Comprehensive Engineering Strategy: Testing Architectures for AI-Driven Microservices in 2025

December 28, 2025 - By Hashan Ruchira

Executive Summary

The convergence of deterministic microservices architecture with probabilistic Artificial Intelligence (AI) components represents the defining engineering challenge of 2025. This report, prepared from the perspective of Staff Engineering, analyzes the optimal testing framework workflow for a modern technology stack comprising FastAPI, LangGraph, PostgreSQL, and React, orchestrated via GitLab CI/CD. The system architecture is transitioning from a monolithic structure to a Domain-Driven Design (DDD) microservices ecosystem enabled by Kafka event streaming and monitored via Prometheus and Grafana.

This transition necessitates a fundamental shift in testing philosophy: moving from simple code verification to System Behavior Validation. The traditional Test Pyramid, with its heavy reliance on unit tests and mocks, proves inadequate for systems where the primary failure modes are distributed race conditions, schema drift in asynchronous events, and semantic drift in Large Language Model (LLM) outputs.

Our analysis identifies three critical testing imperatives for this architecture:

Infrastructure Fidelity: The use of Testcontainers to replace in-memory mocks with ephemeral, production-grade Docker instances for PostgreSQL and Kafka during integration testing.
Semantic Assurance: The implementation of “LLM-as-a-Judge” patterns using Ragas and LangSmith to evaluate the non-deterministic outputs of LangGraph agents.
Contract Rigor: The adoption of Consumer-Driven Contract Testing (CDCT) with Pact to enforce interface stability between microservices without requiring synchronized deployments.

The following report details a rigorous, multi-layered testing strategy. It explores the implementation nuances of “Observability-Driven Testing” within GitLab CI/CD, where pipeline success is predicated not just on passing assertions, but on satisfying Prometheus-defined Service Level Objectives (SLOs). We conclude with three graded workflow archetypes, recommending the “High-Fidelity GitOps Workflow” as the standard for production-grade resilience in 2025.

1. Architectural Context and the 2025 Testing Paradigm

The software engineering landscape of 2025 is characterized by “High-Assurance Complexity.” We are no longer building standalone CRUD applications; we are constructing distributed decision-making engines. The stack in question—FastAPI for high-performance I/O, LangGraph for stateful agentic workflows, PostgreSQL for relational data integrity, and React for reactive interfaces—is robust but introduces specific vectors of instability that testing must address.

1.1 The Domain-Driven Design (DDD) Imperative

In the context of a migration to microservices, Domain-Driven Design (DDD) serves as the architectural bedrock. It prevents the system from devolving into a “Distributed Monolith” where services are tightly coupled by shared database schemas or rigid API calls. For testing, DDD mandates a strict separation of concerns, which we operationalize through the “Testing Onion.”

1.1.1 The Domain Layer: Pure Logic Verification

The core of the application—the Entities, Value Objects, and Aggregates—must remain pure Python.¹ This layer encapsulates the business rules (e.g., “An Order cannot be finalized if the Inventory check fails”).

Testing Strategy: These tests must be dependency-free. They should not import sqlalchemy or fastapi. They exist to verify the integrity of the business logic in isolation.
The Staff Engineer’s Perspective: A common anti-pattern observed is the leakage of infrastructure concerns into domain tests. If a unit test for an Order entity requires mocking a database session, the architecture has failed the “purity check”.² We enforce strict boundaries: domain tests execute in milliseconds and validate logic, not persistence.

1.1.2 The Infrastructure Layer: The Anti-Corruption Boundary

Surrounding the domain is the Infrastructure Layer, responsible for persistence (PostgreSQL) and messaging (Kafka). This is the “Anti-Corruption Layer” that translates dirty external data into clean domain objects.

Testing Strategy: This layer requires High-Fidelity Integration Testing. Mocks are dangerous here. A mock repository might successfully return an entity, but a real PostgreSQL database might throw a UniqueConstraintViolation or a SerializationFailure.³ In 2025, we reject the use of SQLite as a substitute for PostgreSQL in tests; the divergence in locking semantics and JSONB support is too high a risk.

