¿Worrying about flaky API tests, missing fixtures, or leaking production data into a cloud LLM? This guide focuses on how to get the best free AI mocks for VS Code in production-like quality, with step-by-step setup, prompt templates, local vs cloud privacy trade-offs, CI practices, and a practical comparison of the most useful free options.
Key takeaways: what to know in 1 minute
- Top free options: use a mix of open-source mock servers (Mockoon, MSW) plus local LLMs (LocalAI / GPT4All) to produce realistic fixtures inside VS Code.
- Fast setup: install a VS Code extension (Thunder Client or REST Client) and run a local AI mock server (LocalAI + simple prompt pipeline) to generate deterministic responses.
- Privacy first: prefer local LLMs for sensitive payloads; cloud LLMs are convenient but may expose data. See NIST and OWASP guidance for data handling.
- CI strategy: store generated mocks as checked-in fixtures, pin prompt versions, and run smoke tests using MSW or a lightweight mock server in CI.
- Prompt templates: use structured prompts to generate schemas, example responses, and edge cases. Lock outputs by using seeds or schema-based generation.
Top free AI VS Code extensions for mocking
The following extensions and tools integrate cleanly with VS Code and enable AI-assisted mock generation, editing, or local serving. Links point to official pages.
- Thunder Client, lightweight REST client inside VS Code, ideal for manual API mocking and testing. Thunder Client on Marketplace
- REST Client, send HTTP requests from VS Code files; useful to call a local AI mock server. REST Client
- Mockoon (CLI + desktop), fast mock server with import/export; pairs well with VS Code workflows. Mockoon
- MSW (Mock Service Worker), programmatic, integrates with node tests, supports recording; excellent for frontend and CI. MSW
- LocalAI (server to run local OSS LLMs), run open models locally and call them to generate mock payloads. LocalAI GitHub
- Keploy, test-case recorder and mock generator, open source for integration testing. Keploy
- VS Code LocalAI / Local LLM extensions, bridge VS Code to a local AI instance for quick prompts (use with LocalAI or GPT4All). llama.cpp
Each tool covers part of the flow: Thunder Client / REST Client for manual requests, Mockoon / MSW for serving mocks, LocalAI or GPT4All for generating content using an LLM.
Choosing the right combination
- For frontend devs: MSW + LocalAI for realistic JSON fixtures that run in-browser or in node tests.
- For backend devs: Keploy or Mockoon + LocalAI to record, generate, and replay API behaviors.
- For quick manual QA: Thunder Client + simple prompt-to-fixture pipeline.
How to generate realistic API mocks with AI
Generating realistic API mocks requires structure, examples, and edge-case coverage. A reliable pipeline converts a short prompt into a JSON schema, then into multiple example payloads.
Step: prompt to schema to examples
- Ask the LLM for a JSON schema given endpoint description and sample inputs.
- Validate schema (ajv or similar) and then generate N examples covering typical, boundary, and error cases.
- Persist examples as fixtures (JSON files) and serve with a mock server.
Prompt template (deterministic, use with LocalAI or cloud LLM)
Use a structured prompt. Example (replace placeholders):
You are an API mock generator. Given the endpoint, return a JSON object with two fields: "schema" (JSON Schema draft 7) and "examples" (array of 6 example responses). Endpoint: POST /orders. Description: create order with items array, total, currency, customerId, and possible 'out_of_stock' error. Return only valid JSON.
When calling a cloud LLM, append: "Do not include explanations. Respond only with JSON. Use deterministic sampling: temperature=0.0." For LocalAI/GPT4All, use the equivalent parameter settings.
Example minimal response (sanitized)
{
"schema": {
"$schema": "http://json-schema.org/draft-07/schema#",
"type": "object",
"properties": {
"orderId": {"type": "string"},
"status": {"type": "string"},
"total": {"type": "number"}
},
"required": ["orderId","status","total"]
},
"examples": [
{"orderId":"o_1001","status":"confirmed","total":45.5},
{"orderId":"o_1002","status":"out_of_stock","total":0}
]
}
(When embedding into markdown, ensure JSON is validated.)
- Use ajv (node) to validate schemas and generated examples. ajv
- Use Faker libraries to synthesize fields from schema types.
- For edge cases, instruct the model to produce invalid payloads intentionally (status codes 400/500) for negative testing.
Setting up free AI mock servers inside VS Code
A reproducible local stack that fits in VS Code: LocalAI (runs model) + a tiny express/mock server + REST Client/Thunder Client. The HowTo below details a step-by-step pipeline.
How To: run a LocalAI-based mock server in VS Code
- Install LocalAI (or GPT4All) and download a small model (MPT-7B-Instruct or a GPT4All-compatible model). See LocalAI docs for model placement. LocalAI
- Create a minimal node server in the project to forward endpoint descriptions to LocalAI and return generated fixtures.
- Install Thunder Client or REST Client to call the local mock endpoints from within VS Code.
- Save generated fixtures to a fixtures/ folder and add tests that import those fixtures.
