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library for making AI API calls with streaming support","contributors":[{"name":"J Chris Anderson"},{"name":"Meno Abels"}],"maintainers":[{"name":"jchris","email":"jchris@gmail.com"},{"name":"fastandfearless","email":"meno.abels@gmail.com"}],"readme":"# call-ai\n\nA lightweight library for making AI API calls with streaming support.\n\n## Installation\n\n```bash\nnpm install call-ai\n# or\nyarn add call-ai\n# or\npnpm add call-ai\n```\n\n## Usage\n\n```typescript\nimport { callAi } from \"call-ai\";\n\n// Basic usage with string prompt (non-streaming by default)\nconst response = await callAi(\"Explain quantum computing in simple terms\", {\n  apiKey: \"your-api-key\",\n  model: \"gpt-4\",\n});\n\n// The response is the complete text\nconsole.log(response);\n\n// With streaming enabled (returns an AsyncGenerator)\nconst generator = callAi(\"Tell me a story\", {\n  apiKey: \"your-api-key\",\n  model: \"gpt-4\",\n  stream: true,\n});\n\n// Process streaming updates\nfor await (const chunk of generator) {\n  console.log(chunk); // Streaming updates as they arrive\n}\n\n// Using message array for more control\nconst messages = [\n  { role: \"system\", content: \"You are a helpful assistant.\" },\n  { role: \"user\", content: \"Explain quantum computing in simple terms\" },\n];\n\nconst response = await callAi(messages, {\n  apiKey: \"your-api-key\",\n  model: \"gpt-4\",\n});\n\nconsole.log(response);\n\n// Using schema for structured output\nconst schema = {\n  name: \"exercise_summary\",\n  properties: {\n    title: { type: \"string\" },\n    summary: { type: \"string\" },\n    points: { type: \"array\", items: { type: \"string\" } },\n  },\n  required: [\"title\", \"summary\"],\n};\n\nconst response = await callAi(\"Summarize the benefits of exercise\", {\n  apiKey: \"your-api-key\",\n  schema: schema,\n});\n\nconst structuredOutput = JSON.parse(response);\nconsole.log(structuredOutput.title);\n\n// Streaming with schema for OpenRouter structured JSON output\nconst schema = {\n  properties: {\n    title: { type: \"string\" },\n    items: {\n      type: \"array\",\n      items: {\n        type: \"object\",\n        properties: {\n          name: { type: \"string\" },\n          description: { type: \"string\" },\n        },\n      },\n    },\n  },\n};\n\nconst generator = callAi(\"Create a list of sci-fi books\", {\n  apiKey: \"your-api-key\",\n  stream: true,\n  schema: schema,\n});\n\nfor await (const chunk of generator) {\n  console.log(chunk); // Shows the partial JSON as it's being generated\n}\n```\n\n## Features\n\n- 🔄 Streaming responses via AsyncGenerator when `stream: true`\n- 🧩 Structured JSON outputs with schema validation\n- 🔌 Full compatibility with OpenRouter's JSON schema format for structured outputs\n- 📝 Support for message arrays with system, user, and assistant roles\n- 🔧 TypeScript support with full type definitions\n- ✅ Works in Node.js and browser environments\n\n## Supported LLM Providers\n\nCall-AI supports all models available through OpenRouter, including:\n\n- OpenAI models (GPT-4, GPT-3.5, etc.)\n- Anthropic Claude\n- Gemini\n- Llama 3\n- Mistral\n- And many more\n\n## Choosing a model\n\nDifferent LLMs have different strengths when working with structured data. Based on our testing, here's a guide to help you choose the right model for your schema needs:\n\n### Schema Complexity Guide\n\n| Model Family | Grade | Simple Flat Schema | Complex Flat Schema              | Nested Schema | Best For                                                  |\n| ------------ | ----- | ------------------ | -------------------------------- | ------------- | --------------------------------------------------------- |\n| OpenAI       | A     | ✅ Excellent       | ✅ Excellent                     | ✅ Excellent  | Most reliable for all schema types                        |\n| Gemini       | A     | ✅ Excellent       | ✅ Excellent                     | ✅ Good       | Good all-around performance, especially with flat schemas |\n| Claude       | B     | ✅ Excellent       | ⚠️ Good (occasional JSON errors) | ✅ Good       | Simple schemas, robust handling of complex prompts        |\n| Llama 3      | C     | ✅ Good            | ✅ Good                          | ❌ Poor       | Simpler flat schemas, may struggle with nested structures |\n| Deepseek     | C     | ✅ Good            | ✅ Good                          | ❌ Poor       | Basic flat schemas only                                   |\n\n### Schema Structure Recommendations\n\n1. **Flat schemas perform better across all models**. If you need maximum compatibility, avoid deeply nested structures.