1.2 The Rise of Probabilistic Systems (LangGraph)

The integration of LangGraph introduces a paradigm shift from deterministic to probabilistic software. A traditional function f(x) always returns y. A LangGraph agent agent(x) returns y' where y' is subject to variance based on the LLM’s temperature, stochastic decoding, and context window dynamics.⁴

The Testing Gap: Standard unit assertions (assert result == expected) fail with LLMs. “Similar” is not “Equal.”
The Solution: We must adopt Evaluation Driven Development (EDD). We treat the agent’s behavior as a hypothesis that must be graded on a curve using semantic metrics like Faithfulness and Relevancy.⁵

1.3 Observability as a Testing Interface

With the inclusion of Prometheus and Grafana, observability is no longer just for post-deployment monitoring. It becomes a primary interface for testing.

Shift-Right Testing: In a microservices environment, some behaviors (like cascading latency degradation) only manifest under load. “Testing” involves deploying to a staging environment, generating synthetic load with k6, and querying Prometheus to ensure the p95 latency remains within the defined error budget.⁶

2. Backend Testing Strategy: FastAPI, PostgreSQL, and DDD

The backend testing strategy must balance the speed required for developer velocity with the rigor required for system stability. We employ a tiered approach that leverages the specific strengths of the Python ecosystem in 2025.

2.1 Unit Testing: The First Line of Defense

For the FastAPI application, unit tests focus on the Application Layer (Use Cases) and the Domain Layer. We utilize pytest as the runner due to its powerful fixture system, which aligns perfectly with FastAPI’s Dependency Injection system.

2.1.1 Testing Domain Invariants

Domain tests are the fastest suite. We use Property-Based Testing (via Hypothesis) to generate a vast array of inputs against our Pydantic models to ensure robustness.

Scenario: An Order must have a total greater than zero.
Implementation: Instead of writing three manual test cases, we define a property: “For any list of OrderLine items, if the list is non-empty and prices are positive, the Order.total must be positive.”
DDD Alignment: This ensures that the “Ubiquitous Language” rules are mathematically enforced in the code.⁷

2.1.2 Testing Application Services with Fakes

The Application Layer orchestrates the Domain and Infrastructure. To test this without hitting the database, we prefer Fakes over Mocks.

The Pattern: Instead of using unittest.mock to stub a Repository.save() method, we implement a FakeOrderRepository that uses an in-memory dictionary.
Why: Mocks couple tests to implementation details (e.g., “Expect save to be called once”). Fakes verify behavior (e.g., “After execution, the order is retrievable”). This decoupling makes refactoring significantly safer and is a hallmark of mature Staff-level engineering.¹

2.2 Integration Testing: The Testcontainers Revolution

The critical innovation for 2025 is the standardized use of Testcontainers for all infrastructure integration tests. This technology allows us to programmatically spin up disposable Docker containers for dependencies directly from the test code.

2.2.1 The PostgreSQL Container Workflow

Testing the interaction between SQLAlchemy and PostgreSQL must be done against a real PostgreSQL instance to catch dialect-specific issues.⁸

Mechanism:
1. The test session starts. A pytest fixture initializes a PostgresContainer running the exact version (postgres:16) used in production.⁹
2. Port Mapping: The container maps the internal port 5432 to a random ephemeral port on the host, eliminating port conflicts in CI environments.¹⁰
3. Migration Application: The fixture runs Alembic migrations against this fresh container, verifying that the schema migration scripts are valid.
4. Test Execution: Tests run against this live database.
5. Teardown: The container is destroyed automatically.

2.2.2 Optimizing for Speed: The Singleton Pattern

Spinning up a Docker container takes time (2-5 seconds). Doing this for every single test function is prohibitively slow for large suites.

Staff Insight: We implement the Singleton Container Pattern.
- The container is started once per pytest session (scope=session).
- For each individual test, we use SQLAlchemy Savepoints (nested transactions).
- The test runs, inserts data, and asserts results.
- The fixture automatically issues a ROLLBACK at the end of the test function.
- Result: Thousands of database-backed tests can run in minutes with near-native speed, while maintaining total isolation.⁸

2.3 Testing FastAPI Endpoints

FastAPI’s design facilitates testing via dependency_overrides.