Minimal server snippet (conceptual)
// server.js (conceptual)
const express = require('express')
const fetch = require('node-fetch')
const app = express()
app.use(express.json())
app.post('/generate-mock', async (req, res) => {
const prompt = `Generate schema+examples for: ${req.body.description}`
const aiResp = await fetch('http://localhost:8080/v1/completions', {
method: 'POST',
headers: {'Content-Type':'application/json'},
body: JSON.stringify({model:'local-model', prompt, temperature:0})
})
const data = await aiResp.json()
res.json(JSON.parse(data.text))
})
app.listen(3000)
(Adjust to the chosen LocalAI API surface.)
Privacy and local vs cloud AI mocking considerations
- Local LLMs: best for sensitive data. No network egress; models run on local CPU/GPU. Use LocalAI or GPT4All. Advantages: data stays on device; deterministic if seedable. Trade-offs: resource use and model capability limits.
- Cloud LLMs: more capable but risk data exposure. Avoid sending real PII or production payloads. When cloud LLMs are used, redact or anonymize payloads before sending. See NIST guidance: NIST AI resources and OWASP API security notes: OWASP.
Practical checklist
- ✅ Prefer local LLMs for regulated environments.
- ✅ Use synthetic or sanitized data for cloud prompts.
- ✅ Pin model and prompt versions; record outputs as fixtures.
- ⚠ Avoid sending raw production payloads to cloud LLMs without contractual safeguards.
Best practices: using AI-generated test mocks in CI
- Check fixtures into the repo as canonical sources of truth. Do not regenerate on every CI run unless intentionally testing model drift.
- Pin prompt templates and model versions. Store them adjacent to tests (prompts/ folder) and add a changelog.
- Validate every AI-generated fixture with schema validators (ajv) during CI; fail the build on mismatches.
- Use snapshot tests for generated payloads, but prefer explicit schema checks to reduce brittle tests.
- Run a small deterministic generation pass in CI (temperature=0) to reproduce examples when needed.
Below is a concise comparison of the most relevant free tools and combinations for AI-assisted mocking inside VS Code.
| Tool / combo |
Primary purpose |
Free tier / license |
Best fit |
| LocalAI + MSW |
Local LLM generation + in-test mocking |
Open source (LocalAI: MIT) |
Privacy-sensitive CI & frontend tests |
| Mockoon + Thunder Client |
Standalone mock server + manual testing |
Free desktop + open source |
Rapid prototyping and manual QA |
| Keploy |
Record/replay and mock generation |
Open source |
Integration testing for backend services |
| Cloud LLM + REST Client |
High-fidelity mock generation |
Free tiers available (limits) |
When realism outweighs privacy concerns |
Visual reference: local AI mock flow
Local AI mock flow
🧠
Describe endpoint
Write endpoint & sample input
⚡
Generate schema
LocalAI returns schema+examples
🗄️
Persist fixtures
Save to fixtures/ and version
🚀
Serve & test
MSW/Mockoon in dev and CI
Advantages, risks and common mistakes
Benefits / when to apply
- ✅ Quick realistic fixtures for frontend/back-end independent development.
- ✅ Lower test flakiness by using schema-validated, AI-generated edge cases.
- ✅ Faster prototyping of API contracts.
Mistakes to avoid / risks
- ⚠️ Relying on non-deterministic outputs without versioning prompts.
- ⚠️ Sending production PII to cloud LLMs without anonymization.
- ⚠️ Using AI-generated mocks as the only source of truth; always validate against real contracts.
Frequently asked questions
What are the best free VS Code extensions for generating mocks?
The core combination is LocalAI or GPT4All for generation, plus Thunder Client or REST Client for calling local endpoints; pair with Mockoon or MSW for serving fixtures.
Can AI-generated mocks replace contract tests?
No. AI mocks accelerate development and expand coverage, but contract tests against staging or contract tools are still required for production guarantees.
Is it safe to use cloud LLMs with production data?
Not recommended. Redact or anonymize production data and prefer local LLMs for sensitive information. See NIST guidance for AI risk management.
How to keep AI mock generation deterministic in CI?
Pin model and prompt versions, use temperature=0, and store generated fixtures in the repo as canonical files.
Which mock server is best for frontend development?
MSW is the preferred choice because it intercepts network requests and integrates easily with browser/node tests.
How many examples should an AI-generated mock include?
Aim for 5–10 examples: 1 canonical, 2 boundary values, 2 error cases, and 1–2 random realistic variants.
Conclusion
A practical, privacy-aware approach combines free open-source LLM runners (LocalAI / GPT4All) with proven mock servers (MSW, Mockoon) and VS Code REST tools (Thunder Client / REST Client). Generate structured schemas, validate them, persist fixtures, and add CI validation to keep tests reliable.
Your next step:
- Install Thunder Client or REST Client in VS Code and try calling an existing mock server (Mockoon or MSW).
- Spin up LocalAI or GPT4All and run a simple prompt that returns a JSON schema and examples; save fixtures to fixtures/.
- Add schema validation (ajv) in CI and pin prompt/model versions to ensure reproducibility.
