: {text}")])
    chain = prompt | fake
    out = chain.invoke({"text": "I love it"})
    assert out.content == "positive"

This is deterministic, runs in single-digit milliseconds, and has zero provider dependency. Use it for every chain assertion that does not specifically require real model behavior.

Step 2 — Subclass FakeListChatModel to emit response_metadata (P43 fix)

The stock fake emits no response_metadata["token_usage"]. If your chain has a callback that records cost, the callback crashes under the fake. Subclass and synthesize the metadata instead of mocking around the callback:

from langchain_core.language_models.fake import FakeListChatModel
from langchain_core.outputs import ChatGeneration, ChatResult
from langchain_core.messages import AIMessage

class FakeChatWithUsage(FakeListChatModel):
    """FakeListChatModel that emits response_metadata['token_usage'] so
    downstream callbacks reading token usage do not crash under test."""

    def _generate(self, messages, stop=None, run_manager=None, **kwargs):
        response = self.responses[self.i % len(self.responses)]
        self.i += 1
        message = AIMessage(
            content=response,
            response_metadata={
                "token_usage": {
                    "input_tokens": 10,
                    "output_tokens": len(response.split()),
                    "total_tokens": 10 + len(response.split()),
                },
                "model_name": "fake-chat",
            },
            usage_metadata={
                "input_tokens": 10,
                "output_tokens": len(response.split()),
                "total_tokens": 10 + len(response.split()),
            },
        )
        return ChatResult(generations=[ChatGeneration(message=message)])

Use FakeChatWithUsage whenever a chain's observability / cost path is in the assertion surface. See Fake Model Fixtures for agent, retriever, and embedder fakes.

Step 3 — pytest fixtures that wire the fake into chains

Put fixtures in tests/conftest.py so they are shared across the suite:

# tests/conftest.py
import pytest
from langchain_core.prompts import ChatPromptTemplate
from tests.fakes import FakeChatWithUsage

@pytest.fixture
def fake_chat():
    """Reusable fake chat model. Override responses per-test via
    monkeypatch.setattr(fake_chat, 'responses', [...])."""
    return FakeChatWithUsage(responses=["ok"])

@pytest.fixture
def summarize_chain(fake_chat):
    prompt = ChatPromptTemplate.from_messages([
        ("system", "Summarize the user's text in one line."),
        ("user", "{text}"),
    ])
    return prompt | fake_chat

Per-test response override:

def test_summary_shape(summarize_chain, fake_chat):
    fake_chat.responses = ["short summary"]
    out = summarize_chain.invoke({"text": "long input"})
    assert out.content == "short summary"

Step 4 — VCR cassettes for integration tests with key redaction (P44 fix)

Unit tests should never touch the network. Integration tests do, exactly once — to record a cassette — and every subsequent run replays from the cassette file. vcrpy records headers by default, which means Authorization: Bearer sk-... lands in the fixture unless you filter it.

Configure VCR in tests/conftest.py:

# tests/conftest.py (continued)
import pytest

@pytest.fixture(scope="module")
def vcr_config():
    return {
        "filter_headers": [
            "authorization",
            "x-api-key",
            "anthropic-version",
            "openai-organization",
            "cookie",
        ],
        "filter_query_parameters": ["api_key"],
        # Block accidental re-recording in CI:
        "record_mode": "none",
    }

Use pytest-recording:

import pytest

@pytest.mark.vcr  # cassette at tests/cassettes/<test_name>.yaml
@pytest.mark.integration
def test_live_claude_short_answer():
    from langchain_anthropic import ChatAnthropic
    chat = ChatAnthropic(model="claude-sonnet-4-6", temperature=0, timeout=30)
    out = chat.invoke("Say 'ok' and nothing else.")
    assert "ok" in out.content.lower()

To record (once, locally, with a real key): pytest --record-mode=once tests/. Every other run replays — cassettes are committed, real API is never hit again.

Pre-commit hook to block key leaks:

# .git/hooks/pre-commit or .pre-commit-config.yaml entry
#!/usr/bin/env bash
set -e
if git diff --cached --name-only | grep -q '^tests/cassettes/'; then
    if git diff --cached -U0 -- 'tests/cassettes/' | \
       grep -E '(sk-ant-[a-zA-Z0-9_-]+|sk-[a-zA-Z0-9]{20,}|Bearer\s+[a-zA-Z0-9_-]{20,})'; then
        echo "ERROR: API key pattern found in staged cassette." >&2
        exit 1
    fi
fi

See VCR Cassette Hygiene for the full pre-commit config, record-new-episodes flow, shared-cassette patterns, and the PR review checklist.