Strategy: We replace the authentication dependency with a dummy user for standard functional tests, but we keep the database dependency pointing to the Testcontainer.
Coverage: This verifies the full HTTP stack: serialization (Pydantic), routing (FastAPI), business logic (Domain), and persistence (PostgreSQL).¹¹

3. Event-Driven Architecture Testing: Kafka

Migrating to microservices with Kafka introduces the complexities of asynchronous communication and eventual consistency. The primary risk is Schema Drift: the Producer changes the message format, and the Consumer crashes.

3.1 Infrastructure Testing with Testcontainers for Kafka

Just as with PostgreSQL, we use testcontainers-python to spin up a Kafka (or Redpanda) container for integration testing.³

Producer Verification: We test that the OrderService correctly serializes the OrderCreated domain event into the expected JSON/Avro format and successfully pushes it to the topic.
Consumer Verification: We verify that the InventoryService listener can consume a message from the containerized topic and correctly trigger the side-effect (e.g., deducting stock in the database).
Why Redpanda? For testing, we often substitute Kafka with Redpanda (a C++ compatible alternative) because it starts up significantly faster (milliseconds vs. seconds), reducing CI loop times.¹²

3.2 Consumer-Driven Contract Testing (CDCT) with Pact

While Testcontainers prove that we can talk to Kafka, Pact proves that others can understand us. This is the “glue” that holds independent microservices together without requiring monolithic deployments.¹³

3.2.1 The Philosophy of Contracts

In a large organization, it is impossible to know every downstream consumer of a Kafka topic. If the OrderService team renames user_id to customer_id, they might break a critical reporting service they didn’t know existed.

Pact Solution: Consumers define a “Contract” (a pact file) stating: “I listen to OrderCreated and I expect a field user_id of type UUID.”
Verification: The OrderService (Provider) pipeline downloads all these contracts from the Pact Broker. It then replays the contracts against its own message generation logic. If the field is missing, the OrderService pipeline fails, preventing the breaking change from reaching production.¹⁴

3.2.2 Schema Registry vs. Pact

A common 2025 architectural question is the overlap between Confluent Schema Registry (Avro/Protobuf) and Pact.

The Distinction:
- Schema Registry ensures structural validity (e.g., “This is a valid Avro binary”). It protects against serialization errors.¹⁵
- Pact ensures semantic intent (e.g., “The consumer requires this specific field to perform its logic”).
Recommendation: Use Schema Registry for forward/backward compatibility checks. Use Pact for validating specific consumer-provider interactions and preventing semantic breakage.¹⁶

4. AI-Native Assurance: LangGraph & Ragas

The introduction of LangGraph for building stateful AI agents requires a bifurcated testing strategy: Structural Testing for the graph logic and Semantic Evaluation for the AI’s cognitive quality.

4.1 Structural Testing: Mocking the Stochasticity

We cannot rely on real LLM calls for functional testing due to cost, latency, and non-determinism.

Mocking the LLM: We utilize GenericFakeChatModel from LangChain. This allows us to inject a deterministic queue of “fake” AI responses (e.g., Response 1: “Call Search Tool”, Response 2: “Final Answer”).⁴
Graph Topology Verification: We verify the state transitions.
- Input: A mock user query.
- Mocked AI: Signals a tool call.
- Assertion: The graph transitions to the ToolNode, executes the tool logic, and updates the state dictionary with the tool output.¹⁷
State Persistence: Using InMemorySaver checkpointers, we verify that the conversation history is correctly preserved across multi-turn interactions, simulating the user disconnecting and reconnecting.¹⁸

4.2 Semantic Evaluation with Ragas

Once the structure is sound, we evaluate the quality of the intelligence. We employ Ragas (Retrieval Augmented Generation Assessment).⁵

4.2.1 The Metrics

Ragas uses an “LLM-as-a-Judge” (typically GPT-4) to grade the application’s traces against distinct metrics:

Faithfulness: Does the answer hallucinate? The judge checks if every claim in the answer can be inferred from the retrieved context.¹⁹
Answer Relevancy: Is the answer actually helpful? The judge generates a synthetic question for the answer and checks similarity with the original query.
Context Precision: Did the retrieval system find the right documents?

4.2.2 The Evaluation Pipeline

Implementing this in GitLab CI requires careful orchestration to manage costs.