\n\n2. **Field names matter**. Some models have preferences for certain property naming patterns:\n   - Use simple, common naming patterns like `name`, `type`, `items`, `price`\n   - Avoid deeply nested object hierarchies (more than 2 levels deep)\n   - Keep array items simple (strings or flat objects)\n\n3. **Model-specific considerations**:\n   - **OpenAI models**: Best overall schema adherence and handle complex nesting well\n   - **Claude models**: Great for simple schemas, occasional JSON formatting issues with complex structures\n   - **Gemini models**: Good general performance, handles array properties well\n   - **Llama/Mistral/Deepseek**: Strong with flat schemas, but often ignore nesting structure and provide their own organization\n\n4. **For mission-critical applications** requiring schema adherence, use OpenAI models or implement fallback mechanisms.\n\n## Setting API Keys\n\nYou can provide your API key in three ways:\n\n1. Directly in the options:\n\n```typescript\nconst response = await callAi(\"Hello\", { apiKey: \"your-api-key\" });\n```\n\n2. Set globally in the browser:\n\n```typescript\nwindow.CALLAI_API_KEY = \"your-api-key\";\nconst response = await callAi(\"Hello\");\n```\n\n3. Use environment variables in Node.js (with a custom implementation):\n\n```typescript\n// Example of environment variable integration\nimport { callAi } from \"call-ai\";\nconst apiKey = process.env.OPENAI_API_KEY || process.env.OPENROUTER_API_KEY;\nconst response = await callAi(\"Hello\", { apiKey });\n```\n\n## API\n\n```typescript\n// Main function\nfunction callAi(prompt: string | Message[], options?: CallAIOptions): Promise<string> | AsyncGenerator<string, string, unknown>;\n\n// Types\ntype Message = {\n  role: \"user\" | \"system\" | \"assistant\";\n  content: string;\n};\n\ninterface Schema {\n  /**\n   * Optional schema name that will be sent to the model provider if supported\n   */\n  name?: string;\n  properties: Record<string, any>;\n  required?: string[];\n  additionalProperties?: boolean;\n}\n\ninterface CallAIOptions {\n  apiKey?: string;\n  model?: string;\n  endpoint?: string;\n  stream?: boolean;\n  schema?: Schema | null;\n  [key: string]: any;\n}\n```\n\n### Options\n\n- `apiKey`: Your API key (can also be set via window.CALLAI_API_KEY)\n- `model`: Model identifier (default: 'openrouter/auto')\n- `endpoint`: API endpoint (default: 'https://openrouter.ai/api/v1/chat/completions')\n- `stream`: Enable streaming responses (default: false)\n- `schema`: Optional JSON schema for structured output\n- Any other options are passed directly to the API (temperature, max_tokens, etc.)\n\n## License\n\nApache-2.0\n\n## Contributing and Release Process\n\n### Development\n\n1. Fork the repository\n2. Make your changes\n3. Add tests for new functionality\n4. Run tests: `npm test`\n5. Run type checking: `npm run typecheck`\n6. Create a pull request\n\n### Integration Tests\n\nThe project includes integration tests that make real API calls to verify functionality with actual LLM models:\n\n1. Copy `.env.example` to `.env` and add your OpenRouter API key\n2. Run integration tests: `npm run test:integration`\n\nNote: Integration tests are excluded from the normal test suite to avoid making API calls during CI/CD. They require a valid API key to execute and will be skipped if no key is provided.\n\n### Release Process\n\nThis library uses GitHub Actions to automate the release process:\n\n1. Update the version in `package.json` (follow semver)\n2. Update `CHANGELOG.md` with details of changes\n3. Commit changes: `git commit -am \"Release vX.Y.Z\"`\n4. Create a git tag: `git tag -a vX.Y.Z -m \"Version X.Y.Z\"`\n5. Push changes and tag: `git push origin main vX.Y.Z`\n\nThe GitHub workflow in `.github/workflows/publish.yml` will:\n\n- Automatically trigger when a new tag is pushed\n- Run tests and type checking\n- Verify the tag signature\n- Publish the package to npm\n\nWhen making significant changes, remember to:\n\n- Document breaking changes in the changelog\n- Update documentation to reflect API changes\n- Update TypeScript types\n","readmeFilename":"README.md"}