llmockapi 0.1.1

An LLM-powered mock API server that dynamically generates realistic API responses based on OpenAPI/Swagger specifications.

llmockapi

An LLM-powered mock API server that dynamically generates realistic API responses based on OpenAPI/Swagger specifications.

Overview

llmockapi is a Python-based development tool that uses Large Language Models to automatically generate mock API responses according to your API specifications. Instead of manually creating mock data, simply provide an OpenAPI/Swagger spec and let the LLM handle the response generation intelligently.

Features

• LLM-Powered Responses: Uses AI models to generate contextually appropriate API responses
• OpenAPI/Swagger Support: Works with standard API specifications in JSON or YAML format
• Flexible Spec Loading: Load specifications from local files or HTTP URLs
• Conversation History: Maintains context across requests for consistent mock data
• Debug UI: Built-in web interface to view request/response history
• FastAPI-Based: Fast, modern Python web framework with async support
• Configurable: Control via environment variables or CLI arguments

Requirements

• Python >= 3.12
• An LLM API endpoint (compatible with OpenAI chat completions format)
• API key for your LLM provider

Installation

# Install using pip
pip install llmockapi

# Or install from source
git clone https://github.com/yourusername/llmockapi.git
cd llmockapi
pip install -e .

Configuration

Configure llmockapi using environment variables, a .env file, or CLI arguments:

Required Configuration

Parameter	Environment Variable	CLI Argument	Description
API Key	API_KEY	--api-key	Your LLM provider API key
Base URL	BASE_URL	--base-url	LLM API endpoint URL
API Spec	MOCK_API_SPEC	--mock-api-spec	Path or URL to OpenAPI/Swagger spec

Optional Configuration

Parameter	Environment Variable	CLI Argument	Default	Description
Model	MODEL	--model	anthropic/claude-haiku-4.5	LLM model to use
Host	HOST	--host	localhost	Server host
Port	PORT	--port	9000	Server port

Example .env file:

API_KEY=your-api-key-here
BASE_URL=https://api.yourlm-provider.com
MOCK_API_SPEC=./tests/mocks/api_specs.json
MODEL=anthropic/claude-haiku-4.5
HOST=localhost
PORT=9000

Usage

Starting the Server

# Using the CLI with environment variables
llmockapi

# Or with CLI arguments
llmockapi --api-key YOUR_KEY --base-url https://api.provider.com --mock-api-spec ./spec.json

# Or using Python module
python -m llmockapi

The server will start on http://localhost:9000 (or your configured host/port).

Making Requests

Once the server is running, make HTTP requests to any endpoint defined in your API specification:

# Example: Get a pet by ID
curl http://localhost:9000/pet/123

# Example: Create a new user
curl -X POST http://localhost:9000/user \
  -H "Content-Type: application/json" \
  -d '{"username": "johndoe", "email": "[e-mail]"}'

API Specifications

llmockapi supports OpenAPI/Swagger specifications in multiple formats:

Local Files

# JSON format
llmockapi --mock-api-spec ./path/to/spec.json

# YAML format
llmockapi --mock-api-spec ./path/to/spec.yaml

Remote URLs

# Load from HTTP/HTTPS
llmockapi --mock-api-spec https://example.com/api/swagger.json

Internal Endpoints

llmockapi provides internal endpoints for debugging and monitoring:

Endpoint	Method	Description
/__internal/health	GET	Health check endpoint
/__internal/messages	GET	View conversation history (JSON)
/__internal/ui	GET	Web UI to view request/response history

Example:

# Check server health
curl http://localhost:9000/__internal/health

# View conversation history
curl http://localhost:9000/__internal/messages

# Open web UI in browser
open http://localhost:9000/__internal/ui

How It Works

1. Initialization: The server loads your API specification and creates a system prompt for the LLM
2. Request Handling: When a request arrives, llmockapi intercepts it via middleware
3. LLM Processing: The request details (method, path, headers, body) are sent to the LLM with the API spec as context
4. Response Generation: The LLM generates a contextually appropriate response matching your API specification
5. History Tracking: All requests and responses are stored in conversation history for consistency

The LLM maintains context across requests, ensuring that related API calls return consistent data (e.g., a created resource can be retrieved later).

Example

Here's a quick example using the included Petstore API specification:

# Start the server with the example spec
llmockapi --api-key YOUR_KEY \
  --base-url https://api.provider.com \
  --mock-api-spec ./tests/mocks/api_specs.json

# Get pet by ID
curl http://localhost:9000/pet/1

# Create a new pet
curl -X POST http://localhost:9000/pet \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Fluffy",
    "photoUrls": ["https://example.com/photo.jpg"],
    "status": "available"
  }'

# View the conversation in the web UI
open http://localhost:9000/__internal/ui

Development

# Clone the repository
git clone https://github.com/yourusername/llmockapi.git
cd llmockapi

# Install dependencies
pip install -e .

# Set up environment variables
cp .env.example .env
# Edit .env with your configuration

# Run the server
llmockapi

License

See the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.