The Golden Dataset: We maintain a curated dataset of inputs and high-quality “Ground Truth” answers (Json/CSV).
Execution: A Python script (evaluate_agent.py) runs the LangGraph agent against this dataset.
Scoring: It calculates the Ragas scores.
Gating: The CI job fails if the faithfulness score drops below a defined threshold (e.g., 0.85). This acts as a “Quality Gate” preventing dumber models from reaching production.²⁰

4.3 Deep Tracing with LangSmith

Evaluation provides a score; Tracing provides the diagnosis.

Integration: We configure the application to export traces to LangSmith by setting LANGCHAIN_TRACING_V2=true in the CI environment.²¹
Usage: If a Ragas test fails, the Staff Engineer does not just see “Score: 0.6”. They see a direct link to the LangSmith trace, visualizing the exact prompts, the retrieved documents, and the LLM’s raw output. This drastically reduces Mean Time to Resolution (MTTR) for AI regressions.²²

5. Frontend Testing: React, Micro-frontends & Playwright

The migration to Micro-frontends (MFE) using Webpack Module Federation introduces a new dependency hell: the application is no longer a single bundle but a runtime composition of disparate services.

5.1 Playwright: The Industry Standard

By 2025, Playwright has firmly superseded Cypress and Selenium for enterprise-grade testing.²³ Its architecture—communicating directly with the browser engine protocol rather than via a WebDriver bridge—enables handling the complex asynchronous nature of micro-frontends and AI streaming.

5.2 Testing Strategy for Micro-frontends

The core challenge in MFE testing is isolation. If the “Checkout” MFE is deployed by Team A, and the “Shell” App is deployed by Team B, how does Team B test the Shell without depending on Team A’s potentially unstable staging environment?

5.2.1 Network Interception and Mocking

We utilize Playwright’s powerful network interception capabilities to mock the infrastructure of Module Federation.

The Technique: When the Shell App requests the remoteEntry.js (the manifest file for the remote MFE), Playwright intercepts this request.TypeScript// Playwright Interception Example await page.route('**/remoteEntry.js', route => { route.fulfill({ status: 200, contentType: 'application/javascript', body: '...mocked module definition...' }); });
The Benefit: This allows us to test the Shell’s ability to mount a remote, handle loading states, and manage error boundaries even if the remote server is down. We are testing the integration contract, not the remote implementation.²⁴

5.2.2 Component Testing (`playwright-ct`)

For the individual micro-frontends (which are essentially React components), we use playwright-ct. This runs the component in a real browser (unlike Jest/JSDOM which simulates a browser). This is critical for ensuring CSS isolation and browser-specific event handling works correctly.²⁵

5.3 Testing AI Streaming (SSE)

FastAPI and LangGraph typically stream tokens to the UI using Server-Sent Events (SSE). Testing this “typewriter effect” is tricky for traditional tools.

WebSocket/SSE Mocking: Playwright can mock the WebSocket connection, sending a controlled sequence of token frames to the UI.
Assertion Logic: We do not assert on the final text immediately. We verify the state changes.
- Step 1: Assert the UI shows a “Thinking…” indicator.
- Step 2: Assert the “Stop Generating” button appears.
- Step 3: Assert the final text is present.
- This validates the UX of streaming, not just the data.²⁶

6. The CI/CD Pipeline: GitLab CI Optimization & Observability

GitLab CI acts as the factory floor. Optimizing this pipeline is essential to maintain developer velocity despite the heavy weight of Docker containers and AI evaluations.

6.1 Optimizing Docker-in-Docker (DinD)

Using Testcontainers requires a Docker daemon. In GitLab CI, this is typically provided by the docker:dind service. However, this is notoriously slow because it starts with a cold cache every time.

6.1.1 The Caching Strategy

To speed up builds, we must leverage Docker Layer Caching external to the ephemeral runner.

Registry Cache: We configure docker buildx to store the build cache in the GitLab Container Registry.Bashdocker buildx build \ --cache-from type=registry,ref=$CI_REGISTRY_IMAGE/cache:main \ --cache-to type=registry,ref=$CI_REGISTRY_IMAGE/cache:main,mode=max \ ... This allows a runner to pull cached layers even if it’s a completely new machine.²⁷

6.1.2 Overlay2 Storage Driver

We explicitly configure the runner to use the overlay2 storage driver instead of the default vfs. vfs creates a deep copy of the filesystem for every layer, which is excruciatingly slow. overlay2 uses efficient copy-on-write mechanisms.²⁸

Config: DOCKER_DRIVER: overlay2 variable in .gitlab-ci.yml.