Step 5 — Pytest warnings + markers in pyproject.toml (P45 fix)

langchain_community and some provider SDKs emit DeprecationWarning at import time. If the suite runs -W error, collection fails before any test does. Set the policy once in pyproject.toml:

[tool.pytest.ini_options]
minversion = "8.0"
testpaths = ["tests"]
addopts = [
    "-ra",
    "--strict-markers",
    "--strict-config",
    "-W", "error",
]
markers = [
    "integration: hits real APIs or replays VCR cassettes (set RUN_INTEGRATION=1)",
    "slow: takes > 1s per test",
    "smoke: minimal healthcheck run in CI",
]
filterwarnings = [
    "error",
    "ignore::DeprecationWarning:langchain_community.*",
    "ignore::DeprecationWarning:pydantic.*",
    "ignore::PendingDeprecationWarning:langchain_core.*",
]

See Pytest Config for the full skeleton including coverage config and parallel execution notes.

Step 6 — Integration-test gating via env var

Default pytest must never hit real APIs. Gate on RUN_INTEGRATION=1:

# tests/conftest.py (continued)
import os
import pytest

def pytest_collection_modifyitems(config, items):
    if os.getenv("RUN_INTEGRATION") == "1":
        return
    skip_integration = pytest.mark.skip(reason="set RUN_INTEGRATION=1 to run")
    for item in items:
        if "integration" in item.keywords:
            item.add_marker(skip_integration)

CI default: pytest (unit only). Nightly / manual: RUN_INTEGRATION=1 pytest -m integration.

Step 7 — LangGraph tests: per-test thread_id + state assertions

LangGraph state is scoped to a thread_id. Tests that share a thread_id leak state between each other. Give every test a fresh thread_id and a fresh MemorySaver:

from langgraph.checkpoint.memory import MemorySaver
import uuid, pytest

@pytest.fixture
def graph_config():
    return {"configurable": {"thread_id": str(uuid.uuid4())}}

@pytest.fixture
def checkpointed_graph(fake_chat):
    from my_app.graphs import build_graph
    return build_graph(fake_chat).compile(checkpointer=MemorySaver())

def test_node_emits_plan(checkpointed_graph, graph_config, fake_chat):
    fake_chat.responses = ["step 1\nstep 2\nstep 3"]
    result = checkpointed_graph.invoke({"goal": "deploy"}, graph_config)
    # Assert state shape per node, not just the final output:
    assert result["plan"] == ["step 1", "step 2", "step 3"]
    # Time-travel: inspect every checkpoint for debugging
    history = list(checkpointed_graph.get_state_history(graph_config))
    assert history[-1].values == {"goal": "deploy"}  # initial state

Subgraph isolation testing cross-references langchain-langgraph-subgraphs (pain P21 — parent cannot read child state unless the key is in the parent schema). See LangGraph Test Patterns for the subgraph-shared-state test recipe.

Output

• tests/fakes.py with FakeChatWithUsage subclass that emits response_metadata
• tests/conftest.py with fake-model fixtures, VCR config, and RUN_INTEGRATION gate
• pyproject.toml [tool.pytest.ini_options] block with markers and filterwarnings
• tests/cassettes/ committed with filtered headers (no Authorization / x-api-key)
• Pre-commit hook grepping cassettes for sk- / sk-ant- / Bearer patterns
• LangGraph tests with per-test thread_id and MemorySaver — no cross-test leakage

Test-type matrix

Error Handling

Examples

A flaky chain assertion, fixed in three commits

1. Commit 1 — failing test uses real ChatAnthropic, passes locally, fails 1-in-5 in CI at temperature=0 (P05).
2. Commit 2 — swap to fake model uses FakeListChatModel, passes deterministically, but the cost-logging callback crashes (P43).
3. Commit 3 — fake with metadata uses FakeChatWithUsage, the callback reads response_metadata["token_usage"] cleanly, the test is green and runs in 40ms.

See Fake Model Fixtures for the full worked example including agent and retriever fakes.

Recording a cassette without leaking a key

# 1. Ensure conftest.py has filter_headers configured FIRST
# 2. Record with real key present in the environment
ANTHROPIC_API_KEY=sk-ant-... pytest --record-mode=once tests/integration/test_summarize.py
# 3. Verify no leak
grep -E 'sk-|Bearer' tests/cassettes/*.yaml && echo "LEAK" || echo "clean"
# 4. Commit cassettes/ — pre-commit hook runs the same grep as a hard gate
git add tests/cassettes/ && git commit -m "test: record summarize cassette"

See VCR Cassette Hygiene for record-new-episodes mode, rerecord-on-mismatch, and the PR review checklist.

LangGraph time-travel debugging on a failing test

When a graph test fails mid-graph, get_state_history(config) returns every checkpoint — you can replay from any point by passing its config.checkpoint_id back into graph.invoke. See LangGraph Test Patterns for the full time-travel debugging recipe and the subgraph-shared-state test pattern (cross-ref langchain-langgraph-subgraphs / pain L30).

Resources

• LangChain Python: testing guide
• FakeListChatModel API
• vcrpy documentation
• pytest-recording
• LangGraph MemorySaver + get_state_history
• Pytest filterwarnings
• Pack pain catalog: docs/pain-catalog.md (entries P05, P43, P44, P45)

Source: ZenProjectGit/claude-code-plugins-plus-skills — distributed by TomeVault.