6.1.3 Networking for Testcontainers

A common “gotcha” in GitLab CI is networking. Testcontainers running inside the job container need to talk to the Docker daemon (DinD) running in a service container.

Fix: We must set TESTCONTAINERS_HOST_OVERRIDE to docker (the hostname of the service) and ensure DOCKER_HOST is set to tcp://docker:2375. Without this, Testcontainers tries to connect to localhost and fails.²⁸

6.2 Observability-Driven Quality Gates

In 2025, passing tests is not enough. The pipeline must pass Observability Gates.

6.2.1 The Logic

We deploy the code to a temporary “Review App” environment (a Kubernetes namespace spun up for the Merge Request). We then run a load test using k6.

6.2.2 The Prometheus Gate

While the load test runs, the pipeline executes a “Gate Job” that queries the Prometheus server monitoring the Review App.

Query: sum(rate(http_requests_total{status=~"5.."}[1m])) > 0
Action: If this query returns true (meaning 500 errors are occurring), or if the p95 latency exceeds 500ms, the pipeline fails automatically.³⁰
Tooling: We use simple curl and jq scripts in the CI job to hit the Prometheus API and parse the result, ensuring a lightweight yet powerful gate.³¹

7. Recommended Workflows for Production

We present three distinct workflows tailored to different organizational maturities and risk appetites.

Table 1: Comparative Analysis of Testing Workflows

Feature	Approach A: “Shift-Left Velocity”	Approach B: “High-Fidelity GitOps”	Approach C: “AI-Native Adaptive”
Primary Goal	Developer Speed (Time-to-Merge)	System Reliability (Zero Regressions)	Semantic Quality (AI Performance)
Backend DB	Mocks / In-Memory Fakes	Testcontainers (Real Postgres)	Testcontainers + Prod Snapshots
Kafka Testing	Mocks	Pact + Testcontainers	Schema Registry + Mirroring
AI Eval	Structural Only (Fake LLM)	Ragas Smoke Tests (CI)	Continuous LangSmith Eval
Frontend	Component Tests (Mocked API)	E2E Playwright (Review App)	Production Traffic Replay
CI Duration	~5 Minutes	~20 Minutes	~45 Minutes + Async Eval
Cost	$	$$$	$$$$
Production Risk	Medium (Integration bugs)	Low (Caught in CI)	Very Low (Caught in Shadow)
Rating	⭐⭐⭐	⭐⭐⭐⭐⭐ (Recommended)	⭐⭐⭐⭐

7.1 Approach B: The “High-Fidelity GitOps Workflow” (Recommended)

This workflow represents the best balance for a Staff Engineer designing a robust system in 2025. It prioritizes correctness over raw speed, acknowledging that the cost of a microservices incident far outweighs the cost of CI compute.

The Pipeline Flow:

Commit: Developer pushes code.
Build Stage: Docker images built with Registry Cache.
Static Analysis: Ruff (Linting), MyPy (Types), Trivy (Security).
Unit & Contract Stage:
- pytest runs Domain tests (ms).
- pact-verifier runs Kafka contracts against the code (no broker needed).
Integration Stage:
- pytest runs Infrastructure tests using Testcontainers (Postgres/Redpanda).
- AI Gate: evaluate_agent.py runs a “Smoke Suite” (10 critical questions) using Ragas + OpenAI.
Review App Deployment: Ephemeral Kubernetes namespace deployed.
E2E & Observability Stage:
- Playwright runs critical user journeys against the Review App.
- k6 generates synthetic load.
- Prometheus Gate: Pipeline queries metrics; fails if latency spikes.
Merge: Code merges to Main.

7.2 Approach C: The “AI-Native Adaptive Workflow”

For teams where the AI agent’s quality is the product (e.g., a customer support bot), we augment Approach B with Shadow Testing.

Shadow Mode: Upon deployment, the new agent version receives a copy of live traffic but its responses are suppressed.
Async Eval: These shadow responses are logged to LangSmith and asynchronously graded by Ragas.
Promotion: Only if the shadow agent outperforms the live agent on “Helpfulness” metrics for 24 hours is it promoted to the primary handler.

8. Implementation Guide: Critical Configuration Snippets

8.1 GitLab CI for Docker-in-Docker with Caching

This configuration enables the high-performance setup discussed in Section 6.

YAML

#.gitlab-ci.yml
variables:
  # Tell Testcontainers to use the DinD service
  DOCKER_HOST: "tcp://docker:2375"
  DOCKER_TLS_CERTDIR: "" 
  # Use efficient storage driver
  DOCKER_DRIVER: overlay2
  # Tell Testcontainers internal logic to use 'docker' hostname
  TESTCONTAINERS_HOST_OVERRIDE: "docker"

services:
  - name: docker:27.4.1-dind
    command: ["--tls=false"]

stages:
  - build
  - test

build_image:
  stage: build
  image: docker:27.4.1
  script:
    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
    - docker buildx create --use
    # Build with Registry Cache
    - docker buildx build 
      --cache-from type=registry,ref=$CI_REGISTRY_IMAGE/build-cache:main 
      --cache-to type=registry,ref=$CI_REGISTRY_IMAGE/build-cache:main,mode=max 
      -t $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA.

integration_tests:
  stage: test
  image: python:3.11
  services:
    - name: docker:dind
      command: ["--tls=false"]
  script:
    - pip install poetry
    - poetry install
    - poetry run pytest tests/integration

8.2 The Ragas Evaluation Script

This script serves as the “AI Quality Gate” in the pipeline.

Python

# scripts/evaluate_ai.py
import os
import sys
from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy
from datasets import Dataset
from app.agent import graph  # Import the LangGraph agent

def run_smoke_test():
    # 1. Define Smoke Dataset (Small, Critical)
    questions =
    ground_truths =,]
    
    # 2. Run Inference
    answers =
    contexts =
    for q in questions:
        # Invoke LangGraph
        result = graph.invoke({"messages": [("user", q)]})
        answers.append(result["messages"][-1].content)
        # Extract retrieved docs from state
        contexts.append([d.page_content for d in result["context"]])

    # 3. Create Dataset
    ds = Dataset.from_dict({
        "question": questions,
        "answer": answers,
        "contexts": contexts,
        "ground_truth": ground_truths
    })

    # 4. Evaluate with Ragas
    results = evaluate(
        ds,
        metrics=[faithfulness, answer_relevancy]
    )

    # 5. Gate Logic
    print(f"Evaluation Results: {results}")
    if results["faithfulness"] < 0.85:
        print("FAIL: Faithfulness below 0.85 threshold")
        sys.exit(1)

if __name__ == "__main__":
    run_smoke_test()

9. Conclusion

The testing framework for 2025 is a layered defense system. It acknowledges that in a microservices architecture driven by AI, “correctness” is a moving target.

Testcontainers provide the physical ground truth (Infrastructure Fidelity).
Pact provides the diplomatic ground truth (Service Contracts).
Ragas provides the cognitive ground truth (AI Quality).
Prometheus provides the operational ground truth (System Health).

By adopting the High-Fidelity GitOps Workflow, the Staff Engineer ensures that the system is resilient not just to code errors, but to the inherent chaos of distributed, probabilistic systems. This is not merely a testing strategy; it is a risk management strategy for the modern enterprise.

Testing Strategy Report 2025: FastAPI/React/Microservices

Executive Summary: The “Modern Enterprise” Stack

As we migrate towards a Domain-Driven Design (DDD) and Microservices architecture in 2025, our testing strategy must evolve from simple unit tests to a comprehensive Contract-First & Container-Native approach. This report evaluates testing frameworks for our specific stack: FastAPI, React, PostgreSQL, LangGraph, and Kafka.

Recommended Backend

Pytest + Asyncio

Native Async Support

Recommended Frontend

Vitest + RTL

3x Faster than Jest

E2E Framework

Playwright

Full Traceability

Architecture Fit

98% Suitability

Based on Microservices/DDD

Target Testing Pyramid (2025 Standard)

Shift-left approach emphasizing heavy unit/contract testing to reduce CI costs and improve feedback loops.

Strategic Goals for Production

Containerized Isolation

Abandon SQLite mocks. Use Testcontainers for ephemeral Postgres & Kafka instances in integration tests to match prod parity.

Contract Testing for DDD

As we split into microservices, use Pact to ensure consumer-provider compatibility without spinning up the world.

Deterministic AI Testing

For LangGraph, implement strict evaluation pipelines comparing outputs against a “Golden Dataset” using semantic similarity.

Strategic Approaches Comparison

Three potential paths were evaluated for the 2025 roadmap. The “Modern Cloud-Native” approach is recommended for its balance of speed and robustness.

1. Traditional/Legacy

Score: 65/100

Standard stack from 2020-2023. Reliable but slow and lacks async native depth.

Pytest + Mocking
SQLite (In-mem)
Jest + Enzyme
Selenium

Bottleneck: Mocks drift from real DB behavior.

RECOMMENDED

2. Modern Cloud-Native

Score: 92/100

Optimized for speed, async, and container parity. Best for current stack.

Pytest-Asyncio + Polyfactory
Testcontainers (PG/Kafka)
Vitest + Testing Lib
Playwright

Advantage: High fidelity integration tests.

3. Full Microservice Scale

Score: 88/100

Heavy tooling for teams > 50 engineers. High maintenance but robust.

Pact (Contract Testing)
K6 (Performance)
SonarQube strict gates
Chaos Mesh

Caution: High configuration overhead.

Suitability Analysis

FastAPI & Async Testing

Since FastAPI is natively async, utilizing pytest-asyncio is non-negotiable. We must move away from synchronous DB drivers in tests to match production asyncpg usage.

The “Testcontainers” Pattern

Instead of mocking the database session, spin up a real Postgres container for the test suite duration. This catches SQL syntax errors specific to Postgres that SQLite mocks miss.

Data Factory (Polyfactory)

Use polyfactory (formerly pydantic-factories) to generate typed fixtures derived directly from your Pydantic models. This ensures test data is always schema-compliant.

LangGraph & AI Testing

Testing nondeterministic AI agents requires a specialized pipeline. LangGraph nodes are stateful; we must test state transitions.

Component	Strategy	Tool
LLM Calls	Record & Replay (VCR)	`pytest-recording`
Graph State	Unit Test Node Logic	`langchain-core` mocks
Output Quality	Semantic Similarity	`DeepEval` / `Ragas`

Kafka Integration

Event-driven architectures are notoriously hard to test. The approach:

Produce test event to Topic A.
Wait for Consumer to process.
Assert side-effect (DB change or Topic B event).
Tool: testcontainers-python (Kafka module).

Prometheus

Don’t just trust metrics appear. Test them.

def test_metrics_increment():
client.get(“/endpoint”)
assert REGISTRY.get_sample_value(‘http_requests_total’) == 1

React Testing Strategy

Moving from Jest to Vitest for speed and native ESM support.

Vitest React Testing Library Playwright

Component & Unit (Vitest)

Jest is significantly slower in CI pipelines. Vitest shares configuration with Vite, reducing boilerplate.

Behavior-Driven: Test user interactions (clicks, typing), not implementation details (state, props).
MSW (Mock Service Worker): Intercept network requests at the network layer. Do not mock `axios`/`fetch` directly. This allows switching to integration tests easily.

End-to-End (Playwright)

Playwright is preferred over Cypress for its better parallelization, Safari (WebKit) support, and “trace viewer” for debugging CI failures.

Key Configuration

Parallel Workers 4 (CI)

Retries 2 (Flakiness guard)

Artifacts Video + Trace (on fail)

GitLab CI/CD Pipeline Architecture

The pipeline is designed to “fail fast”. Expensive integration tests only run if unit tests pass. Artifacts (Docker images) are built once and promoted.

Stage 1: Verify

Lint & Static Analysis

Ruff, Black, ESLint

Unit Tests

Pytest (Mocked), Vitest

parallel: 4

Stage 2: Build

Build Containers

Docker Build

Stage 3: Integration

Contract Tests

Pact Broker Verify

Service Tests

Pytest + Testcontainers (PG/Kafka)

Stage 4: Acceptance

E2E Tests

Playwright (Headless)

Against Ephemeral Env

# .gitlab-ci.yml Snippet (Service Tests)

integration_tests: stage: integration services: – name: docker:dind script: – pip install poetry – poetry install – poetry run pytest tests/integration –cov=app –cov-report=xml variables: # Critical for Testcontainers in GitLab DOCKER_HOST: tcp://docker:2375 DOCKER_TLS_CERTDIR: “”